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

• Lambert, V.
  • Towards Ecologically Relevant Targets: Impact of flow and sediment discharge on seagrass communities in the Great Barrier Reef
    

    Lambert, V., Adams, M.P., Collier, C., Carter, A., Saunders, M., Brodie, J., Bainbridge, Z., Rasheed, M., Turner, R. and O'Brien, K.R.
    https://doi.org/10.36334/modsim.2019.G1.lambert

• Lamichhane, B.P.
  • Modelling cell aggregation using a modified swarm model
    

    Georgiou, F., Thamwattana, N. and Lamichhane, B.P.
    https://doi.org/10.36334/modsim.2019.A1.georgiou

• Larraondo, P.R.
  • Burn extent and severity mapping by spectral anomaly detection in the Landsat data cube
    

    Renzullo, L.J., Tian, S., Van Dijk, A.I.J.M., Larraondo, P.R., Yebra, M., Yuan, F. and Mueller, N.
    https://doi.org/10.36334/modsim.2019.H7.renzullo

• Leemaqz, S.Y.
  • Artificial Neural Networks & Random Forest Classification of druggable molecules and disease targets via scoring functions (SFs)
    

    Hudson, I.L., Leemaqz, S.Y. and Abell, A.D.
    https://doi.org/10.36334/modsim.2019.A1.hudson

• Leonard, D.
  • Modelling household water use: Using empirical data to inform continuous simulation
    

    Leonard, D. and Gato-Trinidad, S.
    https://doi.org/10.36334/modsim.2019.K9.leonard

• Lerat, J.
  • Assimilating satellite soil moisture retrievals to improve operational water balance modelling
    

    Tian, S., Renzullo, L.J., Pipunic, R., Sharples, W., Lerat, J. and Donnelly, C.
    https://doi.org/10.36334/modsim.2019.H6.tian

• Leslie, L.M.
  • Trends in precipitation and temperature in Canberra
    

    Hartigan, J., MacNamara, S. and Leslie, L.M.
    https://doi.org/10.36334/modsim.2019.K7.hartigan

• Li, H.
  • Quantifying the contribution of multiple factors to land subsidence using artificial intelligence technology in the typical area of Beijing, China
    

    Cheng, R., Zhu, L., Li, H., Yu, J., Guo, L., Hu, L. and Li, Y.
    https://doi.org/10.36334/modsim.2019.K18.chengR

• Li, Y.
  • Source adoption in Northern Victoria: a hydrological model of an integrated Goulburn, Broken, Campaspe, Coliban and Loddon Rivers system
    

    Li, Y., Manandhar, U. and Mariyapillai, S.
    https://doi.org/10.36334/modsim.2019.K25.li

  • Quantifying the contribution of multiple factors to land subsidence using artificial intelligence technology in the typical area of Beijing, China
    

    Cheng, R., Zhu, L., Li, H., Yu, J., Guo, L., Hu, L. and Li, Y.
    https://doi.org/10.36334/modsim.2019.K18.chengR

• Liem, R.P.
  • Routing strategy using local information based on a two layer cellular automaton model
    

    Zhu, B. and Liem, R.P.
    https://doi.org/10.36334/modsim.2019.F3.zhu

• Lim-Camacho, L.
  • Reflecting on integrated assessment in the Socially Inclusive Agricultural Intensification (SIAGI) project
    

    Merritt, W.S., Hamilton, S.H., Carter, L., Cosijn, M., Lim-Camacho, L. and Roth, C.H.
    https://doi.org/10.36334/modsim.2019.J9.merritt

  • Integrated assessment frameworks for understanding pathways for socially inclusive agricultural intensification
    

    Hamilton, S.H., Merritt, W.S., Lim-Camacho, L., Carter, L., Cosijn, M. and Roth, C.H.
    https://doi.org/10.36334/modsim.2019.J7.hamilton

• Lintern, A.
  • Future Water: Comparing and contrasting approaches to predicting water quality
    

    Guo, D., Lintern, A., Prodanovic, V., Kuller, M., Bach, P.M., Deletic, A., Shi, B., McCarthy, D., Ryu, D., Webb, J.A., Liu, S. and Western, A.W.
    https://doi.org/10.36334/modsim.2019.K15.guo

• Liu, F.
  • Exploring the relationship between spatiotemporal characteristics of snow cover and ENSO episodes in the Qinghai-Tibetan Plateau
    

    Zhang, C., Liu, F. and Shen, Y.
    https://doi.org/10.36334/modsim.2019.H1.zhangC

• Liu, S.
  • Future Water: Comparing and contrasting approaches to predicting water quality
    

