Author Index

* Please note this includes full papers only

A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z

L

• Lai, J.C.S.
  • CFD solver validations for simulating passively pitching tandem wings in hovering flight
    

    Huang, Q., Mazharmanesh, S., Tian, F.-B., Young, J., Lai, J.C.S. and Ravi, S.
    https://doi.org/10.36334/modsim.2021.A2.huang

• Laing, A.M.
  • Changing crop management improves farm level productivity and profitability for smallholder farmers in northern India
    

    Beletse, Y.G., Laing, A.M., Prestwidge, D.B., Gathala, M.K., Subash, N. and Liedloff, A.C.
    https://doi.org/10.36334/modsim.2021.B8.beletse

• Lall, U.
  • Trade strategies to mitigate the global impact of regional wheat production shocks
    

    Golding, P., Kapadia, S., Naylor, S., Schulz, J., Maier, H.R., Lall, U. and van der Velde, M.
    https://doi.org/10.36334/modsim.2021.B5.golding

• Lam, C.P.
  • The Open Maritime Traffic Analysis Dataset
    

    Masek, M., Lam, C.P., Rybicki, T., Snell, J., Wheat, D., Kelly, L., Glassborow, D. and Smith-Gander, C.
    https://doi.org/10.36334/modsim.2021.M9.masek

  • An evolutionary approach to balancing and disrupting real-time strategy games
    

    Snell, J., Masek, M. and Lam, C.P.
    https://doi.org/10.36334/modsim.2021.M8.snell

• Laugesen, R.
  • Improving sub-seasonal streamflow forecasts across flow regimes
    

    McInerney, D., Thyer, M., Kavetski, D., Laugesen, R., Woldemeskel, F., Tuteja, N. and Kuczera, G.
    https://doi.org/10.36334/modsim.2021.K5.mcinerney

  • Advances in subseasonal streamflow forecasting: An overview
    

    Thyer, M., McInerney, D., Kavetski, D., Laugesen, R., Woldemeskel, F., Tuteja, N. and Kuczera, G.
    https://doi.org/10.36334/modsim.2021.K5.thyer

• Lawrence-Wood, E.
  • The role of embodied cognition in linguistic mental health analysis
    

    Kernot, D., Van Hooff, M., Lawrence-Wood, E., Glenny, L., Snowden, C., Green, K. and Son, C.
    https://doi.org/10.36334/modsim.2021.H2.kernot

• Lawson, K.
  • Prototyping, developing, and iterating a gamified survey to evaluate participatory systems modelling for youth mental health: Quality assurance pilot
    

    Lee, G.Y., Hickie, I.B., Occhipinti, J., Song, Y.J.C., Huntley, S., Skinner, A., Lawson, K., Hockey, S.J. and Freebairn, L.
    https://doi.org/10.36334/modsim.2021.H2.lee

• Lee, G.Y.
  • Prototyping, developing, and iterating a gamified survey to evaluate participatory systems modelling for youth mental health: Quality assurance pilot
    

    Lee, G.Y., Hickie, I.B., Occhipinti, J., Song, Y.J.C., Huntley, S., Skinner, A., Lawson, K., Hockey, S.J. and Freebairn, L.
    https://doi.org/10.36334/modsim.2021.H2.lee

• Lei, Y.
  • Prior versus data: A new Bayesian method for fishery stock assessment
    

    Lei, Y., Zhou, S. and Ye, N.
    https://doi.org/10.36334/modsim.2021.A1.lei

• Leonard, R.
  • A framework for engaging stakeholders in solving real-world water resources management problems
    

    Wu, W., Maier, H.R., Dandy, G.C., Leonard, R., Bellette, K., Cuddy, S.M. and Maheepala, S.
    https://doi.org/10.36334/modsim.2021.F3.wu

• Lerat, J.
  • Estimation of road closure risks along the Bruce highway using the AWRA-L water balance model
    

    Vogel, E., Lerat, J., Gericke, L.A., Russell, C.A., Preece, A., Stuart, G., Pipunic, R., Khan, Z. and Donnelly, C.
    https://doi.org/10.36334/modsim.2021.K5.vogel

