Partners – MODSIM2025

J8. Decision-Making Under Uncertainty in Water Resources Management

In water resources management, making optimal decisions under uncertainty remains a significant challenge. Uncertainty can stem from various sources, such as natural climate variability, climate change, data errors, model limitations, operational unpredictability and future policy change. These uncertainties can negatively affect the design and real-time operations of large water infrastructures (including reservoirs and groundwater systems) essential for water supply, flood control, environmental watering, and hydropower generation. For this session, we invite contributions from researchers and practitioners pertaining to any of the following: (1) improved methods in the field, including but not limited to those based on optimisation, simulation, scenario analysis and/or artificial intelligence, (2) application of probabilistic forecasts to inform planning or operational decisions, (3) advancements in scoping and communicating forecasts; (4) extending water forecasts to assess social and economic impacts, and (5) case studies on real world challenges of decision-making under uncertainty. We also welcome contributions on other relevant topics not mentioned above.

Keywords: Decision-making; Decision modelling; Uncertainty; Hydrometeorological forecasting; Water resources management

J9. Challenges, new concepts and new methods for water resources management

Intensifying competition for water from agricultural, industrial, and domestic users has sharply increased stress on aquatic and wetland ecosystems. In a growing number of catchments and aquifers, the volume of water allocated has reached or exceeded sustainable limits in many parts of the world. These issues are predicted to worsen in the future with climate change. This poses challenges to manage water resources effectively and efficiently in a changing world. New concepts and innovative approaches need to be developed to solve these challenges. This session welcomes papers on “Challenges, new concepts and new methods for water resources management”. The areas include (but not limited to) challenges and recent development of novel advances for water resources management to adapt in the changing world, problems and challenges in the current water management systems, new ideas to effectively manage water resources, development of statistical and machine learning based methods to assess and predict water resources (e.g. assessment the impact of anthropogenic climate change and hydro dam operations on low flows), developing integrated models for surface-groundwater movement and contaminant transport, innovative approaches to estimate future water demand and water availability.

Keywords: Water resources management; Machine learning technology; Water demand and supply; Anthropogenic climate change

J10. Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques

Traditional hydrological models have long relied on in-situ measurements, which, despite their value, often present challenges due to spatial and temporal limitations. Recent advancements in data acquisition technologies offer new opportunities to address these constraints and enhance hydrological predictions.

This session will explore innovative approaches that integrate diverse datasets and cutting-edge modelling techniques to improve predictive accuracy, uncertainty management, and real-world applications in water resource management. We particularly welcome contributions that introduce methodologies not traditionally applied in hydrology, fostering interdisciplinary advancements in the field.

Topics of interest include, but are not limited to:

Remote sensing applications – The use of remote sensing to monitor hydrological variables, particularly surface water dynamics, and their integration into hydrological modelling frameworks.
Innovative modelling – The application of novel hydrological modelling approaches, including deep learning, stochastic methods, and models that incorporate data-driven insights to enhance hydrological predictions.

This session aims to bring together researchers working on novel sensing technologies and innovative modelling approaches to advance hydrological predictions. By expanding the scope of predictive capabilities, it will contribute to sustainable water resource management and informed policy decision-making.

Keywords: Hydrological modelling; Remote sensing applications in hydrology; Innovatives in hydrology; Unsolved Problems in Hydrology; Predictions in ungauged basins; Deep learning in hydrology; Stochastic modelling in hydrology

J11. Ecohydrological Modelling

The interactions between water, soils and ecosystems are complex and can have important effects on water resources, land, and ecosystem management. This session welcomes ecohydrological modelling studies on issues involving relationships between water, soils, vegetation and other biota in different settings (rural/urban, pristine/modified, wetlands/drylands) spanning plot to catchment to global scales and over short and long temporal scales. Contributions may address water use by ecosystems, biochemical cycles, vertical fluxes between soils and atmosphere, ecosystem responses to floods and droughts, and climate change effects.

Keywords: Hydrology; Plants; Soils; Atmosphere; Ecosystem response

K1. Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins

Globally, large river basins provide crucial values for agriculture, biodiversity, industry, and communities. Most of these large basins suffer from a complex set of water, environmental, socio-economic, cultural and management issues often impacted by significant anthropogenic and climate change-induced environmental degradation. While the stability of regional communities is dependent on river basins being highly productive, resilient and sustainable, the way river basins are often managed ignores diverse values and priorities which leads to conflict and inequitable trade-offs. New participatory models and tools are needed to integrate diverse values, including traditional cultural knowledges. The Murray-Darling Basin is an exemplary case of a large river basin where actors actively participate in the co-design of collaborative solutions for these ‘wicked problems’. For this session, we solicit contributions that present approaches from local to supra-regional scales addressing how integrated, participatory and systems-based analysis and modelling can contribute to addressing these social, environmental, agricultural, cultural and governance issues.

Keywords: Modelling complexity; Large river basins; Murray-Darling Basin;

K2. Statistical, post-processing and verification methods for environmental prediction

This session will focus on statistical and data driven methods that are used to improve the quality of predictions, and on methods that are used to evaluate and diagnose prediction quality. It includes, but is not limited to, the post-processing of probabilistic and ensemble predictions to improve reliability, statistical methods for the quality control of observations, data driven methods (such as machine learning) for improving predictions, applications of decision theory to evaluate predictive utility, and advances in verification methods. The session will also cover post-processing methods used to generate downstream products for specific applications.

We particularly encourage submissions that make advances in such methods or that promote techniques developed in other disciplines (e.g. statistics or econometrics) that may be little known to environmental scientists but have potential within environmental prediction.

Keywords: Prediction verification; Post-processing; Machine learning; Statistics

K3. Advancing Drought Monitoring, Prediction, And Resilience For Sustainable Development

Drought is a complex and slow-onset disaster with far-reaching impacts on water resources, agriculture, ecosystems, and livelihoods. As climate change intensifies hydroclimatic extremes, droughts’ frequency, duration, and severity are increasing, posing significant challenges for water security and sustainability. Addressing these challenges is crucial for achieving global targets such as Sustainable Development Goal (SDG) 6 – Clean Water and Sanitation, SDG 13 – Climate Action, and SDG 15 – Life on Land.

This session will bring together researchers, policymakers, and practitioners to explore the latest drought science and management advancements. We welcome contributions that focus on:

Innovative approaches to drought monitoring, including remote sensing, in-situ observations, and multi-variable indices.
Advances in drought prediction and early warning systems, leveraging climate models, statistical methods, and emerging technologies.
Understanding drought propagation across hydrological, ecological, and socio-economic systems.
The role of climate change and human activities in shaping drought patterns and water availability.
Practical solutions for drought mitigation and adaptation, including policy interventions and nature-based strategies.

This session aims to bridge the gap between science and action by fostering interdisciplinary discussions, ensuring that research contributes to real-world resilience and sustainable water management. We invite perspectives from across disciplines and regions to share insights, challenges, and solutions to tackle one of our time’s most pressing hydroclimatic issues.

Keywords: Vegetation Responses; CO2 Rise; Drought; Water Use Efficiency; Soil Moisture; Global Drought Patterns; Extreme Climatic Events; Food Security Under Drought

K4. Understanding and modelling catchment behaviour in a variable and changing climate

This session explores how catchments react to climate changes over periods ranging from several years to multiple decades, and how this can be simulated for water planning purposes. Catchments are complex systems that respond to external influences (such as climate change) across various timeframes due to the interplay and feedback loops among their elements. Recent findings indicate that current models and techniques tend to underestimate the effects of climate changes on streamflow, which significantly impacts the dependability of these methods for future planning. The poor performance of these models suggests that they may misrepresent or overlook crucial processes, their timescales, or the interactions between them. These shortcomings must be rectified to improve our ability to project hydrological conditions for future climate scenarios – particularly those scenarios that may exceed the range of variability of the past.

We welcome submissions on topics including, but not limited to:

Initiatives to enhance the realism of hydrological models for projections under future climate scenarios;
Improved understanding of water-related and/or biophysical processes linked to long-term climate shifts that may contribute to apparent changes in hydrological responses;
Comprehending and measuring catchment “memory” over multiple years;
Research that expands or reassesses established hydrological principles (such as the Budyko hypothesis) for predictions under changing conditions; and
Data-driven and modelling studies aimed at better understanding and simulating how water systems respond to historical climate variability and change.

Keywords: Climate change; Hydrology; Models; Drought; Projections

K5. When models fail: lessons learned from false starts, wrong turns and dead ends

It’s somewhat disputed whether Thomas A. Edison actually said “I have not failed. I’ve just found 10,000 ways that won’t work” but the sentiment holds true for modellers and their models. In this session, inspired by the 2008 ‘Court of Miracles of Hydrology’ workshop, we invite contributions from modellers on the cases where their models have failed or not behaved as expected. These failure stories are rarely published in the scientific literature. By identifying the key lessons learned by sharing stories of case studies where previously well behaved models performed poorly it can help others to avoid repeating the same mistakes. We welcome contributions from all environmental modellers – the aim here is to lead to improved prediction across the wide spectrum of environmental modelling. Topics may cover statistical approaches through to physical models, data ‘outliers’ and failures in operationalising models. Lessons learned from model failures that led to eventual success are also welcome.

Keywords: Modelling; failure; Lessons learned

K6. Advancing hydroclimate forecasting: methods and applications.

Hydroclimate and hydrometeorological forecasts are increasingly available for lead times of hours through to seasons. These forecasts are beneficial for a wide range of applications across the water resources management and agricultural domains for planning routine operations and responding to extreme events like floods and droughts. However, there are many outstanding challenges in producing new forecasting systems, including: developing methods that are as accurate as possible; the deployment of real-time forecasting systems; and understanding the role of forecasts in operational decision-making. This session invites contributions from scientists and practitioners covering developments in forecasting methods and applications on potential topics, including but not limited to:

•Developments in modelling approaches for hydrometeorological or hydroclimate forecasting
•Extreme events
•Statistical and machine learning methods for forecasting
•Hybrid forecasting techniques (e.g., blending dynamic and ML methods)
•Ensemble forecasting and uncertainty analysis
•Evaluation of forecasts for decision making
•Post-processing techniques
•Operational challenges in implementing forecasting systems
•Applications of new forecasting systems

Keywords: Hydrological forecasting; Forecast evaluation; Operational forecasts; Forecast-informed decisions

K7. Understanding hydrological extremes: trends, drivers and impacts

Hydrological extremes such as floods, drought, extreme precipitation, heat waves, and storm surge have disastrous consequences on the economy and society which are only becoming more frequent and severe due to both climate change and anthropogenic activities. We invite contributions from any field investigating the nature of hydroclimatic extremes, particularly those aiming to simulate, predict, and understand extremes. Potential submissions include but are not limited to (i) Data analysis on global and regional trends in frequency and intensity of hydrological extremes; (ii) Process- and AI-based modelling for improving the prediction of hydrological extremes; (iii) Understanding how land surface changes (e.g., land cover changes, urbanization, wildfires, vegetation greening) and anthropogenic interventions (e.g., dams, reservoirs) cause and intensify hydrological extremes; (iv) Natural and societal impacts of hydrological extremes, as well as management solutions. By bringing together interdisciplinary research from environmental science, hydrology, and policy, this session aims to enhance our understanding of hydrological extremes and inform management practices in response to global changes.

Keywords: Extremes; Extreme Rainfall; Flooding; Droughts; Storms; Heat waves; Climate Change

K8. Watershed flood regime changes and their prediction

Flood events usually show tremendous spatial and temporal variabilities in behaviour due to heterogeneities in meteorological and underlying surface conditions across basins or entire regions. Particularly, flash floods usually happen in the small mountainous watershed, but have remarkable damage to human lives and property due to flash flooding and their potential secondary disasters. They are one of the costliest and most dangerous natural disasters globally and have the highest mortality rate. Existing studies usually focus on the magnitude and timing of flood events, and the variability, timing, duration, and rate of changes are usually ignored, all of which are the critical components of flood events. The prediction performance still needs to be improved further due to the impacts of complicated hydroclimate and underlying surface conditions. Therefore, it is fundamentally important to investigate the flash flood change characteristics and prediction in the small watersheds for both flash flood scientific research and management purposes. The objectives of this sessions are to (1) investigate the flood regime changes and their causal mechanisms under the impacts of climate changes and human disturbances in both China and Australia; (2) discuss the new technology applications (e.g., hydrodynamic modelling, machine learning) for flash flood predictions with international researchers.

