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

H

• Hagley, 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

• Hai, F.
  • Modelling of a five reactor Activated Sludge cascade process using ASM #1
    

    Watt, S., Nelson, M., Hai, F. and Sidhu, H.
    https://doi.org/10.36334/modsim.2021.L2.watt

• Hamilton, D.P.
  • Assessing performance of the MERGE model for simulating gully interventions
    

    Prentice, M.J., Waud, M.W., Hamilton, D.P. and Roberts, M.E.
    https://doi.org/10.36334/modsim.2021.L4.prentice

• Hamza, M.
  • Forecasting sales with Bayesian networks: a case study of a supermarket product in the presence of promotions
    

    Hamza, M., Abolghasemi, M. and Alvandi, A.O.
    https://doi.org/10.36334/modsim.2021.M9.hamza

• Harrison, S.M.
  • Mechanical digital twinning of the human body in the workplace for reduced injury risk and improved health
    

    Harrison, S.M. and Cohen, R.C.Z.
    https://doi.org/10.36334/modsim.2021.C1.harrison

  • Ergomechanic: A markerless motion capture and ergonomic assessment tool developed using the Workspace workflow engine
    

    Cohen, R.C.Z. and Harrison, S.M.
    https://doi.org/10.36334/modsim.2021.C2.cohen

  • Using Workspace to implement Digital Twins in the Mixed Reality Lab
    

    Watkins, D., Bolger, M., Hetherton, L., Zhang, Z., Xie, P., Harrison, S.M. and Cohen, R.C.Z.
    https://doi.org/10.36334/modsim.2021.C1.watkins

• Hautphenne, S.
  • Simulating population-size-dependent birth-and-death processes using CUDA and piecewise approximations
    

    Hautphenne, S. and Patch, B.
    https://doi.org/10.36334/modsim.2021.A1.hautphenne

• Hepworth, G.
  • 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

• Hetherton, L.
  • Using Workspace to implement Digital Twins in the Mixed Reality Lab
    

    Watkins, D., Bolger, M., Hetherton, L., Zhang, Z., Xie, P., Harrison, S.M. and Cohen, R.C.Z.
    https://doi.org/10.36334/modsim.2021.C1.watkins

• Hickie, I.B.
  • 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

• Hill, B.
  • 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

• Hilton, J.E.
  • A water suppression model for wildfires
    

    Hilton, J.E., Sullivan, A., Plucinski, M. and Swedosh, W.
    https://doi.org/10.36334/modsim.2021.G3.hilton

  • Incorporating directionality in the modelling of firebreaks using a two-dimensional dynamic fire spread simulator
    

    Swedosh, W. and Hilton, J.E.
    https://doi.org/10.36334/modsim.2021.G3.swedosh

• Hipsey, M.R.
  • Modelling methane emissions from lakes and reservoirs: case study of Lake Kinneret
    

    Kurucz, K., Hipsey, M.R. and Huang, P.
    https://doi.org/10.36334/modsim.2021.L3.kurucz

• Hock, K.
  • Operational effectiveness of future land combat teams: model conceptualisation
    

    Richmond, M.K., Gary, M.S., Cao, T., Blumson, D., Kosowski, L., Hock, K., Elsawah, S. and Kempt, N.
    https://doi.org/10.36334/modsim.2021.M4.richmond

• Hockey, S.J.
  • 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

• Hoek, P.J.
  • 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

• Hossain, M.M.
  • A comparative study on 10 and 30-year simulation of CMIP5 decadal hindcast precipitation at catchment level
    

    Hossain, M.M., Garg, N., Anwar, A.H.M.F., Prakash, M. and Bari, M.
    https://doi.org/10.36334/modsim.2021.K5.hossain

• Huang, K.
  • 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

• Huang, P.
  • Modelling methane emissions from lakes and reservoirs: case study of Lake Kinneret
    

    Kurucz, K., Hipsey, M.R. and Huang, P.
    https://doi.org/10.36334/modsim.2021.L3.kurucz

• Huang, Q.
  • 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

• Hudson, I.L.
  • 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

  • Use of auxiliary information in estimation of the finite population mean: An exponential type of estimator
    

    Ullah, K., Hudson, I.L., Cheema, S.A., Khan, A., Rahman, A.R. and Hussian, Z.
    https://doi.org/10.36334/modsim.2021.A1.ullah

• Humphrey, G.B.
  • 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

• Humphries, R.
  • AeroDAQ: A Workspace based application
    

    Sankaranarayanan, N. and Humphries, R.
    https://doi.org/10.36334/modsim.2021.C2.sankaranarayanan