  • Enhancement of lightning strike forecasts using a machine learning approach
    

    Gow, D., Lerat, J., Warren, R. and Blockley, I.
    https://doi.org/10.36334/modsim.2021.K7.gow

  • A method to calibrate daily rainfall-runoff models to monthly streamflow data
    

    Lerat, J., Kavetski, D., McInerney, D. and Thyer, M.
    https://doi.org/10.36334/modsim.2021.J4.lerat

• Lett, R.
  • Mapping additional streamflow decline due to shifts in catchment response during the Millennium Drought
    

    Saft, M., Peel, M., Jordan, P., Shepherd, D., Steendam, G., Lett, R. and Peterson, T.
    https://doi.org/10.36334/modsim.2021.K11.saft

• Liedloff, A.C.
  • Changing crop management improves farm level productivity and profitability for smallholder farmers in northern India
    

    Beletse, Y.G., Laing, A.M., Prestwidge, D.B., Gathala, M.K., Subash, N. and Liedloff, A.C.
    https://doi.org/10.36334/modsim.2021.B8.beletse

• Liu, Y.L.
  • Temporal convolutional network algorithm for streamflow predictions in a subtropical river
    

    Liu, Y.L., Deng, C. and Zhang, H.
    https://doi.org/10.36334/modsim.2021.J13.liu

• Liu, Z.
  • A large-scale multi-rider matching problem with reneging passengers: single source case
    

    Fu, J., Zhang, L., Huang, K. and Liu, Z.
    https://doi.org/10.36334/modsim.2021.M3.fu

• Lo, E.H.S.
  • Command Professional Edition: Initial insights of a commercial military operational modelling and simulation tool
    

    Hagley, S., Lo, E.H.S., Hoek, P.J., Donohoo, A. and Peacock, J.
    https://doi.org/10.36334/modsim.2021.M7.hagley

• London, G.
  • The local influence of residential infill on the urban water flows: a multi-city analysis
    

    Moravej, M., Kenway, S., Renouf, M., Urich, C., Sainsbury, O., Bertram, N. and London, G.
    https://doi.org/10.36334/modsim.2021.J4.moravej

• Loo, P.S.
  • Impacts of localizing food system on farmers’ livelihood resilience
    

    Loo, P.S., Önal, F., Bax, K., Smairy, M. and Surovets, V.
    https://doi.org/10.36334/modsim.2021.B5.loo

• Lu, J.
  • A sensitivity analysis of a water quality model for a constructed wetland
    

    Roberts, M.E., Lu, J., Kavehei, E. and Adame, M.F.
    https://doi.org/10.36334/modsim.2021.L2.roberts

• Lynar, T.
  • Data preprocessing for non-image data: using convolution neural networks for network intrusion detection systems
    

    Glukhov, G. and Lynar, T.
    https://doi.org/10.36334/modsim.2021.M3.glukhov

M

• Ma, L.
  • A change-point test for autoregressive processes using a harmonic mean P-value
    

    Ma, L. and Sofronov, G.
    https://doi.org/10.36334/modsim.2021.A1.ma

• Maheepala, S.
  • A framework for engaging stakeholders in solving real-world water resources management problems
    

    Wu, W., Maier, H.R., Dandy, G.C., Leonard, R., Bellette, K., Cuddy, S.M. and Maheepala, S.
    https://doi.org/10.36334/modsim.2021.F3.wu

• Maheswaran, M.S.
  • Source: a water resource modelling system for supply augmentation planning
    

    Raut, A.K., Neupane, R., Kibria, G. and Maheswaran, M.S.
    https://doi.org/10.36334/modsim.2021.J3.raut

• Maier, H.R.
  • Trade strategies to mitigate the global impact of regional wheat production shocks
    

    Golding, P., Kapadia, S., Naylor, S., Schulz, J., Maier, H.R., Lall, U. and van der Velde, M.
    https://doi.org/10.36334/modsim.2021.B5.golding

  • A framework for engaging stakeholders in solving real-world water resources management problems
    

    Wu, W., Maier, H.R., Dandy, G.C., Leonard, R., Bellette, K., Cuddy, S.M. and Maheepala, S.
    https://doi.org/10.36334/modsim.2021.F3.wu

  • Modelling future changes in social vulnerability and bushfire risk
    

    Moore, E., Zhar, Y., Radford, P., O'Callaghan, M., Maier, H.R., Riddell, G., van Delden, H. and Wouters, M.
    https://doi.org/10.36334/modsim.2021.G1.moore

  • An assessment framework for classifying determinants of household water consumption and their priorities for research and practice
    

    Cominola, A., Preiss, L., Thyer, M., Maier, H.R., Stewart, R. and Castelletti, A.
    https://doi.org/10.36334/modsim.2021.J3.cominola

  • Uncertainty, sensitivity and scenario analysis: how do they fit together?
    