Keywords: Flash flood; Flood behavior; Change characteristics; Prediction

L1. Catchment Water Quality Modelling

Deteriorating water quality in rivers and streams can have significant environmental, economic and social impacts. As such, it is increasingly important to enhance our understanding of key catchment processes and to predict future water quality through catchment modelling.

This session aims to address recent advances in modelling water quality in natural, rural and urban catchments, including:

Representation and understanding of catchment water quality processes, systematic behaviours, and predicting future water quality
Modelling to assist water quality policy development, decision-making and management for natural, rural and urban catchments
Model-based engineering and planning solutions for water quality improvement and environmental restoration in rural and urban catchments.
New insights on water quality assessment reflecting aesthetical values, ecological values, engineering values, carbon emissions and sinks, emerging pollutants, etc.
Novel approaches, techniques and tools in water quality modelling and analysis, such as applications of machine learning, remote sensing techniques, and integration of water quality models with ecological systems and modelling across environments, land uses and scales.

We welcome contributions on water quality modelling at a range of scales, such as sub-catchment and/or catchment-scale modelling, and large-scale programs such as modelling for the Great Barrier Reef.

Keywords: Catchment, Water quality, Modelling, Management

L2. Advanced Lake, Reservoir, Wetland and River System Modelling

The development and application of numerical models for assessing hydrodynamic and biogeochemical processes on water bodies has seen considerable development in recent years. New modelling platforms, improvements to existing models and applications to unique environments have all led to significant advances in our ability to understand water quality and biological impacts of different driving forces such as pollution, land use change and climate change.

This session seeks to present papers encompassing all elements of lake, wetland and river system modelling focused on assessment of biophysical and biochemical system responses, including (but not limited to), harmful algal blooms, pathogens, eutrophication, stratification, pollution mitigation or abatement, pollution assimilation and/or evaluation of source, cause and extent of water quality impacts. We particularly encourage papers which develop and apply novel techniques or novel applications through existing or new tools.

Keywords: Hydrodynamics; Water quality; Biogeochemistry; Lake; Wetland; River; Dam; Reservoir

L3. Modelling water quality treatment and management

Water can contain contaminants such as sediments, nutrients, metals, microorganisms and emerging contaminants of concern. Management systems are needed both in urban and agricultural catchments for: (i) the treatment of water for reuse (e.g., irrigation, drinking), and (ii) the treatment of both wastewater (domestic and industrial) and stormwater prior to discharge into the environment, often utilizing various modelling approaches for water treatment planning, alongside intervention models to enhance treatment efficiency and sustainability. There are a wide range of management systems currently in use. These include grey and traditional infrastructure (e.g., water treatment plants) or green infrastructure (e.g., wetlands, biofilters). Management systems can also range from structural systems to non-structural systems. Modelling water quality treatment and management options involves assessing the physical, chemical, biological, socio-political, and economic factors that influence system performance. This includes simulating various treatment processes, optimizing management strategies, and using intervention models like adaptive management frameworks and decision-support tools to improve effectiveness and achieve water quality goals.

This session aims to address the modelling of water management and treatment systems including:

Approaches for modelling water treatment and wastewater treatment processes and infrastructure;
Modelling green infrastructure (e.g., wetlands, biofilters, infiltration systems);
Techniques and approaches to modelling non-structural water management systems (e.g., socio-economic policies);
Calibration, sensitivity analysis and uncertainty analysis of water management and treatment systems;
Data-driven models / physical-based for real-time control of water management systems;
Modelling to assist in treatment train planning / design / and real-time operation.

Keywords: Modelling, Water treatment, Wastewater, Stormwater, Green infrastructure, Non-structural management

L4. Modelling Erosion and Pollutant Dynamics

Sediments and nutrients from catchments flow into inland, coastal, and marine waterways, degrading water quality and often impacting environments. Understanding the sources and contributions of these landscape processes is crucial for effective water quality management and mitigation. In urban and rural waterways, sediment and nutrient pollution arise from complex interactions between erosion processes (overland, gully, and streambank erosion), human activities (e.g., farming tillage, construction), and transport mechanisms via overland flow. These processes are further influenced by hydroclimatic events, land use and management changes, and climate change over time.

This session aims to enhance our understanding of erosion and nutrient transport modelling in urban and rural catchments, and how these processes evolve with climate, catchment conditions, and management practices. We seek to foster interdisciplinary collaboration and share pioneering research to advance the management of sediment and water quality processes. We welcome contributions on topics including, but not limited to:

Utilizing models to pinpoint sediment and water quality sources in catchments (e.g., unmixing models and sediment fingerprinting)
Developing models to describe erosion and water quality mobilisation and transport across catchments and larger spatial scales as well as across temporal scale (e.g. historical, forecasting and projections)
Investigating drivers, mechanisms, and impacts of water quality changes from local to regional and continental scales
Projecting sediment and water quality generation and transport under various land use and climate change scenarios
Presenting case studies and innovative modelling approaches for predicting and analysing water quality variability and process changes
Investigating impactful water quality events including compound events (e.g. black water events after bushfires)
Applying modelling to plan and evaluate conservation and rehabilitation activities

This session seeks to be a platform for cutting-edge discussions and advancements in the field of erosion and pollutant transport modelling.

Keywords: Water quality; Erosion; Sediment; Nutrient; Transport; Catchment; Modelling; Climate change

L5. Innovative approaches to water quality forecasting and projections using machine learning and novel sensor technologies

Efficient water quality management is critical for ensuring the sustainability of water resources. This session explores the use of machine learning (ML) techniques and novel sensor technologies to enhance water quality monitoring, forecasting and projections.
To advance predictions, we invite contributions that leverage on large water quality datasets collected from remote sensing, in situ and advanced sensors to develop and validate predictive water quality models with high accuracy. Further, we welcome contributions on the use of novel ML algorithms, such as Random Forest, Extreme Gradient Boosting, and Neural Networks, to analyse complex datasets and to predict key water quality indicators. These new types of models can demonstrate the potential of these models to inform decision-making processes by providing timely and reliable forecasts of water quality.
The session highlights the importance of integrating cutting-edge technologies and data-driven methods to address the challenges of water quality management in the face of climate change and increasing anthropogenic pressures.

Keywords: Novel water quality sensors, Machine learning, Water quality forecasting and projections

M1. OR methods and applications

This session invites papers that seek to advance the general field of OR, but that may not fit neatly into one of our other sessions. This may include, but is not limited to theoretical developments or applications of combinatorial optimisation methods (e.g., linear, integer, non-linear or stochastic programming, metaheuristics, evolutionary methods, etc.) and other data science techniques (e.g., queuing theory, simulation, Markov decision processes, data envelopment analysis, optimal control, real options analysis, etc.).

Keywords: Operations Research; Combinatorial optimisation; Continuous optimisation; Discrete event simulation; System dynamics; Data science; Planning and scheduling; Decision support;

M2. Simulation modelling and analysis

Real-world problems are often too complex to model and rarely analytically tractable to solve. Simulation modelling is a powerful tool used to alleviate these difficulties by capturing intricate relationships and uncertainties associated with complex systems. Thanks to the increasing amount of computational power and a flood of easily accessible data, simulation models have been coupled with optimization algorithms (e.g., heuristics and metaheuristics), machine learning (e.g., reinforcement learning), and data science (e.g., predictive and prescriptive analytics) methods to enhance capabilities of simulation models and to address challenging decision-making problems. The goal of this session is to bring recent theoretical and applied developments in simulation modelling (including, Agent-Based, and Discrete Event models) and their intersections with optimization, machine learning, and data science. Papers are invited in, but are not limited to, the following areas:

Simulation modelling applications and case studies in defence, supply chain management and logistics, healthcare systems, mining, manufacturing, maintenance, communication systems, etc.
Single/multi-objective simulation-based optimization
Multi-method simulation modelling (e.g., Agent-based + Discrete Event)
Integration of simulation models with machine learning and data science methods

Keywords: Simulation modelling; Decision making; Simulation-based optimisation; Multi-method modelling

G7. Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions

The report “Spreading like Wildfire” by UNEP and GRID-Arendal finds that factors such as climate and land-use change are influencing Wildfire patterns everywhere. Wildfires, along with prescribed burns and agricultural burning practices, are collectively termed landscape fires. A global increase of extreme wildfires is anticipated given projected heatwaves and droughts, even in areas previously unaffected. Uncontrollable and extreme wildfires can be devastating to people, biodiversity and ecosystems. They also exacerbate climate change, contributing significant greenhouse gases to the atmosphere. Given the implicit, intricate links between climate change, biodiversity loss and environmental pollution associated with landscape fires, as well as the potential impacts on public health, local biodiversity and natural resources, environmental modelling and simulation are key tools for our ability to consider scenarios of the future. Equally, integrated modelling systems are indispensable for both the ex-post assessment of interventions and the effectiveness of mitigation measures, as well as the ex-ante exploration of possible future scenarios. This session invites contributions from modelling studies, scenario simulations, tool developments and data science approaches addressing current and future challenges related to landscape fires. In addition, inter- and transdisciplinary modelling of environmental and public health impacts related to fires are welcome. Finally, the session would suit submissions explicitly addressing the interconnectedness of drivers, impacts and potential policy responses to the triple existential crisis of climate change, biodiversity loss and environmental pollution specifically related to fires.

Keywords: Wildfires; Landscape fires; Biomass burning; Air pollution; Air quality; Atmospheric composition; Health impact assessment; Environmental impacts; Drought; Climate change; Biodiversity loss;

G9. Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies

Over the past century, significant progress has been made in the modelling of flood inundation and tsunamis, leading to continuous advancements in this critical field. Despite these developments, modelling remains challenging due to the inherent complexity and chaotic nature of these systems. Recent breakthroughs have been driven by enhancements in modelling structures and algorithms, data assimilation techniques, integration of remote sensing data, the combination of diverse modelling approaches, and the adoption of cutting-edge technologies such as artificial intelligence (AI), machine learning (ML), and cloud computing.

An important area of advancement is the ability to generate quantitative estimates of uncertainty associated with model simulations and predictions, enhancing the reliability and applicability of models. These improvements have been instrumental in practical applications such as risk mapping, damage assessment, real-time forecasting, hydraulic structure design, river and coastal restoration, and water resources planning, while also supporting critical decision-making processes in disaster management and climate adaptation strategies.

At the same time, the rapid growth in data need and algorithmic complexity widens the gap between academic researchers and practitioners, which compromises the adoption of modern modelling methods. As a result, practical application will also be a focus of this session with the of development of simplified, yet scientifically sound floodplain models.

This session invites contributions showcasing the latest innovations in flood and tsunami modelling, with a focus on diverse approaches, applications, and technologies and their practical application. Topics of interest include, but are not limited to:

Innovations in modelling tools, structures, and techniques.
Advances in data collection, assimilation, and processing.
Comparative studies of different modelling approaches.
Hydrodynamic and inundation modelling.
Integration of remote sensing data with modelling frameworks.
Applications of machine learning (ML) and artificial intelligence (AI) for real-time inundation simulation.
Flood frequency analysis and forecasting.
Risk assessment, hazard mapping, and resilience planning.
Modelling of flash floods, storm surges, and tsunami impacts.
Case studies of urban and rural flood and tsunami modelling.
Quantifying and addressing uncertainties in modelling and predictions.
Supporting decision-making through improved modelling outputs.
Cross-disciplinary approaches and innovative methodologies.

The session aims to highlight the breadth of advancements in flood and tsunami modelling, promote collaboration across disciplines, and inspire new approaches to address the challenges posed by these dynamic systems.

Keywords: Floodplain inundation, Risk assessment, Storm surge, Tsunami, Uncertainty, Hydrodynamic modelling, Remote sensing, Flood frequency analysis, Flood forecasting

H1. Use Of Artificial Intelligence And Machine Learning In The Design And Modeling Of Disease, Cancer, And Safety Data

The integration of AI and machine learning in health data and industry safety data modelling has increased. These models often require addressing multiple layers of data, including community-level data where individual-level data may be unavailable. We encourage colleagues to submit papers on various methods and new methodologies/applications in clinical study design and modelling for cancer, real-world evidence, other diseases, and industry safety data within translational research, focusing on utilizing AI and machine learning in these studies.