    Guo, D., Lintern, A., Prodanovic, V., Kuller, M., Bach, P.M., Deletic, A., Shi, B., McCarthy, D., Ryu, D., Webb, J.A., Liu, S. and Western, A.W.
    https://doi.org/10.36334/modsim.2019.K15.guo

• Liu, S.X.
  • Evaporation rates for a coral island by field observation and simulation
    

    Han, S.S., Liu, S.X., Hu, S., Mo, X. and Song, X.
    https://doi.org/10.36334/modsim.2019.A4.han

• Lo, E.H.S.
  • Baselining the whole-of-force capability and capacity of the Australian Defence Force
    

    Peacock, J., Blumson, D., Mangalasinghe, J., Hepworth, A., Coutts, A. and Lo, E.H.S.
    https://doi.org/10.36334/modsim.2019.B3.peacock

• Loechel, B.
  • Area-wide management guidelines for Sterile Insect Technique developed through interdisciplinary modelling research
    

    Parry, H., Capon, T., Clarke, A., Crisp, P., Hulthen, A., Kandulu, J., Kranz, B., Loechel, B., Mankad, A., Measham, P., Merkel, K., Murray, J., Reynolds, O., Schellhorn, N., Schwarzmueller, F., Tam, M., Tapsuwan, S., van Klinken, R., Whitten, S. and Willi
    https://doi.org/10.36334/modsim.2019.G3.parry

• Lopez Benavides, M.
  • Detection of dairy cattle Mastitis: modelling of milking features using deep neural networks
    

    Khamaysa Hajaya, M., Samarasinghe, S., Kulasiri, D. and Lopez Benavides, M.
    https://doi.org/10.36334/modsim.2019.A1.khamaysahajaya

• Lowry, M.M.
  • Modelling feedlots using the MEDLI model framework
    

    Vieritz, A.M., Lowry, M.M., Sullivan, T.J., Atzeni, M.G., Hope-O'Connor, A.J. and Watts, P.J.
    https://doi.org/10.36334/modsim.2019.C1.vieritz

• Loy, R.J.
  • Deconvolution for oscillatory shear rheometry using the Landweber iteration
    

    Anderssen, R.S., de Hoog, F.R. and Loy, R.J.
    https://doi.org/10.36334/modsim.2019.A3.anderssen

M

• Macknight, M.
  • Classifying the shape of qPCR expression data
    

    Whigham, P.A., Stanton, J., Mason, C., Rawle, C.B. and Macknight, M.
    https://doi.org/10.36334/modsim.2019.E2.whigham

• MacNamara, S.
  • Trends in precipitation and temperature in Canberra
    

    Hartigan, J., MacNamara, S. and Leslie, L.M.
    https://doi.org/10.36334/modsim.2019.K7.hartigan

• Magee, A.D.
  • Revisiting the Australian/New Zealand Standard for Wind Actions (AS/NZS 1170.2:2011): Do current wind standards sufficiently capture local wind climates?
    

    Magee, A.D. and Kiem, A.S.
    https://doi.org/10.36334/modsim.2019.H8.magee

• Maheswaran, S.
  • Assessment of hydrological stationarity of the Border Rivers catchment
    

    Raut, A.K., Kibria, G., Nandakumar, N. and Maheswaran, S.
    https://doi.org/10.36334/modsim.2019.K7.raut

• Mai, L.S.
  • An integrated approach for improved management of an island's scarce water resources under climate change and tourism development
    

    Phan, T.D., Sahin, O., Trinh, H.D., Dao, H.N., Mai, L.S., Pham, T.V., Do, T.P., Vu, T.M. and Capon, S.J.
    https://doi.org/10.36334/modsim.2019.J1.phan

• Makarieva, O.
  • Understanding hydrological processes at a remote mountainous continuous permafrost watershed in a changing environment
    

    Nesterova, N., Makarieva, O. and Post, D.A.
    https://doi.org/10.36334/modsim.2019.K28.nesterova

• Mak-Hau, V.
  • Aggregation of dependent criteria in multicriteria decision making problems by means of capacities
    

    Beliakov, G., Bowsell, S., Cao, T., Dazeley, R., Mak-Hau, V., Nguyen, M.-T., Wilkin, T. and Yearwood, J.
    https://doi.org/10.36334/modsim.2019.B3.beliakov

• Manandhar, U.
  • A vision for future hydrologic modelling to support Victoria's evolving water resource management
    

    Mariyapillai, S., Manandhar, U. and Sheedy, T.
    https://doi.org/10.36334/modsim.2019.K27.mariyapillai