    Maier, H.R., Guillaume, J.H.A., McPhail, C., Westra, S., Kwakkel, J.H., Razavi, S., van Delden, H., Thyer, M.A., Culley, S.A. and Jakeman, A.J.
    https://doi.org/10.36334/modsim.2021.J5.maier

  • Water security analysis of the Middle River supply system: a foreSIGHT application
    

    Culley, S., Westra, S., Bennett, B., Maier, H.R., Newman, J., Van Der Linden, L. and Irvine, M.
    https://doi.org/10.36334/modsim.2021.J5.culley

  • Modelling hydrological change due to wildfires
    

    Partington, D., Thyer, M., Shanafield, M., McInerney, D., Westra, S., Maier, H.R., Simmons, C.T., Croke, B.F.W., Jakeman, A.J., Gupta, H.V. and Kavetski, D.
    https://doi.org/10.36334/modsim.2021.J8.partington

  • Beyond validation: assessing the legitimacy of artificial neural network models
    

    Humphrey, G.B., Maier, H.R., Wu, W., Mount, N.J., Dandy, G.C. and Dawson, C.W.
    https://doi.org/10.36334/modsim.2021.A3.humphrey

• Mak-Hau, V.
  • The Standard Refuelling Facility Expansion Problem: A case study in Australia
    

    Calle, J., Perez-Franco, R., Mak-Hau, V., Abril, D. and Rojas, D.
    https://doi.org/10.36334/modsim.2021.M1.calle

  • A regret-based incremental elicitation for multi-criteria force design
    

    Mak-Hau, V., Cao, T. and Yearwood, J.
    https://doi.org/10.36334/modsim.2021.M8.mak-hau

  • Hydrogen bus route planning in regional Victoria
    

    Esmaeilbeigi, R., Mak-Hau, V., Pineda-Villavicencio, G. and Ugon, J.
    https://doi.org/10.36334/modsim.2021.M1.esmaeilbeigi

• Malone, B.
  • Towards a provenance-enabled, reproducible, and extensible machine learning platform by integrating databases, web services, containers, and code repositories in a loosely coupled manner
    

    Singh, R.M., Wilson, P., Gregory, L., Seaton, S. and Malone, B.
    https://doi.org/10.36334/modsim.2021.C4.singh

• Mancilla-Barría, E.A.
  • Algorithms for diffusion and convection models implemented using open source libraries
    

    Mas-Hernandez, E., Mancilla-Barría, E.A., de la Torre-Sanzana, M. and Berres, S.
    https://doi.org/10.36334/modsim.2021.C3.mas-hernandez

• Marquez, L.
  • Developing an Adaptive Evacuation Simulation Framework based on Agent Performance Dynamics
    

    Rollan, T.A., Marquez, L. and Chua, C.
    https://doi.org/10.36334/modsim.2021.G1.rollan

• Masek, M.
  • The Open Maritime Traffic Analysis Dataset
    

    Masek, M., Lam, C.P., Rybicki, T., Snell, J., Wheat, D., Kelly, L., Glassborow, D. and Smith-Gander, C.
    https://doi.org/10.36334/modsim.2021.M9.masek

  • An evolutionary approach to balancing and disrupting real-time strategy games
    

    Snell, J., Masek, M. and Lam, C.P.
    https://doi.org/10.36334/modsim.2021.M8.snell

• Mas-Hernandez, E.
  • Algorithms for diffusion and convection models implemented using open source libraries
    

    Mas-Hernandez, E., Mancilla-Barría, E.A., de la Torre-Sanzana, M. and Berres, S.
    https://doi.org/10.36334/modsim.2021.C3.mas-hernandez