Keywords: Artificial Intelligence, Machine Learning, Modelling, Clinical Trial, Chronic Disease, Cancer, Safety data,

H2. Data science and simulation modelling methods in health

Papers are invited presenting new and innovative modelling, data science and decision support methods to address the challenges of modelling in the health sector, including but not limited to: model testing; validation and calibration methods; data collection, availability, and suitability for modelling; uncertainty; data visualisation methods, knowledge development and capacity building for modellers and decision makers.

Keywords: Data science, Health modelling and simulation, Model testing, Health data visualisation

H3. Evidence-Based and Optimal Decision-Making in Health Services Research through Simulation and Modelling

In recent years, advanced modelling and simulation techniques have been increasingly applied to health services research, offering robust, evidence-based insights to support clinical decision-making, optimise resource allocation, and evaluate health policies. This session will explore how advanced simulation models, such as system dynamics (SD), discrete event simulation (DES), and agent-based modelling (ABM), and simulation-based optimisation can be leveraged to generate actionable evidence for improving service delivery and optimising decision-making in oncology, chronic disease management, and broader healthcare system planning.

By integrating real-world data with simulation frameworks, these models enable evidence-based scenario testing, capturing uncertainty in healthcare decision-making and informing the design of optimal, efficient and equitable health systems. This session invites contributions that present methodological advancements, comparative modelling approaches, and real-world implementations of advanced simulation models in health services research.

Key areas of interest include:

Simulation of patient pathways and treatment decision-making, including genomic-based risk stratification and survivorship care.
Optimising resource utilisation in hospital and primary care settings, including workforce planning and service delivery.
Policy evaluations and economic modelling, assessing the impact of interventions on healthcare using data, machine learning (ML) and artificial intelligence (AI).
Modelling population health dynamics, integrating behavioural and epidemiological parameters to understand disease progression and system-wide effects.

We invite contributions demonstrating how these advanced modelling techniques generate evidence to inform practice and policy, bridging methodological rigour with real-world implementation to enhance health services’ efficiency, effectiveness, and equity.

Keywords: Health Services Research, Modelling, Optimization, Evaluation

H4. Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens

Vector-borne and zoonotic diseases represent a significant source of deaths and morbidity around the globe. Environmental pressures, such as climate, land use and ecosystem resilience play an important role in the spread of these diseases and their spillover from wildlife and domestic animals into people. While this is particularly true for lower-income or developing countries, high income countries such as Australia are still at risk from a range of endemic and emerging diseases including Ross River Virus, Dengue fever, Hendra and Japanese Encephalitis. Meanwhile, our neighbours in the Pacific Island Countries and Territories continue to face high disease burdens coupled with under-funded health care systems and the rising tide of climate change.
In this session we look at the use of environmental modelling for exploring the environmental drivers of vector-borne and zoonotic disease burdens, with a focus on (but not limited to) Australia and the Pacific region. We encourage studies that represent a systems or Planetary Health approach, or that explore the complex relationships between human disease risk and the environment, how these relationships could change in a warmer world, and how models can inform interventions or policies to reduce the burden, particularly for those in poorer regions. These could include, but are not limited to systems dynamics, Bayesian geostatistics, Bayesian networks, agent-based models. We are keen to hear talks on either the direct or indirect effects of vector-borne and zoonotic disease burdens.

Keywords: Infectious diseases; Climate change; Human health

H5. Development and applications of simulation models in health economics

Simulation modelling plays a pivotal role in health economics as it allows extrapolation of health outcomes and costs, prediction of final endpoints based on intermediate outcomes, synthesis of evidence from different sources, generalisation of the results from clinical trials, evaluation of uncertainties, and generation of information about which health interventions represent value for money. This session welcomes submissions from a wide range of modelling studies and development of software tools for prediction of health outcomes as well as health care costs to support economic evaluations. Models that can be used to simulate disease progression and/or predict serious health-related events over a long time period in people with ill health, and those to predict transmission of infectious diseases and assess the impact of interventions on health and economic burden are of special interest. Examples of model types include state-transition models, discrete-event simulation, probabilistic discrete-time simulation, system dynamics and agent-based modelling.

We encourage submissions of studies involving development of new modelling methods or use of modern techniques to increase accuracy of the predictions or to address complex issues in decision making. Examples of such methods include the use of big data and machine learning or Bayesian approach in the simulations and development of new metrics for calibration of existing models. Other areas of interest include methods to reduce computational burden in patient-level or microsimulation models and translation of simulated results into practice.

Topics

Modelling health outcomes in non-communicable diseases
Modelling transmission of communicable diseases
Decision analytic models for health economic evaluations
Advanced methods in health economic modelling
Calibration and parameterisation of health economics models
Tools for development of health economics models

Keywords: Health Economics; Economic Evaluation; Cost-Effectiveness Analysis; Cost-Utility Analysis; Cost-Benefit Analysis; Decision Analytic Modelling; Disease Simulation; Disease Modelling

I1. Modelling and Simulating Human Behaviour and Movements

The ubiquity of data and sensors has enabled modelling of complex systems with unprecedented precision, whether it be the intricacies of human body movements or the mobility of entire populations. The combination of physical models of human body mechanics with cognitive models of human behaviour is an exciting avenue in current research for predicting how such systems may respond when perturbed in extreme events.

This session will focus on advancements in computational models that capture the complexity of human actions and interactions. We invite work on innovative approaches, technologies, and applications of modelling and simulation of human behaviour, movements, and population mobility.

We encourage all submissions in human behaviour modelling and simulation, and are particularly interesting in the following topics:

Methods for Analysis and Emulation of Body Movements from Sensor Data: Data analysis, feature extraction, anomaly detection and emulation of the large movement data sets that are available from wearable sensors and camera-based motion capture.

Simulating Body Movements: Three-dimensional modelling of movement of one or more individuals, by physics-based or machine learning approaches.
Large Scale Agent-Based Models: For simulating the behaviour of groups of humans for understanding complex systems and predicting outcomes in dynamic environments.
Crowd Dynamics and Evacuation: Techniques used to simulate crowd behaviour and evacuation processes, both pedestrian and vehicular.
Traffic Flow and Transportation Systems: Activity and agent-based models for simulating traffic at scale, for testing interventions that improve transportation efficiency.
Workplace Safety and Ergonomics: Human behaviour modelling for creating safer and more ergonomic work environments for reducing workplace injuries.
Community Safety and Disaster Response: Simulating human behaviour in the context of community safety and disaster response.
Health applications: Preventative health or treatment applications of movement analysis and simulation.
Sports Science: Improving athletic performance and injury prevention through biomechanical simulations.
Virtual Reality: Modelling and simulation of human behaviour for immersive and realistic virtual environments for training and education.

Keywords: Biomechanics; Agent-based modelling; Movement; Crowd; Evacuation; Traffic Flow; Ergonomics; Sports; Rehabilitation

I2. Advancing System Dynamics Modelling: Integrating Emerging Technologies and Participatory Approaches for Enhanced Decision Support in Social Systems

This session explores the evolving landscape of system dynamics modelling, focusing on the opportunities and challenges presented by emerging technologies, including AI-driven modelling approaches, and the application of participatory modelling approaches in decision support for complex multi-stakeholder social problems. System dynamics remains a powerful tool for capturing complex system behaviours, integrating diverse perspectives, and fostering shared understanding. However, as modelling techniques evolve, key questions arise around stakeholder engagement, model transparency, usability, and the role of automation and AI in shaping future practice.

Bringing together experts and practitioners, this session will explore how system dynamics can adapt to technological advancements while continuing to support decision-making in complex social systems. Discussions will cover best stakeholder engagement practices, the applications of emerging technologies, and methodological innovations in system dynamics modelling. This session focuses on two main area; the first one includes strategies for enhancing decision-maker engagement, integrating system dynamics modelling into decision-making processes, and designing intuitive model interfaces that improve usability and adoption. Next, the session welcomes discussions on the implications of AI and machine learning for system dynamics modelling—how they can enhance, challenge, or transform traditional approaches.

This session is a collaborative effort between The Oceania Chapter of the System Dynamics Society, represented by Dr. Hossein Hosseini, and The Modelling and Simulation Society of Australia and New Zealand Inc. (MSSANZ), represented by Professor Sondoss El Sawah. It aims to foster knowledge exchange across modelling communities, highlight innovative applications, and explore how emerging technologies are shaping the future of system dynamics in socio-economic decision-making.

Keywords: System dynamics; Participatory systems modelling; Artificial intelligence; Stakeholder engagement

I3. Agent-based and data-driven modelling of complex social systems in public information environments

Online social platforms are dynamic spaces for information exchange, but are increasingly a potential vector for the proliferation of misinformation and malicious influence operations. This session explores the application of agent-based modelling and empirical data-driven approaches to understand the dynamics of complex social systems, such as online information environments.

By defining rules which govern individual behaviour and inter-individual interactions and simulating the repeated application of these rules within a population of individuals, agent-based modelling offers a powerful tool for understanding population-level phenomena in terms of the individual differences and interaction patterns that give rise to them. By integrating empirical data, researchers can create models that reflect real-world phenomena more accurately and use them to explore different scenarios and examine counterfactuals.

The session will explore applications in diverse public environments such as social media and forums, focusing on how behaviours adapt, information spread, and community dynamics evolve over time. Talks will explore questions like the differences between online and offline environments, and how the technological affordances of online environments, such as recommender systems, impact the system dynamics. Key challenges include managing model complexity, incorporating large text-based datasets efficiently, ensuring computational feasibility, and validating models against real-world data.
This session aims to foster discussions on the latest advancements in modelling techniques, their practical applications across multiple contexts including defence and national security, and offering insights into understanding complex social phenomena through a multidisciplinary lens.

Keywords: Agent-based models; Data science; Public information environment; Social influence; Recommender systems; Social networks; Social media; Information warfare

I4. Training future modellers for an uncertain future

The recent explosion of computing power and cheap sensor technology has triggered an avalanche of data, and generative AI is disrupting education and industry alike. At the same time, a rapidly changing climate and geopolitical instability make quantitative assessment more important than ever. What are the skills modellers need in this brave new world, and how do we develop these in our students and across our profession? How do we empower modellers to act ethically, think in systems and engage effectively with non-modellers, while developing rigorous technical capabilities? What can, and can’t, we outsource to the machines? How should we assess learning in a world of online education and ubiquitous access to large language models (LLMs)? Modelling is a broad church, encompassing data analysts and wranglers, decision makers, model developers and model drivers: what training do these groups need, in formal education and profession practice? This session welcomes submissions from anyone asking questions about how we develop modelling workforce capability to meet current and future demands. Interactive and participatory submissions are particularly encouraged, which use the session to collaboratively answer these challenging questions.

Keywords: Education; AI; Training; Capacity-building; Future workforce

I5. Indigenous Modelling and Participatory Approaches

This session solicits presentations by Indigenous researchers on all aspects of modelling and simulation, as well as presentations by non-Indigenous researchers on matters of interest to Indigenous communities and peoples. This session highlights the growing field of Indigenous-led and community-informed modelling and simulation. We invite presentations by Indigenous researchers on how Indigenous ways of knowing and being can guide the development of modelling frameworks, from social systems to environmental phenomena. We also encourage contributions by non-Indigenous scholars who collaborate ethically and reciprocally with Indigenous communities, applying participatory methods to address social, cultural, economic, and environmental challenges. Topics may include (but are not limited to): system dynamics, agent-based models, geospatial applications, storytelling approaches, data sovereignty and governance, and innovative ways to combine traditional knowledge with new technologies. Presentations will showcase diverse disciplines and methods, with an emphasis on community-driven outcomes and ethical engagement.

Keywords: Indigenous Researchers; Indigenous Issues; Participatory Decision-Making; Ethical Collaboration; Data Sovereignty; Community-Engaged Scholarship

J1. Long term shifts in hydrological systems

Water systems are complex and dynamic, exhibiting changes in behaviour as a result of environmental disturbances and climate change. Prolonged stressors, such as hydrological droughts, can push these systems beyond critical thresholds, leading to long-term changes known as “state shifts.” A state shift refers to a significant alteration in the fundamental probability distribution of a variable.

In the context of water resources, it means a catchment switching its stable state from its threshold called tipping point as a result of any disturbances. The catchment continues to remain in that altered state until another disturbance pushes it back to normal conditions. When these conditions continue to be for long number of years, it causes long term shift. These “state shifts” result in significant changes in the system’s structure, function, and resilience.