• Hunter, J.
  • 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

• Huntley, S.
  • 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

• Hussian, Z.
  • Use of auxiliary information in estimation of the finite population mean: An exponential type of estimator
    

    Ullah, K., Hudson, I.L., Cheema, S.A., Khan, A., Rahman, A.R. and Hussian, Z.
    https://doi.org/10.36334/modsim.2021.A1.ullah

I

• Irvine, M.
  • 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

• Islam, K.M.N.
  • An inclusive city water account by integrating multiple data sources for South-East Queensland (SEQ), Australia
    

    Islam, K.M.N., Kenway, S.J., Renouf, M.A. and Wiedmann, T.
    https://doi.org/10.36334/modsim.2021.J7.islam

J

• Jackson, M.
  • 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

• Jacomazzi, M.A.
  • Evaluation of the time of concentration estimation methods for small rural watersheds
    

    Rezende, B.N., Boulomytis, V.T.G., Duarte, S.N., Jacomazzi, M.A. and Zuffo, A.C.
    https://doi.org/10.36334/modsim.2021.J7.rezende

• Jagdish, N.
  • The Lobster Game: Experiential learning of system dynamics through serious gaming
    

    Richards, R.G. and Jagdish, N.
    https://doi.org/10.36334/modsim.2021.I8.richards

• Jakeman, A.J.
  • 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

  • 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

• Jayasinghe, J.M.P.
  • Preliminary results of parameterisation of DairyMod pasture model for tropical pasture; Brachiaria
    

    Jayasinghe, J.M.P., Donaghy, D.J., Pembleton, K.G., Barber, D.G. and Ramilan, T.
    https://doi.org/10.36334/modsim.2021.B1.jayasinghe

• Jeffrey, S.J.
  • Improving catchment runoff estimates by ameliorating the impacts of SILO pluviometer data discontinuities through expanding the SILO reference database
    

    Stewart, L.K. and Jeffrey, S.J.
    https://doi.org/10.36334/modsim.2021.J13.stewart

• Jiang, L.
  • 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

  • An instance space analysis of combat simulations to understand the impact of force and information advantage on survival ratios
    

    Smith-Miles, K., Bista, S. and Jiang, L.
    https://doi.org/10.36334/modsim.2021.M7.smith-miles

• Johns, K.
  • 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

  • An evaluation model and tool for Defence capability options
    

    Nguyen, M.-T., Coutts, A., Knoll, J., Zuparic, M., Peacock, J., Bilusich, D., Galister, M. and Johns, K.
    https://doi.org/10.36334/modsim.2021.M8.nguyen

• Jordan, P.
  • 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

• Jovanoski, Z.
  • Application of a delayed logistic equation to a reindeer population in a closed environment
    

    Watt, S., Jovanoski, Z., Towers, I., Saifuddin, R. and Sidhu, H.
    https://doi.org/10.36334/modsim.2021.F3.watt

  • 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

  • The population dynamics of ecosystem engineers and habitat modification
    

    Watt, S., Jovanoski, Z., Towers, I., Saifuddin, R. and Sidhu, H.
    https://doi.org/10.36334/modsim.2021.F3.watt2

• Ju, J.
  • MOOR: Model-based Offline Reinforcement learning for sustainable fishery management
    

    Ju, J., Kurniawati, H., Kroese, D. and Ye, N.
    https://doi.org/10.36334/modsim.2021.M2.ju

K

• Kahagalage, S.
  • The closed loop military maintenance workforce: A simulation-based optimisation approach
    

    Withers, K.R., Turan, H.H., Kahagalage, S. and Elsawah, S.
    https://doi.org/10.36334/modsim.2021.M2.withers

• Kanthilanka, H.
  • Economically optimum N fertiliser decisions for rice cultivation in Sri Lanka: Does soil type matter?
    

    Kanthilanka, H., Farquharson, R.J., Ramilan, T. and Weerahewa, J.
    https://doi.org/10.36334/modsim.2021.B5.kanthilanka

• Kapadia, S.
  • 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

• Kapsis, M.
  • 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

• Karunaratne, S.B.
  • A nationally scalable approach to simulating soil organic carbon in agricultural landscapes
    

    Coelli, K.M., Karunaratne, S.B., Baldock, J.A., Ugbaje, S., Buzacott, A.J.V., Filippi, P., Cattle, S. and Bishop, T.F.A.
    https://doi.org/10.36334/modsim.2021.B6.coelli

• Kavehei, E.
  • 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

• Kavetski, D.
  • 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

  • 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

  • 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

• Kelly, L.
  • 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

• Kempt, N.
  • Operational effectiveness of future land combat teams: model conceptualisation
    