• Masoudian, S.
  • Stochastic modelling of wind and its implication for wildfire spread predictions
    

    Masoudian, S., Sharples, J.J., Jovanoski, Z., Towers, I. and Watt, S.
    https://doi.org/10.36334/modsim.2021.G3.masoudian

• Mazharmanesh, S.
  • CFD solver validations for simulating passively pitching tandem wings in hovering flight
    

    Huang, Q., Mazharmanesh, S., Tian, F.-B., Young, J., Lai, J.C.S. and Ravi, S.
    https://doi.org/10.36334/modsim.2021.A2.huang

• McDonough, K.
  • Assessing waterbird breeding conditions using an emulator of wetland inundation regime
    

    Quijano, J., Rodriguez, J., Kuczera, G., Saco, P.M., Sandi, S.G., Carlier, R., Wen, L. and McDonough, K.
    https://doi.org/10.36334/modsim.2021.J1.quijano

• McInerney, D.
  • Recent learnings towards achieving high quality probabilistic predictions in practical applications of hydrological models
    

    Kavetski, D., McInerney, D., Hunter, J. and Thyer, M.
    https://doi.org/10.36334/modsim.2021.J4.kavetski

  • Modelling hydrological change due to wildfires
    

    Partington, D., Thyer, M., Shanafield, M., McInerney, D., Westra, S., Maier, H.R., Simmons, C.T., Croke, B.F.W., Jakeman, A.J., Gupta, H.V. and Kavetski, D.
    https://doi.org/10.36334/modsim.2021.J8.partington

  • Improving sub-seasonal streamflow forecasts across flow regimes
    

    McInerney, D., Thyer, M., Kavetski, D., Laugesen, R., Woldemeskel, F., Tuteja, N. and Kuczera, G.
    https://doi.org/10.36334/modsim.2021.K5.mcinerney

  • Advances in subseasonal streamflow forecasting: An overview
    

    Thyer, M., McInerney, D., Kavetski, D., Laugesen, R., Woldemeskel, F., Tuteja, N. and Kuczera, G.
    https://doi.org/10.36334/modsim.2021.K5.thyer

  • A method to calibrate daily rainfall-runoff models to monthly streamflow data
    

    Lerat, J., Kavetski, D., McInerney, D. and Thyer, M.
    https://doi.org/10.36334/modsim.2021.J4.lerat

• McKeon, G.
  • Disentangling the effects of management and climate on perennial grass pastures and the degradation that follows multi-year droughts
    

    Owens, J., McKeon, G., O’Reagain, P., Carter, J., Fraser, G., Nelson, B. and Scanlan, J.
    https://doi.org/10.36334/modsim.2021.B1.owens

• McPhail, C.
  • Uncertainty, sensitivity and scenario analysis: how do they fit together?
    

    Maier, H.R., Guillaume, J.H.A., McPhail, C., Westra, S., Kwakkel, J.H., Razavi, S., van Delden, H., Thyer, M.A., Culley, S.A. and Jakeman, A.J.
    https://doi.org/10.36334/modsim.2021.J5.maier

• McRae, R.H.D.
  • Underpinning a new approach to predicting blow-up fire events
    

    McRae, R.H.D.
    https://doi.org/10.36334/modsim.2021.G3.mcrae

  • Assessing the potential for pyrocumulonimbus occurrence using simple fire weather indices
    

    Sharples, J.J. and McRae, R.H.D.
    https://doi.org/10.36334/modsim.2021.G3.sharples

  • Modelling terrain for wildfire purposes
    

    McRae, R.H.D.
    https://doi.org/10.36334/modsim.2021.G3.mcrae2

• Mellen, P.R.
  • OneWorld: Evolving an architecture for complex system representation in engagement simulation
    

    Armstrong, R.D., Christie, M.J. and Mellen, P.R.
    https://doi.org/10.36334/modsim.2021.M7.armstrong

• Mendes, A.
  • Freight train scheduling via decentralised multi-agent deep reinforcement learning
    

    Bretas, A.M.C., Mendes, A., Chalup, S., Jackson, M., Clement, R. and Sanhueza, C.
    https://doi.org/10.36334/modsim.2021.M1.bretas

• Meyer, R.S.
  • Using milk tanker pickup and weather data to quantify the impacts of heat stress on milk production in Australia
    