Key drivers of state shifts include prolonged droughts, extreme weather events, land-use changes, and climate change. These factors can disrupt hydrological processes, depleting soil moisture, reducing groundwater recharge, and diminishing streamflow. Multiple findings from different parts of the world have shown that prolonged hydrological droughts often lead to long-term state shifts in hydrological variables, especially streamflow.

Understanding the dynamics behind state shifts in hydrological variables is crucial for effective water resource management, as it highlights the importance of recognizing factors that can push hydrological systems beyond critical thresholds and thereby affecting resilience of the system.

This session invites papers addressing :

State shifts in hydroclimatic variables :Changes in streamflow, evapotranspiration, or groundwater.
State shifts in water quality variables : Long-term impacts of pollution and salinization.
Drivers and impacts: Factors causing state shifts and their hydrological, environmental and societal effects.
Predicting state shifts: Modelling and tools for forecasting and estimating these state changes.

Keywords: State shifts, Hydrological systems, Long-term change, Disturbances, Water resources

J2. River System Modelling for Water Resources Management: Advances and Challenges

Hydrological modelling of river systems plays an important role in water management. In the past four decades, numerous rainfall-runoff models, river water balance models, groundwater models and hydraulic models have been developed to inform the development and implementation of key water management policies in regulated and unregulated river valleys in different jurisdictions within Australia. In the early years of river system model development, the key focuses were on catchment runoff generation, river flow and water demand modelling of key water users to support development of water sharing plans. In the recent years, there has been greater emphasis on enhancing capability of river system model to represent various consumptive water uses, floodplain take, environmental flow, cultural values, surface and groundwater exchange, water trading, climate risk, etc. to support greater transparency and efficiency in water resources management.

This session aims to bring together researchers, modellers, practitioners and policy makers to discuss the current successes, issues, and potential solutions in dealing with challenges in river system modelling to support water resources planning and management in changing environment in the 21st Century. Contributions are invited in any aspect of hydrological and river system modelling targeted at servicing modern water management decision making, including:

Environmental flow processes
Water trading
Integrations of river system models for large basins
Surface water and groundwater interactions
Integration of river system and hydraulic models
Addressing climate risk in water modelling and management
Uncertainty in data and modelling
Workflow and automation for greater efficiency and transparency
Hydrological non-stationarity

Keywords: River System Modelling; Hydrological Modelling; Model Integration; Water Resources Management; Uncertainty in modelling; Climate Risk.

J3. Novel methods for data collection and predictive modelling in data sparse environments

The reliable modelling and simulation of physical and ecological processes requires measurements to use as inputs, identify dominant processes, calibrate parameters as well as evaluate outputs. Nonetheless in many parts of the world these measurements do not exist, are of low quality and/or are at an inappropriate scale.

The acquisition of measurements to inform numerical modelling, however, can be costly and time consuming. Furthermore, in some regions, such as the remote and sparsely inhabited regions of northern Australia, field sites can be difficult to access, and some data collection activities can pose a risk to human safety.

We invite papers that outline:

Novel cost-effective environmental data collection techniques, particularly in remote and vast environments.
New sources of data from field-based sensors or remote sensing for use with numerical modelling.
Use and application of novel existing data or data that has been hitherto underutilised.
Innovative ways of reducing, quantifying and/or communicating uncertainty when faced with data limitations in the modelling process.
Methods that optimise data collection to minimise cost and time.

This session follows on from the well-attended session at MODSIM 2023 and we look forward to discussing the latest developments to improve modelling in data sparse environments.

Keywords: Data collection; Monitoring; Predictive modelling; Uncertainty; Data limited

J4. Responding to the Challenges and Complexities of Urban Water Management

Managing urban water requires consideration of a diverse range of challenges and requirements, including population growth, urbanization, climate change, complex regulatory requirements, affordability of services, social inclusion and community expectations to reduce environmental impacts (Sivaguranathan, Elsawah and Khan, 2022).

In response to these complexities, urban water managers rely on a broad range of modelling expertise, this session seeks to bring together this diverse range of expertise. As such, managing water in the urban environment epitomizes the conference theme of Modelling for Positive Change: Bridging Science and Society. This session will connect researchers, modellers and policymakers working in the urban context and showcase the innovative, challenging and critical work they do to manage our precious water supplies.

Contributions are invited in any aspect of research or modelling application focusing on the urban context, such as:

Planning for future needs, such as population growth, climate change and managing uncertainty.
Water efficiency and demand management.
Planning for and managing dry conditions.
Integrated Water Resources Management.
Catchment management.
Stormwater Management for Sustainable Urban Futures.
Water Sensitive Urban Design (WSUD): Shaping Sustainable and Liveable Cities.
Climate change adaptation.
Use of ‘big’ data and other novel techniques.
Multi-disciplinary modelling approaches.
Collaboration and engagement with stakeholders and the community.

Sivagurunathan, V. Elsawah, S., and Khan, S.J. (2022) Scenarios for urban water management futures: A systematic review, Water Research, vol 211

Keywords: Urban water modelling; Integrated water resources management; Urban water cycle; Urban water management

J5. Fit for purpose modelling for water resources management

This session addresses the challenge covered by the much over used quote by George Box “All models are wrong, some are useful.” This session will explore what makes a model useful for water management and how do we know a model is fit for purpose. In the context of water resource management, and the related complex socio-ecological system, we have an increasing array of tools, data and models, coupled with increasing uncertainty in climate and social processes. There is therefore a countless number of ways a problem can be considered and represented by models. In the same paper, Box suggests that since all models are wrong, we need to focus only on what is importantly wrong – “It is inappropriate to be concerned about mice when there are tigers abroad”. Given the increasing uncertainty in water management, how well do our current modelling approaches address or help us communicate the important uncertainties. There are tradeoffs in selecting different modelling approaches and selection of modelling tools often goes unquestioned.

This session welcomes paper submissions that address questions such as:

Who should play a role in modelling choices (including selection of scales, system features and processes, and modelling tools or algorithms)?
What influences model selection?
What influences model trust? How can we improve this trust?
How well do we reflect on our existing modelling tools, including on their uncertainties and implications for decision making as well as trust in their outputs from users or other parties?
What frameworks can support selection of fit for purpose modelling?
What are the “tigers” we are missing for the “mice” in our water resource modelling?
In what way do different water management problems or contexts demand different modelling needs?

The session will include invited papers followed by a panel discussion.

Keywords: Water resource modelling; Uncertainty; Decision making

J6. Hydroclimate extremes forecasting: from droughts to floods

Hydroclimate extremes, encompassing both droughts and floods, pose significant challenges to societies and ecosystems worldwide. The increasing frequency and severity of these events, exacerbated by climate change, necessitate a comprehensive understanding of their underlying processes and the development of effective forecasting methodologies. This session aims to unite researchers and practitioners focusing on hydroclimate extremes to foster a holistic understanding of these phenomena. By examining both droughts and floods, we seek to identify commonalities and distinctions in their governing processes, thereby enhancing predictive capabilities and informing mitigation strategies.

We invite contributions that delve into various aspects of hydroclimate extremes, including but not limited to:

Process Understanding: Investigations into the atmospheric, hydrological, and land surface processes that drive droughts and floods.
Modelling and Forecasting: Development and application of advanced methods and models to predict the occurrence, intensity, and duration of hydroclimate extremes.
Climate Change Impacts: Studies assessing how global warming influences the frequency and severity of droughts and floods.
Socio-Economic and Environmental Impacts: Research evaluating the effects of hydroclimate extremes on communities, economies, and ecosystems.

By bringing together diverse perspectives and expertise, this session aims to advance our understanding of hydroclimate extremes and contribute to the development of robust forecasting tools and adaptive strategies. We especially encourage submissions from early career researchers.

Keywords: Hydroclimate extremes; Forecasting; Climate change; Droughts; Floods

J7. The path forward for hydroclimate risk assessments - advancing science, modelling and management to support decision making in large river basins

In 2024 the Independent Hydroclimate Science Expert Panel provided their long-term guidance for assessing hydroclimate risks to the Murray-Darling Basin Authority. Key to this was that the future of modelling must be shaped by a deeper understanding of hydroclimate risks, synthesis of diverse data sources, and improved modelling frameworks to support adaptive water resources management under increasing uncertainty. Existing models and approaches, while valuable, often rely on historical assumptions that may no longer hold under intensifying climate variability and evolving socio-political contexts.

This session is focused on new frontiers in water resource modelling, particularly those that seek to represent a broader range of hydroclimate risk drivers—including compound events, non-stationary climate processes, and multi-scale interactions between water and human systems. We aim to showcase work that integrates multiple lines of evidence, such as palaeoclimate reconstructions, remote sensing, and scenario-based approaches to improve decision-relevant insights. Presentations will also discuss ways to improve the robustness, accessibility, and applicability of water resource models under climate change.

The format of this session will include a range of solicited speakers at the forefront of the science of hydroclimate modelling and management. However, we also welcome wider submissions that meet the aims of the session.

Contributions addressing methods advancements, including hybrid modelling and stochastic approaches are encouraged. Equally critical are discussions on governance and institutional frameworks that ensure modelling remains transparent, inclusive, and actionable for diverse water users, policymakers, and river managers. By fostering collaboration across ecohydrology, climate science, and policy, this session aims to chart a path toward modelling approaches that better support water planning and management in contested and climate-sensitive river basins.

Keywords: Hydroclimate risk assessments; River basin management; Water resource modelling; Climate change adaptation; Fit for purpose modelling.

E5. The Role of Microgrids in Building Resilient, Low-Carbon Communities

Microgrids are pivotal to Australia’s energy transition, providing localized, resilient, and sustainable energy solutions. This session will explore how microgrids, powered by renewable sources like solar and wind and coupled with energy storage, are transforming urban and regional energy landscapes. We will discuss the integration of microgrids into urban planning, emphasizing their role in reducing reliance on central grids, enhancing energy security, and supporting electrification goals. Presentations will highlight innovative technologies, including smart grids, and real-time data analytics which optimize microgrid performance. Case studies will demonstrate how microgrids contribute to energy efficiency, carbon reduction, and regional development. This session will provide practical insights into the design, implementation, and future potential of microgrids in creating sustainable, resilient communities across Australia

Keywords: Microgrids; Resilience; Sustainability; Renewable Energy

F1. Ecological and environmental modelling using a combination of mathematical and statistical approaches

Ecological and environmental modelling carries technical challenges that physical systems do not encounter as strongly. For example, there may be a myriad of plausible models – which range from empirical formulas to mechanistic descriptions – all of which fit the observations well. (By comparison, in physical systems, there may be a smaller group of well-established models.) Part of this problem is the greater uncertainty and stochasticity of ecological and environmental systems compared to physical systems.

This session invites talks which use a combination of mathematical and statistical approaches to address the modelling which is required in ecological and environmental systems. Empirical models, mechanistic models, differential equation models, either deterministic or stochastic, agent-based models, statistical models, Bayesian inference, uncertainty propagation, especially when constrained by observations or data, are welcomed. Applications to ecological and environmental systems can be broad or specific to a particular system. We encourage consideration of ecological and/or environmental models for improved understanding and/or forecasting.

Keywords: Ecology; Environment; Mathematics; Statistics; Uncertainty; Forecasting

F2. Advancing Environmental Information Models for a Sustainable Blue Economy

As marine environments face increasing pressures from climate change, human activities, and biodiversity loss, the need for integrated, data-driven decision-making has never been greater. Environmental Information Models (EIMs) offer a promising approach to managing marine shared spaces by synthesising diverse datasets, modelling ecological and hydrodynamic processes, and facilitating stakeholder collaboration. This session will explore the emerging field of EIMs and their applications in marine spatial planning, sustainable development and coastal management. Topics will include advancements in data capture techniques, geospatial data integration, real-time monitoring, predictive modelling, and development of digital twins for maritime design and asset management. We will also examine how EIMs support policy development, digitisation of environmental impact assessments, and sustainable resource management. Bringing together experts from academia, government, and industry, this session aims to highlight innovative technologies, showcase case studies, and discuss challenges in the implementation of EIMs when data are sourced from open and sensitive platforms. As Australia transitions towards sourcing water, food and energy in the blue economy learn how the key to growth lies in collaboration, innovation, good governance and interoperability.