    Richmond, M.K., Gary, M.S., Cao, T., Blumson, D., Kosowski, L., Hock, K., Elsawah, S. and Kempt, N.
    https://doi.org/10.36334/modsim.2021.M4.richmond

• Kenway, S.J.
  • An inclusive city water account by integrating multiple data sources for South-East Queensland (SEQ), Australia
    

    Islam, K.M.N., Kenway, S.J., Renouf, M.A. and Wiedmann, T.
    https://doi.org/10.36334/modsim.2021.J7.islam

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

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

• Kernot, D.
  • 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

• Khaiter, P.
  • An ML-based study of extreme weather events incorporating seasonality factor
    

    Khaiter, P. and Suleman, M.
    https://doi.org/10.36334/modsim.2021.F5.khaiter

• Khan, A.
  • Use of auxiliary information in estimation of the finite population mean: An exponential type of estimator
    

    Ullah, K., Hudson, I.L., Cheema, S.A., Khan, A., Rahman, A.R. and Hussian, Z.
    https://doi.org/10.36334/modsim.2021.A1.ullah

• Khan, R.N.
  • A discrete event simulation for modelling Aljamarat Bridge rituals and evacuation scenarios
    

    Owaidah, A.A., Olaru, D., Bennamoun, M., Sohel, F. and Khan, R.N.
    https://doi.org/10.36334/modsim.2021.I8.owaidah

• Khan, Z.
  • 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

• Kibria, G.
  • 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

• Kiem, A.S.
  • Robust detection of statistically significant correlations in geophysical timeseries: A Monte Carlo method accounting for serial dependence and sampling uncertainty
    

    Armstrong, M.S., Kiem, A.S. and Kuczera, G.
    https://doi.org/10.36334/modsim.2021.A1.armstrong

• Kifayat, 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

• Kiparoglou, S.
  • Integrating and automating a closed loop simulation capability to enable easier exploration of the parameter space
    

    Kiparoglou, S. and Coppola, D.
    https://doi.org/10.36334/modsim.2021.M7.kiparoglou

• Knoll, J.
  • Using causal models to evaluate Force Generation options
    

    Shine, D.R., Knoll, J. and Cao, T.
    https://doi.org/10.36334/modsim.2021.M8.shine

  • An evaluation model and tool for Defence capability options
    

    Nguyen, M.-T., Coutts, A., Knoll, J., Zuparic, M., Peacock, J., Bilusich, D., Galister, M. and Johns, K.
    https://doi.org/10.36334/modsim.2021.M8.nguyen

• Kohli, A.K.
  • Application of topic modelling to Integrated Water Resource Assessment domain
    

    Erechtchoukova, M.G. and Kohli, A.K.
    https://doi.org/10.36334/modsim.2021.F5.erechtchoukova

• Kosowski, L.
  • Operational effectiveness of future land combat teams: model conceptualisation
    

    Richmond, M.K., Gary, M.S., Cao, T., Blumson, D., Kosowski, L., Hock, K., Elsawah, S. and Kempt, N.
    https://doi.org/10.36334/modsim.2021.M4.richmond

  • System dynamics applications for defence combat modelling: preliminary insights from a literature exploration
    

    Cunico, G., Elsawah, S., Gary, M.S., Cao, T., Kosowski, L. and Richmond, M.K.
    https://doi.org/10.36334/modsim.2021.M4.cunico

• Kroese, D.
  • MOOR: Model-based Offline Reinforcement learning for sustainable fishery management
    

    Ju, J., Kurniawati, H., Kroese, D. and Ye, N.
    https://doi.org/10.36334/modsim.2021.M2.ju

• Kuczera, G.
  • 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

  • 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

  • Robust detection of statistically significant correlations in geophysical timeseries: A Monte Carlo method accounting for serial dependence and sampling uncertainty
    

    Armstrong, M.S., Kiem, A.S. and Kuczera, G.
    https://doi.org/10.36334/modsim.2021.A1.armstrong

• Kurniawati, H.
  • MOOR: Model-based Offline Reinforcement learning for sustainable fishery management
    

    Ju, J., Kurniawati, H., Kroese, D. and Ye, N.
    https://doi.org/10.36334/modsim.2021.M2.ju

• Kurucz, K.
  • Modelling methane emissions from lakes and reservoirs: case study of Lake Kinneret
    

    Kurucz, K., Hipsey, M.R. and Huang, P.
    https://doi.org/10.36334/modsim.2021.L3.kurucz

• Kwakkel, J.H.
  • 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