    Meyer, R.S., Graham, A-M., Hepworth, G. and Eckard, R.J.
    https://doi.org/10.36334/modsim.2021.B5.meyer

• Mhedhbi, R.
  • Data-driven approaches to rainfall nowcasting for application in hydrological modelling
    

    Mhedhbi, R. and Erechtchoukova, M.G.
    https://doi.org/10.36334/modsim.2021.F5.mhedhbi

• Miller, W.
  • A strategy for using cooperating missiles for missile defence
    

    Kapsis, M., Pudney, P., Aarao, J., Miller, W. and Freebairn, G.
    https://doi.org/10.36334/modsim.2021.M7.kapsis

• Mitchell, J.L.
  • Comet: A naval Combat Management System model for combat analysis
    

    Blaess, S.G., Christie, M.J. and Mitchell, J.L.
    https://doi.org/10.36334/modsim.2021.M7.blaess

• Moinuddin, K.A.
  • Application of neural networks to rate of spread estimation in shrublands
    

    Wadhwani, R., Sutherland, D., Moinuddin, K.A. and Sharples, J.J.
    https://doi.org/10.36334/modsim.2021.G3.wadhwani2

  • Improvement of drag model for non-burning firebrand transport in Fire Dynamics Simulator
    

    Wadhwani, R., Sutherland, D., Thorpe, G. and Moinuddin, K.A.
    https://doi.org/10.36334/modsim.2021.G3.wadhwani

• Moore, E.
  • Modelling future changes in social vulnerability and bushfire risk
    

    Moore, E., Zhar, Y., Radford, P., O'Callaghan, M., Maier, H.R., Riddell, G., van Delden, H. and Wouters, M.
    https://doi.org/10.36334/modsim.2021.G1.moore

• Moran, B.
  • Measures to analyse sustainability in military workforce structures
    

    Pike, C. and Moran, B.
    https://doi.org/10.36334/modsim.2021.M9.pike

• Moravej, M.
  • The local influence of residential infill on the urban water flows: a multi-city analysis
    

    Moravej, M., Kenway, S., Renouf, M., Urich, C., Sainsbury, O., Bertram, N. and London, G.
    https://doi.org/10.36334/modsim.2021.J4.moravej

• Morris, B.
  • Enabling integrated modelling systems for environmental prediction at national scale
    

    Specht, A., Guru, S.M. and Morris, B.
    https://doi.org/10.36334/modsim.2021.F3.specht

• Mortimer, K.
  • Simulation and data analytics for a Defence workforce transition
    

    Mortimer, K., Nguyen, V., Caelli, T. and Hill, B.
    https://doi.org/10.36334/modsim.2021.A3.mortimer

• Mount, N.J.
  • Beyond validation: assessing the legitimacy of artificial neural network models
    

    Humphrey, G.B., Maier, H.R., Wu, W., Mount, N.J., Dandy, G.C. and Dawson, C.W.
    https://doi.org/10.36334/modsim.2021.A3.humphrey

• Muhammad, R.
  • Model-based machine learning to explore the nexus between COVID-19 and environmental factors in the United States
    

    Munir, T., Hudson, I.L., Cheema, S.A., Muhammad, R., Shafqat, M. and Kifayat, T.
    https://doi.org/10.36334/modsim.2021.H1.munir

• Munir, T.
  • Model-based machine learning to explore the nexus between COVID-19 and environmental factors in the United States
    

    Munir, T., Hudson, I.L., Cheema, S.A., Muhammad, R., Shafqat, M. and Kifayat, T.
    https://doi.org/10.36334/modsim.2021.H1.munir

• Muniyappagounder Subramanian, C.
  • A layered meta-data approach to the design of joint experimentation data warehouse
    

    Jiang, L., Boyle, M., Johns, K., Muniyappagounder Subramanian, C. and Bista, S.
    https://doi.org/10.36334/modsim.2021.M8.jiang

• Muriel, J.E.
  • Assessing last mile delivery strategies – A hybrid solution approach
    

    Muriel, J.E., Zhang, L., Fransoo, J.C. and Perez-Franco, R.
    https://doi.org/10.36334/modsim.2021.M1.muriel