Keywords: Environmental Information Model; Blue Economy; Collaborative Data

F3. Modelling for effective climate change adaptation

Current information and future projections of climate change have highlighted the need to adapt the management objectives and practices for environmental assets and systems to account for changed future hydrological and ecological states. Modelling provides a means for integrating data from multiple disciplines, including climatology, hydrology, ecology, economics and social sciences, to provide a holistic view of climate impacts, ecological vulnerability and potential adaptation responses. The multi-disciplinary approach allows the cumulative and interconnected nature of climate change impacts to be explored.

This conference session will highlight the importance of modelling as an important tool in climate change adaptation and present examples of different modelling approaches, including:

Scenario analyses to allow decision-makers to explore various future outcomes and understand future system boundaries.
Assessing potential effectiveness of adaptation strategies through simulation of different climate trajectories and policy responses.
Modelling as a communication tool to translate complex hydroclimate data into understandable visualisations of future states and conditions and nuancing aspects of change and persistence.
Facilitating collaborative decision-making and participatory approaches among policymakers, communities and researchers to improve confidence in developing adaptation pathways.

Keywords: Climate adaptation; Hydrology; Environmental Impacts; Climate Vulnerability

F4. Exploring the multiple roles of managed water within river basins

Managed water serves a multitude of purposes – including provision of water for irrigation, for protection of ecosystems and cultural values related to water, and to support livelihoods that are depend on that water.

This session will provide an overview of the context of managed water, what it means in and to different sectors and the benefits it can provide. Examples will be showcased from across the world (but not limited to) demonstrating ecological outcomes of environmental water, approaches to balancing the environment with vital irrigation requirements and identification of ecological flow requirements, how irrigation water can service environmental needs, and reinstating flows to support cultural water requirements and protect cultural values.

Keywords: Managed water; environmental water; environmental flows; river restoration; ecological outcomes

F5. Advancing decision support: Decision tools for building robust and resilient futures

In an era of increasing complexity and uncertainty, scientifically rigorous tools and methodologies that integrate multiple disciplines and approaches are needed to support decision making. This session will explore the role of modelling, simulation, local (social) engagement, and analytical frameworks in supporting decision-making processes that enhance resilience across environmental, social, and economic systems.

Effective decision support must address uncertainty, enable scenario exploration, and facilitate adaptive responses to dynamic challenges. We invite contributions that critically examine methodological advancements, applied case studies, and interdisciplinary approaches that underpin decision support for sustainable and resilient futures.

Topics of interest include, but are not limited to:

Integrated Modelling and Decision Support Systems – Approaches that link models across domains to inform complex decision-making.
Uncertainty and Risk Assessment – Methods for quantifying, managing, and communicating uncertainty in decision contexts.
Scenario-Based Planning and Resilience Strategies – Applications of exploratory modelling and adaptive pathways.
Bridging Science and Policy – Translating technical insights into actionable strategies for governance and management.
Case Studies in Decision-Making – Empirical insights from environmental management, infrastructure planning, resource allocation, and policy development.

This session aims to foster a dialogue between researchers, practitioners, and policymakers on the development and application of decision support tools. By highlighting both theoretical advancements and practical implementations, we seek to contribute to the ongoing evolution of decision-making frameworks that enable resilience and sustainability in uncertain futures.

Keywords: Decision Support Systems; Modelling and Simulation; Uncertainty Analysis; Resilience and Adaptation; Environmental and Resource Management

F6. Large scale cloud computing environmental information systems

Cloud computing facilitates development and implementation of ever larger scale information systems. We are looking for contributions describing the particular challenges and solutions when using cloud computing to model, simulate, and manage large scale environmental systems. We are especially interested in real-world, i.e., out of the academia, examples. Did the authors experience problems with e.g., simultaneous access to data by users and automated processes, heavy computing load from simulating large numbers of environmental objects, difficulties in spatial computations, short and long transactions, varying data models? How did the interaction between system architects/developers and users work out? What is/was the impact of the resulting system?

Keywords: Cloud computing; Environmental information systems

F7. Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices

The complexity of agricultural transformation in the face of climate change necessitates the development of innovative modelling approaches that can effectively capture the complex interactions between environmental, social, and economic factors. Model coupling, the process of linking multiple models to leverage their strengths and improve overall simulation accuracy, is a critical aspect of this endeavour. This session will delve into the challenges and opportunities associated with coupling models to support climate-resilient agricultural practices and sustainable resource management in the context of agricultural transformation. The session will discuss strategies for coupling diverse models, including but not limited to process-based and statistical approaches, agent-based models and techniques for ensuring consistency and compatibility across different scales, systems, and modelling frameworks. Additionally, contributions that couple optimization algorithms with models, e.g. for optimal land-use allocation, are welcome. Managing uncertainty and complexity in coupled models will also be a key topic of the session. Case studies will be presented to showcase successful applications of model coupling in agricultural systems, highlighting lessons learned and best practices. The session will also aim to identify gaps and opportunities for future research and collaboration in model coupling, including the development of new coupling algorithms and methodologies

Keywords: Agricultural transformation; Model coupling; Climate-resilient agriculture; Sustainable resource management; Uncertainty and complexity

F8. GIS and environmental modelling

This session focuses on the integration of geographical information science and environmental modelling. The technology of GIS is well established with tools for management, visualisation, and analysis of environmental information. System interoperability enables GIS software to be coupled to different types of environmental models, and new geoprocessing operations are also being developed to directly support environmental modelling within GIS. There are also new paradigms emerging that relate spatial patterns to environmental landscape processes. This session invites papers integrating GIS and environmental models that address technology issues, methodologies and innovative applications in the areas of, but not limited to: geoprocessing operations, coupled models, and spatial analysis to relate pattern with landscape processes.

Keywords: GIS; Geospatial; Geocomputation

F9. Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts

While ecologists have long developed predictive models of natural systems, such models have often had a relatively long time horizon. In contrast, near-term ecological forecasting, on which predictions are made on shorter timescales (days to decades), is a rapidly expanding field. Rapid environmental change, alongside increasing pressure from human activities, provides major challenges for decision-making in natural resource management, and near-term forecasting allows predictive models to be applied to support environmental decision-making on short time scales. Near-term forecasting also allows for an iterative model development-evaluation-improvement cycle that can support rapid advances in basic scientific understanding. However, the shift from longer term modelling to forecasting requires development of new techniques and community capacity-building. Building on the increasing reach of the Ecological Forecasting Initiative’s Oceania chapter, this session aims to bring together practitioners of near-term ecological forecasting to showcase new developments and share methodologies. We welcome presentations on ecological forecasts which seek to deliver predictions of abundance, distribution, and phenology for single species, multispecies, or communities, or predictions of ecological events such as harmful algal blooms, pathogen loads, species eruptions, and coral bleaching. Forecasts could be on time scales from nowcasts through decadal, and at spatial scales from local (~1 km) to regional spatial scales (~100+ km). Contributions that use short-term iterative forecasting methods to understand risk and response in natural systems are welcomed. Both method development and application aspects are appropriate.

Keywords: Forecasting; Near-term; Ecology; Environmental decision-making

F10. Applications of artificial neural networks and generative artificial intelligence to environmental problems

This session is dedicated to the application of artificial neural networks (ANN) and large language models (LLM) to environmental problems. Abstracts are sought across the following areas:

The development of benchmark datasets and pretrained models in the earth sciences for model development and evaluation. Anything from models on Hugging Face to community datasets such as CAMELS.
The development of ANN architectures for environmental problems (e.g. flood prediction). This includes convolutional neural networks, long short-term memory, graph neural networks, as well as other advanced statistical models. It also includes approaches that make use of geospatial remote sensing and strategies for improving model performance such as input feature engineering.
The communication and understanding of environmental knowledge, results and data using LLMs, including evaluations of their effectiveness through usability studies. This includes approaches to manage and communicate ethical issues such as biases, misinformation and overconfidence in results for decision-makers.
Innovative applications of LLM libraries and tools including multimodal capabilities to the environment domain. Anything from searching libraries of photos to producing podcasts of results.
Using LLMs to assist end users to tailor, customise and develop applications (i.e. using tools such as notebooks and chatbots).
The application of ethical and regulatory considerations to AI work in the environmental domain. Including, understanding legal frameworks and policies governing AI use in environmental applications.

Keywords: Generative Artificial Intelligence; Artificial Neural Networks; Large Language Models; Convolutional Neural Networks; CAMELS

F11. Agricultural Artificial Intelligence Modelling

As the agricultural sector faces increasing pressure to enhance productivity, sustainability, and resilience, Artificial Intelligence (AI) is emerging as a vital tool to address these challenges. This session will explore the transformative potential of AI-driven solutions in agriculture, focusing on innovative modelling and simulation techniques. The session aims to provide an in-depth understanding of how AI can be integrated into various agricultural processes to improve efficiency, reduce resource consumption, and promote sustainable farming practices. Topics include AI-Driven Precision Agriculture: utilising AI for targeted interventions, such as optimising irrigation and fertilisation, reducing input waste, and improving crop yields. Automated Pest Management: leveraging AI to detect and manage pest infestations early, minimising crop damage and reducing the need for chemical pesticides. Crop Health Monitoring: implementing AI-based systems for real-time crop health monitoring, enabling timely detection of diseases and nutrient deficiencies. Integration of AI with IoT: combining AI with Internet of Things (IoT) devices to enhance data collection, analysis, and decision-making on farms or at other points of the agricultural supply chain. Modelling and Simulation Techniques: exploring advanced modelling and simulation methods to predict crop performance, optimise farming operations, and assess the impact of various agricultural practices. The session will highlight the tangible benefits and practical applications of these technologies by showcasing real-world case studies and successful implementations of AI in agriculture. Additionally, discussions will address the challenges and opportunities in adopting AI-driven solutions, focusing on providing actionable insights for farmers, researchers, and industry stakeholders.

Keywords: Machine learning; Deep learning; Hybrid modelling; Crops; Yield

F12. The Future of Vegetation: Advances in Modelling Plant Function and Distribution

Natural and managed vegetation communities around the world are threatened by rapid changes in climate and land use. Anticipating future vegetation function and distribution shifts is critical for enhancing ecosystem resilience. Modelling approaches from local to continental scales provide important insights, including quantification of vegetation productivity, ecosystem health, carbon cycling, and water availability.

Models of varying complexity are currently used to address questions around future vegetation change. Statistical approaches project historical climate-distribution dependencies forward, while state-and-transition models impose shifts between ecological conditions induced by predefined triggers. Process-based soil-plant-atmosphere models explicitly simulate biogeochemical cycles, while ecosystem demography models also include cohort establishment, growth, competition, and mortality. It is common for different approaches to produce conflicting results, and no clear consensus has yet emerged on several key questions. For example, it is unknown whether CO2 fertilisation and increases in plant water use efficiency will overcome the negative productivity impacts of increasing drought frequency and severity projected for many regions. It is also unclear whether competition with other species or individual species’ physiological constraints dominate current vegetation distributions. Long-term legacy effects and landscape coevolution add a further layer of uncertainty to these questions.

This session will cover diverse approaches to advance understanding of vegetation function and distribution modelling. We welcome presentations on vegetation model development, evaluation, and applications. We encourage submissions that describe innovative approaches for model-data comparisons or leverage modern measurement techniques (e.g., remote sensing, drone-based monitoring).

Keywords: Vegetation; Modelling

F13. Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits

Agricultural landscapes are subjected to different land management practices with an aim for crop production as livestock production systems, is considered as one of the most important terrestrial ecosystems to mankind. It covers a significant proportion of land and arguably is the most heavily affected by human activity. However, the current management often focuses on optimizing yield, with the potential to get high greenhouse gas emissions and low biodiversity values, damaging the sustainability in the long term. There is a growing interest on emerging markets worldwide to improve resilience of the agricultural landscapes through enhancing the soil carbon storage, and to improve native species populations. We welcome studies on monitoring and modelling of soil carbon and biodiversity across the agricultural landscapes. Additionally, encourage empirical research on the processes related to soil carbon and biodiversity.

Keywords: Agriculture; Carbon; Biodiversity; Model

G1. Advances in land surface modelling for hydrology – a seamless perspective

Earth system models and their land model components, such as JULES and CABLE, are valuable for understanding the interaction between climate and hydrological processes over land across various scales, from minutes to centuries and from local to global scales. These models with sophisticated representation of critical hydrologic processes including land-atmosphere and land-ocean interactions, enhance our ability to understand, predict and project hydrological variables.
This session will explore recent advancements in modelling land surface processes within seamless Earth System Model framework and their applications in improving hydrological fields, particularly focusing on hydrological extremes such as floods and droughts across historical, forecast and projections timescales. The session aims to bring together researchers, modelers and decision makers to discuss innovative modelling techniques, advancements in modelling terrestrial and biogeochemical processes, data integration, model comparisons, machine learning and practical applications that enhance our understanding and management of hydrological extremes at multiple temporal and spatial scales.

Keywords: Land surface modelling, hydro-climate extremes, land-atmosphere interactions, water related hazards

G2. Recent Advances of Earth Observations in Climate Change Impact Research

Natural hazards such as floods, droughts, bushfires and land deformation have imposed significant negative impacts on environmental sustainability. Ongoing changes to the climate are likely to further increase the frequency and amplify the severity of extreme climate events into the future. There is a growing necessity to strengthen our capability to investigate, analyse, and map the current status and future trends of climate variability in a timely manner, and therefore to enhance our capacity in managing and mitigating climate change across space, time and scales. Recent advances in readily available remotely sensed data have improved sensing characteristics (i.e., high spatiotemporal resolutions from multiple sensors). They have offered an efficient and effective means to tackle the challenge when accompanying developments in artificial intelligence (AI) and cloud computer platforms (i.e., foundation model and Google Earth Engine). This session welcomes presentations on the integration of latest research developments in remote sensing technologies with other geospatial big data and AI techniques, and their applications for monitoring, modelling, assessing and predicting the potential impacts of climate change on natural resources and the environment across spatiotemporal scales, including agriculture, hydrology, land use/cover, flood, drought, bushfire, urban, and natural ecosystems.

Keywords:

G3. Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers

There is increasing demand for climate risk assessments due to several factors including:

New regulatory requirements of annual climate related financial disclosures for medium to large businesses (e.g. ASRS2)
Public and private sector management of existing assets and planning future infrastructure needs
Emergency managers needing to understand how to prepare for changing hazards

Climate scientists have developed sophisticated climate projections under different warming scenarios and under different socioeconomic pathways (i.e. RCPs and SSPs ). These datasets contain information about climate variables including temperature and rainfall for different future time periods and different global warming levels.

Impacts from changes in these climate variables are experienced through variations in natural hazards including heatwaves, storms, floods, fires, cyclones and extreme winds. Additionally, their intensity, frequency and/or duration can change into the future. This requires specialised expertise in each of these domains to translate the climate projections into relevant hazard predictions. These hazards can cause direct impact to assets (i.e. damage or destruction) and indirect impacts due to loss of productivity.

According to the IPCC definition, Climate risk is defined as the interaction between hazards, exposure, vulnerability and response. For financial decision making, risk is quantified with units of an expected annual cost. Further complexity is introduced through modelling interdependencies between hazard events and the cascading risks between different infrastructure sectors.

In this session, we invite practitioners to present novel frameworks and methodologies for bridging the gap between climate science and actionable insights for decision makers in different domains.

Keywords: Risk assessment; Natural hazards; Infrastructure; Climate projections; Built environment

G4. Projections of regional climate change: from modelling to applications

There are a number of regional climate projections that have been produced in Australia using dynamical and/or statistical approaches (including machine learning), often contributing to the international Coordinated Regional Downscaling Experiment (CORDEX).This session calls for contributions addressing a wide range of scientific issues related to preparation and applications of regional climate change projections. Presentations are sought that relate to issues such as choice of emission scenarios, ensemble generation, model evaluation and skill testing, model output calibration and bias correction, statistical and dynamical downscaling methods, investigation of specific regional climate changes, application of regional climate projections to investigate specific climate change impacts on various human and natural systems, and the use of regional climate information in adaptation and decision making applications.

Keywords: Regional climate, CORDEX, Climate projections, Climate change impacts

G5. Recent Advances in Flood Inundation and Mapping

Flood Inundation Mapping (FIM) is pivotal in flood risk management, enabling the identification and prediction of flood-prone areas. This discipline includes various flood types, including riverine, flash, urban, and coastal floods, each with unique causes and impacts. The integration of Geographic Information Systems (GIS) and remote sensing has facilitated the creation of detailed hazard maps, essential for emergency planning and urban development. Moreover, the fusion of diverse datasets and the application of machine learning (ML) algorithms have improved the accuracy of flood prediction and inundation mapping. In the context of climate change, which intensifies rainfall patterns and elevates global flood risks, these innovations are increasingly critical. This session will highlight the latest developments in FIM, focusing on hydrological modelling, remote sensing applications, ML techniques, and impact assessments. Discussions will also cover the implementation and scalability of these methodologies in large-scale and operational settings. We invite presentations on topics including, but not limited to:

The use of remote sensing technologies in FIM;
Applications of machine learning techniques in FIM;
Hybrid approaches, data assimilation, integration and fusion techniques to advance MIP results;
Operational workflows to implement and automate FIM for near real-time applications.
Hydrological and hydrodynamic modelling innovations leading to better FIM;
Climate change impacts and implications in FIM.

Keywords: Flood; Inundation; Mapping; Remote Sensing; Machine Learning; Hydrological modelling

G6. Urbanization, Hydro-Meteorological Hazards, and Risk Mitigation

Urban expansion and changing land-use patterns are reshaping exposure to natural hazards. As cities grow, infrastructure and communities become increasingly vulnerable to extreme weather events including floods and heatwaves, highlighting the need for smarter urban planning, improved flood management, and sustainable development practices.
This session will explore how technological advancements, policy frameworks, and interdisciplinary collaboration can drive more effective risk reduction strategies. It will highlight the role of nature-based solutions, cutting-edge research, and innovative approaches to hazard assessment, risk modelling, and resilience-building. We invite articles that focus on (but are not limited to):

Trends in precipitation patterns, urban expansion, and increased exposure to hydro-meteorological hazards
Advancements in hydrological and heat hazard modelling for forecasting and risk mitigation
Modelling nature-based solutions and smart cities for flood and heat mitigation
Disaster risk reduction and resilience strategies for extreme weather events, including urban heat and flooding

Keywords: Urbanization; Hydro-Meteorological Hazards; Flood Risk; Heat Hazards; Extreme Weather Events; Climate Adaptation; Resilience; Nature-Based Solutions; Risk Mitigation; Smart Cities; Disaster Risk Reduction

E4. AI assessment of distributed energy resources and variable renewable energy systems

Ongoing advances in data driven methods from the Machine Learning (ML) and Artificial Intelligence (AI) domains are increasingly being used to effectively characterise variable renewable energy (VRE) sources and their interactions with the grid. The advent of distributed energy resources (DER) embedded within the grid, where traditional load-only sites are now making use of the combination of:

onsite rooftop solar panels,
onsite battery storage,
offsite VRE
external energy supply and storage aggregation providers
to actively adjust their electricity consumption based on grid conditions arising from energy supply, demand and time varying prices.

This sessions focus is on AI based methods to address the challenges of effectively characterising, monitoring, controlling and assessing VRE sources and more generally DER participants within the grid. Presentations are welcome on topics such as:

VRE resource assessment
VRE resource and DER monitoring and control
AI models for VER and DER participant anomalous behaviour detection and/or system health monitoring.

Keywords: Artificial intelligence; Machine learning; Distributed energy resources; Variable renewable energy; Systems integration

E3. Economic modelling for a sustainable energy transition

The transition to low emission energy sources presents complex challenges requiring comprehensive economic modelling frameworks that integrate engineering, finance, policy, and environmental considerations. This session explores analytical tools and frameworks for understanding the economic and financial stability of energy transitions while addressing societal equity and accessibility.

We will examine the role of technological advancements and innovation in facilitating the de-fossilisation of energy systems, alongside the policies necessary to support investment in low emission energy infrastructures. The government’s role in catalysing private sector engagement through emphasizing mechanisms to enhance energy efficiency, affordability, and sustainable growth.

Furthermore, the session will discuss demand-side modelling approaches, considering people’s affordability and the implications for whole-system planning methods. Special attention will be given to deep uncertainties and risks associated with the net-zero transition, and how novel modelling approaches can address gaps in existing economic toolkits.
We invite contributions on economic modelling tools that bridge renewables, non-renewables, and their ecological linkages, ensuring a holistic and resilient transition to a sustainable energy future.

Keywords: Energy Economics, Energy Transition, Economic modelling

E2. Decarbonisation of industrial processes

Decarbonisation of industrial processes is an increasingly urgent task that is faced by companies across Australia and the world. It requires consideration of variable renewable energy inputs and strategies for integrated these with industrial processes that have historically run very close to continuously with only a limited degree of variability. This session will showcase work where novel process configurations are examined via modelling and simulation, with anticipated applications in iron and steel, cement and alumina production, or any other emissions-intensive process, according to interest.

Keywords:

E1. Tools for the Energy Transition

The International Energy Agency has identified six phases of variable renewable energy (VRE) integration, namely VRE has no significant impact at the system level, VRE has a minor to moderate impact on the system, VRE determines the operation pattern of the power system, VRE meets almost all demand at times, Significant volumes of surplus VRE across the year, Secure electricity supply almost exclusively from VRE.

South Australia and Denmark are the only jurisdictions to reach Phase 5. South Australia has a goal to have 100 per cent net renewable electricity by 2027. South Australia meets all its electricity demand by VRE at times and sometimes even simply with rooftop solar. There are several issues to be addressed to aid the transition to fulfil that goal.

The focus of the session will be on electrical supply and demand. Thus, presentations are welcome on topics such as forecasting of VRE, characterising VRE droughts, impact of electric vehicles on supply and demand, energy efficiency measures in housing stock, load shifting and its impacts, microgrid design, managing minimum demand and system security, price responsive resources impacts, electrification- decarbonising the economy, other topics related to the transition.

Keywords: Forecasting of VRE; characterising VRE droughts; impact of electric vehicles; energy efficiency load shifting and its impacts; microgrid design; electricity demand

D6. Applications of new technologies or methods in economics and finance

This session invites papers or extended abstracts that apply new technologies or undertake novel empirical analysis to answer topical questions in economics and finance.

Keywords: Financial Markets; Corporate Finance; Innovation; Textual Analysis; Investments; Climate Finance; FinTech

D5. Decision-making under uncertainty in economics and finance

Decision-making under uncertainty in economics and finance is a critical area of study that explores how individuals, businesses, and governments make choices when outcomes are uncertain. This field examines the strategies and models used to navigate the complexities of unpredictable environments, where factors such as market volatility, economic and climate fluctuations, and unforeseen events can significantly impact decisions. This also includes simulation modelling and analysis.

Keywords: Economics; Finance; Decision; Uncertainty; Simulation

D4. International Trade, Supply Chains, and Global Markets

This session explores trade dynamics, supply chain resilience, and economic globalization. It examines trade policies, digital trade, and logistics optimization while analysing economic disruptions in global markets. The session aims to provide insights into the evolving nature of international trade, the impact of technological advancements, and strategies for enhancing supply chain efficiency and resilience.

Keywords: International Trade; Global Supply Chains; Trade Barriers and Tariffs; Geopolitical Risks; Technological Advancements in Trade and Logistics

D3. The Use of Modeling and Simulation in the Evaluation of Economic Policy Interventions

This session explores modelling and simulation methodologies for evaluating economic policy interventions across key
socioeconomic domains. As policymakers face pressure to design evidence-based interventions with limited resources,
computational approaches offer powerful tools for assessing policy impacts. The session showcases quantitative
frameworks that combine economic theory, empirical data, and computational methods to inform more effective policy
design.

Topics may include:
1. Gender Wage Gap Reduction Strategies

Machine learning approaches to identify heterogeneous effects of wage gap interventions
Counterfactual simulations for designing targeted policy mechanisms
Agent-based models of organizational policy changes and their impact on wage structures

2. Housing Supply and Affordability Interventions

Spatial equilibrium models to evaluate zoning reform impacts on housing markets
Dynamic models of supply responses to tax incentives and regulatory changes
Microsimulation models of the distributional effects of housing subsidies

3. Rental Market Policy Evaluation

Equilibrium models assessing the impact of rent control effects on market dynamics
Simulations comparing the effectiveness of different rental assistance programs
Causal inference methods for evaluating the effects of rental market regulations

3. Health Sector Reform Assessment

Models analysing healthcare utilization patterns under alternative insurance designs
Simulation for hospital capacity planning and resource allocation
Integrated models evaluating health policy impacts on both health outcomes and economic productivity

5. Education Policy Innovation

Models assessing the long-term earnings impacts of educational investments
Simulations evaluating equity and efficiency trade-offs in school choice policies
Human capital models measuring the effectiveness training program

Keywords: Policy; evaluation; intervention

D2. Modelling Risk Management, ESG, Climate Finance, Policy Evaluation, and Tourism Economics

This session covers both economic and financial aspects of risk management, sustainability strategies, and policy evaluation. It examines ESG (Environmental, Social, and Governance) integration, corporate sustainability, climate finance, and financial stability. Additionally, it includes economic research on environmental policies, regulatory frameworks, economic resilience, and risk assessment in public policy and development.

Keywords: Risk Management Modelling; Climate Finance and Sustainability; Environmental, Social, and Governance (ESG) Strategies; Food Security, Agriculture, and Public Policy; Tourism Economics and Hospitality Management; Carbon Markets, Emissions Trading, and Climate Policy

D1. Machine Learning Applications in Economics and Finance

This session explores the role of machine learning in economic forecasting, financial markets, and decision-making. It covers AI-driven modelling techniques, algorithmic trading strategies, and computational finance applications. The session aims to highlight the impact of machine learning on predictive analytics, investment strategies, and risk assessment in economics and finance.

Keywords: Algorithmic Trading; Computational Finance; Machine Learning; Econometrics; Risk Prediction

C7. Advancing Machine Learning in Hydrology: Ensuring Reliable, Reproducible, and Validated Applications

Over the past decade, machine learning has brought transformative advancements to hydrology, often surpassing traditional models in predictive accuracy and expanding the field’s analytical capabilities. However, as Buchanan (2024) highlights, solid approaches to verification and validation of these models remain a critical challenge, frequently neglected amid the rapid progress and enthusiasm surrounding their use. One major concern is the risk of overestimation of true model performance due to data leakage, which can occur when testing and calibration datasets are not properly separated. Given the strong temporal and spatial correlations in hydrological data, addressing this issue requires careful methodology and robust safeguards. This session aims to highlight examples of the potential of machine learning in hydrology which include or showcase rigorous verification and testing approaches ensuring reliable and unbiased outcomes.

M. Buchanan, Don’t flock to faulty ai fashion, Nature Physics 20 (8)
(2024) 1220–1220. doi:10.1038/s41567-024-02604-y

Keywords: AI; validation of models; model performance

C6. Emulation of Dynamic Models

Simulators used in scientific disciplines such as physical sciences, manufacturing, environmental modelling, and biology, are often computationally demanding. Model emulation, or surrogate modelling, is an approach that has gained widespread use in reducing the computational burden of important tasks such as sensitivity analyses, real-time process control, uncertainty quantification, and AI-assisted design. Model emulation can be particularly challenging for complex, dynamic models where the dimensionality of inputs and outputs can often be high. Papers are invited that either present model emulation approaches for dynamic models, or that demonstrate interesting applications of dynamic model emulation in solving important scientific problems.

Keywords: Model emulation; surrogate modelling; machine learning; computer simulation; dynamical model

C5. Systems for Supporting the Re-use of Data – The Backbone of Digital Transformation

Digital transformation depends on the accessibility of quality data. While analytical tools and modelling approaches have been progressing at astronomical speeds, now more than ever, the availability of well-curated, quality data is the limiting factor. To overcome this limitation, particularly where data requirements are diverse and span regions, organizations, and contexts, there are strong moves to build approaches for sharing and re-using data and models. This underpins impact by amplifying the value of datasets and through digital products that address specific issues. There are hundreds of well-functioning systems that already share data. However, many of these operate within single organizations and well-defined problem spaces. As the complexity of human, political, and business relationships increases, so does the potential societal impact—and so do the difficulties involved. In this session, we will hear various perspectives from collaborative efforts to build data-sharing systems to support modelling and analytics. By ‘system,’ we mean all aspects of governance and the IT platforms that support governance.

Keywords: Federated data

C4. Open and intelligent modeling and simulation

Modelling and simulation are significant methods in exploring dynamic phenomena and processes, analysing global/regional geohypotheses, and supporting decision/policy making. Due to the complexity of modelling issues, traditional modelling and simulation are generally constrained by the comprehensiveness of phenomena, closure of models, inefficient computation, and adaptive capability to dynamic changes. Recently, the exciting emerging and rapid developments in computer technologies, for example, AI, big data, cloud computing and, have provided an efficient and flexible way to aggregate various knowledge and resources dispersed around the world. These advances predictably will significantly promote modelling and simulation to the open and intelligent era, and change the relevant methods, techniques, and applications.
This session aims to explore the latest methodologies, technologies, and applications in the field of open and intelligent modelling and simulation. The focus will be on how these achievements (e.g., open modelling frameworks, model and data sharing strategies, artificial intelligence (AI), collaboration methods, and distributed computing techniques) are transforming traditional modelling and simulation practices, enabling more accurate, scalable, and efficient decision-making.

Keywords: Modelling frameworks; intelligent modelling; collaborative modelling; sharing and integration, distributed computing

C3. Digital twins and mixed reality

Digital twins are digital “copies” of physical objects, processes or interacting humans which semi-autonomously maintain their likeness through the application of cyber-physical systems. A significant topic in both scientific literature and real-world application over the last several years, digital twins are a critical component in industrial automation; designed to be combined with AI, optimisation and computational modelling technologies in order to decentralise decision making, improve forecasting and automate diagnosis, planning and control processes. Alongside recent improvements in sensing and cyber-physical technologies, mixed reality technologies are a significant driver behind the push to implement digital twins. Mixed reality technologies allow the outputs of digital twins to be visualised interactively alongside their physical counterpart within the operating environment.

A growing number of organisations are beginning to incorporate Digital Twins and Mixed Reality technology into their business operations, however, achieving their full potential may require large-scale integration of digital twins across organisations and domains. This conference session will explore applications of Digital Twin and Mixed Reality technologies, examining the state of adoption in Australian industry.

Keywords: Digital Twin, Computational Modelling, Simulation, Applications, Workflows, Workspace

C2. Modelling and simulation of Robotic and Autonomous Systems (RAS)

Robotic and Autonomous Systems (RAS) combine physical and virtual capabilities to perform real-world tasks as autonomous members of human-machine teams. The development and safe deployment of RAS is supported and accelerated by modelling and simulation (M&S), which can also validate system performance and build the trust required for effective operation alongside humans. This session will explore papers discussing the application of modelling and simulation to RAS development and deployment. Topics of interest include best practice methods and applications, linkage between simulations of varied fidelity and focus (i.e. Operational Analysis, digital twins, high-fidelity engineering simulations), applications of artificial intelligence and machine learning (including synthetic training data), test and evaluation, verification and validation (TEVV), and building trust in human-machine teams. All relevant topics and authors are welcome, but papers are particularly sought from the Defence RAS ecosystem (industry, academia and government).

Keywords: Robotics; Autonomous Systems; Modelling and Simulation; uncrewed systems; AI/ML; test and evaluation; verification and validation

C1. Using workflow platforms in modelling and simulation

Most scientific activities can be thought of as workflows which involve sequences of tasks or actions. These tasks often include obtaining input data, processing activities which are then followed by post-processing operations such as visualisation and analysis in support of decision making. Processing steps may involve use of modelling, simulation or analysis programs and may include the outputs of such models being dynamically fed back into the system. Post processing analysis may involve advanced techniques such as Machine Learning to provide insightful analysis which given the data volumes may not be obtainable via traditional methods.

Commercialisation of workflows as software applications or services is often not considered or is considered late in the development process which creates significant hurdles to maximising benefit. Very commonly workflows are created as bespoke combinations of software components linked by manual steps or tasks. The presence of such manual steps increases the cost of performing the activity and limits its reproducibility and its transfer to others (often leaving the IP stranded). They are often also conducted on disparate systems, limiting the possibly of automation and integration. Often workflows and the tasks involved are not well documented. This makes packaging capability for commercial use difficult and can limit the range of users.

The use of workflow engines allows scientific workflow to be systematized and automating leading to reduction in cost of creation and use, reducing dependence on specific people and skills, improving collaboration by fostering interoperability of software components and supports improved reliability, reproducibility and commercialisation.
This session provides a forum for researchers to explore the opportunities for improvement of their scientific workflow development, to discuss what is modelling and modelling software best practice and to exchange their experiences in using workflow concepts to improve their scientific research and its impact.

Keywords: Workflows, software best practice, Workspace, commercialization, stranded IP

B9. Advances in agent-based modelling and their statistical challenges in biological, ecological and agricultural systems

Agent-Based Modelling (ABM) has long been applied to the study of complex phenomena. It has become increasingly popular across many scientific fields including the study of biological, ecological and agricultural systems. Agent-Based models provide a rich environment for the experimentation and analysis of these systems at different levels of complexity by conceptually breaking them down into individual interacting components. However, the likelihoods, the functions that describe the probability of the observed data given parameter values, for these models are not analytically or computationally tractable. This makes statistical inference for these models challenging.

This session aims to highlight the advances in ABM approaches applied to the development of simulation systems in biology, ecology and agriculture. Statistical inferential methods for ABM will also be discussed. Submissions addressing the scientific challenges of modelling these systems using the ABM paradigm and novel approaches for dealing with statistical inference of these models are especially welcomed. Areas of interest, not necessarily exhaustive, include geospatial ABMs, hybrid models, model validation and verification, integration with commercial off-the-shelf or open source Geographical Information Systems, optimisation techniques, ABM-enhanced Decision Support Systems, mobile applications, likelihood-free or simulation-based inferential methods for ABMs such as Generalized Bayesian inference, Approximate Bayesian Computation, Bayesian indirect inference integration with machine learning (ML) and active learning techniques.

Keywords: Agent-Based Modelling, Individual-Based Modelling, Likelihood-free techniques, Simulation systems in biology, simulation-based inference, Optimisation techniques

B8. Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change

As climate change intensifies, agricultural systems are increasingly challenged by extreme weather events, soil degradation, and the urgent need for resilient crop production strategies. These issues threaten global food security and the livelihoods of millions of farmers. In response, innovative research and technologies have emerged in recent decades, focusing on advanced modelling and climate-smart practices for crop and soil management to enhance agricultural sustainability and productivity.

The session will delve into the transformative potential of innovative approaches in addressing climate-related agricultural challenges. It will highlight three key areas: (1) the critical role of integrated modelling by combining biophysical models and climate information, in predicting crop yields under climate change and extreme climate conditions, enabling farmers and policymakers to make informed decisions; (2) the development of techniques including machine learning that allow rapid, accurate, and non-destructive characterization of crop growth, to accelerate modern breeding process and assist in precision management; (3) the exploring of innovative soil management strategies and their regulatory mechanisms to improve soil health, optimize water and carbon dynamics, and support climate mitigation.

By integrating diverse innovation modelling techniques in agricultural production, this approach offers actionable insights to optimize soil management, improve crop resilience, and ensure sustainable agricultural practices in the face of a changing climate. The session aims to bridge the gap between scientific research and practical solutions, empowering stakeholders to build more resilient and productive agricultural systems.

Keywords: biophysical model; climate change; extreme climate; crop management; machine learning

B7. Optimising farming systems to improve climate resilience and sustainability under water-constraint environments

Climate change has amplified the frequency and severity of extreme weather events, including prolonged droughts and irregular rainfall patterns, leading to significant challenges for farming systems. Water scarcity has emerged as a critical threat to global food security, particularly in regions where agriculture heavily relies on finite freshwater resources. Sustainable agricultural practices and innovative farming system designs are urgently needed to enhance resilience, maintain productivity, and ensure long-term environmental sustainability under these water-constrained conditions.

In recent years, advancements in crop modelling, precision agriculture, soil and water management techniques, and climate-resilient cropping systems have offered new opportunities to mitigate the impact of water scarcity on agriculture. However, significant knowledge gaps remain in optimizing these strategies for various agro-ecological contexts and aligning them with broader goals of sustainability and climate resilience.

This session aims to:

Explore innovative farming system designs and practices that improve water-use efficiency, enhance crop productivity, and reduce environmental footprints under water-constrained environments.
Highlight advancements in modelling tools and data-driven approaches for assessing and improving the resilience of farming systems to water stress.
Examine the role of climate-smart agriculture, such as drought-tolerant crop varieties, reduced or no-tillage systems, and integrated soil-water-nutrient management practices, in addressing the challenges of water scarcity.
Discuss policy frameworks and strategies for scaling up sustainable practices and technologies to improve resilience and sustainability across diverse farming systems.
Facilitate knowledge sharing and collaboration among researchers, policymakers, and practitioners to develop practical solutions for climate-resilient and water-efficient agriculture.

Keywords: Climate change impact and mitigation; farming system; sustainable agriculture; water resource

B6. Integrating data and system knowledge to support decision-making: Advances, applications and lessons learned

In an era of rapid change and digitalisation, effective management of agricultural and ecological systems requires moving beyond traditional, siloed quantitative approaches. This has never been clearer as the gap between data and decisions continues to widen, despite more and more data becoming available. Bridging this gap demands robust decision support that integrates and translates diverse data streams and system knowledge into actionable information for decision-makers. Both data-driven and process-based approaches have unique yet complementary roles in achieving this. Data-driven methods, such as machine learning, provide empirical evidence and scalability, while process-based methods, which represent system knowledge in a mathematical framework, enable interpretation and generalizability.

This session will share innovative developments and applications of quantitative approaches that integrate data and system knowledge to improve decision-making for agricultural and ecological system management.

We welcome submissions that adopt (in no particular order): (i) process-based models in concert with parameter estimation, model evaluation (or ‘validation’), data assimilation, etc.; (ii) data-driven approaches such as reinforcement learning and physics-informed machine learning; and (iii) so-called hybrid approaches. Submissions should emphasize both methodological contributions and their relevance to decision support. Authors are encouraged to share lessons learned from the many challenges in trying to unify data and models (e.g. data-model scale discrepancies, measurement and model error) to inspire future advancements.

Keywords: Decision support; Value of information; Data-knowledge integration; Data-model conflict; Multi-method; Sustainability

B5. Agricultural Systems

Agricultural systems comprise a set of components and their interactions that enable the production of food, fibre, and energy from natural resources. Its study must be interdisciplinary and employ systems analysis, encompassing methodologies for diagnosis, assessment, and design of production systems, taking into consideration environmental, social, and economic aspects.

This session accepts papers on model and simulation studies that are based on mathematical and biological principles interfacing with specific applications to the agricultural industries. Applications vary from knowledge generation, exploration of specific issues, to the analysis of integrated systems and decision support tools.

The papers could encompass cropping, horticultural, livestock, fisheries, and forestry industries, either with a specific or broad focus. For example, a paper may tackle a specific problem within an agricultural system whereas a broad paper may describe and discuss a methodology used to improve the production and profitability of an entire agricultural industry.

Keywords: Agriculture, Biology, Environment, Productivity, Profitability, Decision Support Systems

B4. APSIM Developments: Livestock, pests and diseases, whole farm modelling, cloud computing, etc.

As a modelling platform APSIM is highly flexible. This allows developers to add on components that interact with the crop/vegetation and soil models already in the platform without having to (re)create capability that already exists. This session will concentrate on those new developments. We invite papers on APSIM developments that do not otherwise fit into the APSIM vegetation and soil sessions including papers that review existing capability to identify gaps and needs for future development. Session moderated by Dean Holzworth (CSIRO, Australia), Val Snow (AgResearch, New Zealand), Adam Liedloff (CSIRO, Australia)

Keywords: APSIM

B3. APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)

In APSIM the soil provides a central focus. Plants, seasons and managers come and go, finding the soil in one state and leaving it in another. This means that the various APSIM soil modules are included in every simulation, playing a critical role in determining the water, nutrient and temperature dynamics that affect so many of the other processes, including development and growth of plants and losses to the environment. In turn, the plants and environmental conditions affect the various soil processes. APSIM’s applications over the past 30+ years have been varied, resulting in the model evolving over time to meet new and changing needs. This applies to the soil modelling capability as well, where an increased focus on environmental impacts of farming, or its role in mitigating these, challenges the model in different ways. In this session of the APSIM Symposium we invite submissions that concern APSIM’s soil modelling capability. These submissions could be reviews that reflect on the existing capability, contributions showcasing current developments, or forward-looking presentations identifying future development needs. All soil processes modelled (or to be modelled) by APSIM are in scope. Soil modelling capability is considered in a wide sense, including, e.g., return flows from plants or better representation of the effects of environmental conditions on soil processes. The focus of the submissions should be on model capability, development or development needs. Submissions on model use and applications are invited in several other MODSIM sessions.

Keywords: APSIM; soil process modelling; nitrogen; nutrient cycling; water balance; agricultural systems modelling

B2. APSIM-Development: Advances in Plant and Vegetation Modelling

As part of the APSIM 30-Year Symposium, this session will explore recent advancements in modelling plant physiological processes and vegetation dynamics. Presentations will focus on new developments in simulating key physiological processes as well as their interactions with genetic traits and environmental drivers. By integrating genotype-by-environment interactions into APSIM, this session aims to highlight innovative approaches that enhance prediction capability and adaptation strategies under changing conditions. This session is moderated by Dr Karine Chenu, Dr Edith Khaembah and Dr Enli Wang.

Keywords: APSIM

B1. APSIM 30-Year Symposium: Shaping the Future of Agricultural Modelling

Celebrating 30 years of APSIM’s innovation and impact, this symposium session will comprise invited presentations bringing together leading experts to shape the future of APSIM’s development. As a globally leading agricultural systems model, APSIM has been instrumental in advancing the science of simulating crop growth, soil processes, and farm management strategies. This session will feature forward-looking presentations exploring the evolution of APSIM, with a focus on scientific advancements, modelling infrastructure, the integration of emerging technologies like AI, and new pathways for innovation. Discussions will address key challenges and opportunities to ensure APSIM continues to lead the way in agricultural research and decision-making.

Keywords: APSIM

A6. Industrial mathematical modelling and simulation

Much decision making in industry is based on rules-of-thumb, which are either validated via mathematical modelling or simulations. The Mathematics-in-Industry Study Group meetings, organised by Australian and New Zealand Industrial and Applied Mathematics, have played an important role in fostering stronger contacts between mathematicians and statisticians and industrial partners. The speakers will discuss some representative examples where the resulting solutions have had considerable subsequent impact. Even in industrial modelling and simulation, a key issue is the management of uncertainty, so that useful rules-of-thumb are identified even though the models on which they are based are only approximations of the reality of the processes being investigated. The management of uncertainty in industrial modelling and simulation is often achieved by identifying an appropriate simple model that encapsulates the answer to the question under consideration. Various examples will be discussed.

Keywords: Industrial modelling; Uncertainty; Decision making

A5. Advances In Partial Differential Equations: From Methods To Applications

This special session explores recent advances in the numerical and asymptotic solutions of partial differential equations across various applications, from fluid dynamics to stochastic processes. Topics include novel discretization techniques, high-performance computing implementations, fractional operators, and uncertainty quantification in solving equations such as Navier-Stokes and other complex systems. Contributions addressing analytic insights, algorithm development, model integration, and real-world applications in engineering, finance, and physics are especially welcome.

Keywords: Partial differential equations; fluid dynamics; Levy flights

A4. Computational statistics and data analysis

Complex data sets have recently become a norm rather than an exception with the influx of data being now available practically in all areas of our life. Analysis of large data sets and the advances in computing power have given an impetus to the rapid rise of computationally intensive statistical methods. The aim of this special session is to bring together users and researchers to present the latest developments in the field of computational statistics. The special session provides a forum for academics and practitioners to disseminate high-quality results related to different aspects of modern computational statistical methods and their applications in data analysis. Potential topics include, but are not limited to, Bayesian computing, resampling methods, statistical and machine learning, Monte Carlo methods, stochastic optimisation, and statistical modelling.

Keywords: Computational statistics; Data analysis; Monte Carlo methods; Machine learning

A3. Modelling and simulation for trustworthy data science on challenging datasets

Modern data science is an engine for decision-making across society, as well as for driving scientific advances across multiple domains and diverse datasets. However, this only highlights an increasing need for data-driven decisions and algorithms to be trustworthy and explainable: untrustworthy algorithms and models can produce erroneous outcomes and potential harms.

Modelling and simulation can enhance the trustworthiness of data-driven methods by allowing for the clear articulation of assumptions, frameworks for uncertainty quantification, and the ability to explore scenarios and provide counterfactual explanations. This session aims to bridge the gaps between data-driven methods, modelling and simulation and explore their complementarities.

Talks will bring together researchers from diverse backgrounds to explore the intersection of statistical data science, mathematical modelling, and simulation techniques in addressing complex challenges in data science. The focus will be on modelling and simulation methods that deal with challenging datasets exhibiting characteristics like high dimensionality, noise, and missing data. It will explore topics at the interface of modelling and data, such as Bayesian inference, data assimilation, and complex systems. Talks will feature application domains such as climate, social systems, online/offline behaviours, and government/industry applications.

Keywords: Data science; trustworthiness; explainability; Bayesian methods; data assimilation; statistical inference

A2. Advances In Numerical Solutions For Water Resources Models

With mounting pressure on scarce water resources, hydrological models including rainfall-runoff, routing, river system and hydrodynamic models are becoming more and more complex to incorporate a broad range of processes. These models are now required to simulate surface water dynamic, but also incorporate groundwater-surface water interactions, floodplain storage, vegetation growth, water extractions or ecological health indicators. At the same time, models are expected to run at short time steps (e.g. hourly), incorporate a broad range of climate scenarios and generate uncertainty estimates via Monte Carlo sampling. Achieving all these goals requires advanced numerical schemes that solve complex model equations while remaining fast and stable under extreme hydro-climate conditions. Numerical improvements benefit both classical models formulated as differential equations and machine learning models which poses significant challenges in terms of formulation and calibration.

In addition, despite its fundamental importance in hydrology, numerical analysis is often perceived as an obscure subject by many practitioners, which limits the adoption of efficient algorithms and reduces model performance. Consequently, methods and tools to facilitate user training are of utmost importance to accelerate adoption and ensure steady improvement of water resources simulations.

This session invites contributions showcasing the latest innovations in numerical approaches for water resources models. Topics of interest include, but are not limited to:

Software and techniques to accelerate hydrological and hydrodynamic model computations,
Acceleration of machine learning model computations,
Flexible modelling frameworks,
Analytical and Numerical approaches to solve differential equations,
Case study of numerical issues in complex hydrological modelling.

To promote methods facilitating training and adoption, session conveners will attribute a prize to the best presentation in terms of user training and adoption. Prize will be determined based on simplicity of the method and availability of accessible training material (e.g. spreadsheet version of the model).

Keywords: Numerical analysis, accelerated computing, parallelisation, numerical schemes, differential equations

A1. Applied Probability

The increasing availability of large and complex datasets across diverse fields, including finance, engineering, healthcare, biology, game theory and machine learning, has reinforced the central role of probability theory in modelling uncertainty and making data-driven decisions. Probabilistic methods are widely used in areas such as statistical inference, risk assessment, decision theory, and predictive modelling. Challenges persist in developing efficient computational techniques, handling noisy or incomplete data, and ensuring robust probabilistic reasoning in real-world applications. This session aims to provide a platform for researchers and practitioners to share advances in probability theory and its applications, particularly in machine learning and data science.

Keywords: Probabilistic Modelling; Game Theory; Queuing theory; Probability in Machine Learning and Data Science

M3. AI for Optimization: Methodologies and Applications

This session explores how AI can enhance the process of solving optimization problems. Optimization is fundamental to many machine learning models, and recent research has focused on leveraging machine learning to guide optimization algorithms or predict initial and optimal values for decision variables. We welcome presentations and papers on methodological advancements and applications of machine-learning-based optimization. Additionally, contributions on other data-driven optimization techniques, such as reinforcement learning, are highly encouraged and will be considered for this session.

Keywords:

G8. Modelling of bushfire dynamics, fire weather, impact and risk

This session will focus on the modelling of bushfire and bushfire-related phenomena. This includes modelling of: fire ignition, fire weather, fire spread and behaviour, dynamic fire propagation, extreme bushfire development, ember transport, fire-environment interactions, fire ecology and social systems affected by bushfire. We also encourage researchers to provide submissions focusing on the consequences of bushfire; impact and risk modelling as well as framework development. Submissions dealing with all aspects of catastrophic fires, such as have been experienced in various locations around the globe in recent years, are particularly encouraged.

Keywords: Bushfire; Fire weather; Fire behaviour; Bushfire risk