  • Source adoption in Northern Victoria: a hydrological model of an integrated Goulburn, Broken, Campaspe, Coliban and Loddon Rivers system
    

    Li, Y., Manandhar, U. and Mariyapillai, S.
    https://doi.org/10.36334/modsim.2019.K25.li

• Mangalasinghe, J.
  • Baselining the whole-of-force capability and capacity of the Australian Defence Force
    

    Peacock, J., Blumson, D., Mangalasinghe, J., Hepworth, A., Coutts, A. and Lo, E.H.S.
    https://doi.org/10.36334/modsim.2019.B3.peacock

• Mankad, A.
  • Area-wide management guidelines for Sterile Insect Technique developed through interdisciplinary modelling research
    

    Parry, H., Capon, T., Clarke, A., Crisp, P., Hulthen, A., Kandulu, J., Kranz, B., Loechel, B., Mankad, A., Measham, P., Merkel, K., Murray, J., Reynolds, O., Schellhorn, N., Schwarzmueller, F., Tam, M., Tapsuwan, S., van Klinken, R., Whitten, S. and Willi
    https://doi.org/10.36334/modsim.2019.G3.parry

• Manning, C.J.
  • A novel approach to informing the future land force: Incorporating design thinking and concept, development and exploration methods
    

    Manning, C.J., Stephens, A. and Richmond, M.K.
    https://doi.org/10.36334/modsim.2019.B3.manning

• Mariyapillai, S.
  • A vision for future hydrologic modelling to support Victoria's evolving water resource management
    

    Mariyapillai, S., Manandhar, U. and Sheedy, T.
    https://doi.org/10.36334/modsim.2019.K27.mariyapillai

  • Source adoption in Northern Victoria: a hydrological model of an integrated Goulburn, Broken, Campaspe, Coliban and Loddon Rivers system
    

    Li, Y., Manandhar, U. and Mariyapillai, S.
    https://doi.org/10.36334/modsim.2019.K25.li

• Marsh, N.
  • Closing the loop on modelling's role in natural resource investment planning
    

    Marsh, N., Thompson, C., Stewart, M., Grice, V., Phung, V. and Smolders, A.
    https://doi.org/10.36334/modsim.2019.D3.marsh

• Mascaro, S.
  • Bayesian network meta-models from combat simulation for Defence decision analysis
    

    Shahzad, A., Mascaro, S., Cao, T. and Korb, K.B.
    https://doi.org/10.36334/modsim.2019.B8.shahzad

• Masek, M.
  • Evolving behaviour trees for automated discovery of novel combat strategy in real-time strategy wargames
    

    Masek, M., Peng Lam, C., Kelly, L. and Wong, M.
    https://doi.org/10.36334/modsim.2019.B4.masek

• Mason, C.
  • Classifying the shape of qPCR expression data
    

    Whigham, P.A., Stanton, J., Mason, C., Rawle, C.B. and Macknight, M.
    https://doi.org/10.36334/modsim.2019.E2.whigham

• Matthews, K.B.
  • Science for Sustainability: Using Societal Metabolism Analysis to check the robustness of European Union policy narratives in the water, energy and food nexus
    

    Matthews, K.B., Waylen, K.A., Blackstock, K.L., Juarez-Bourke, A., Miller, D.G., Wardell-Johnson, D.H., Rivington, M. and Giampietro, M.
    https://doi.org/10.36334/modsim.2019.J5.matthews

• Mazzola, M.R.
  • A multi-scenario Decision Support System for real-time operation of over-year multi-reservoir systems 2. DSS simulation
    

    Arena, C., Cannarozzo, M., Oliva, F. and Mazzola, M.R.
    https://doi.org/10.36334/modsim.2019.K9.arena2

  • A multi-scenario Decision Support System for real-time operation of over-year multi-reservoir systems 1. Model structure
    

    Arena, C., Cannarozzo, M., Oliva, F. and Mazzola, M.R.
    https://doi.org/10.36334/modsim.2019.K9.arena

• McAleer, M.
  • Pricing carbon emissions for Japan based on energy production
    

    Chang, C.-L., Hamori, S. and McAleer, M.
    https://doi.org/10.36334/modsim.2019.E3.chang2

  • Modelling latent carbon emission prices: theory and practice
    

    Chang, C.-L. and McAleer, M.
    https://doi.org/10.36334/modsim.2019.E3.mcaleer

• McCarthy, D.
  • Future Water: Comparing and contrasting approaches to predicting water quality
    

    Guo, D., Lintern, A., Prodanovic, V., Kuller, M., Bach, P.M., Deletic, A., Shi, B., McCarthy, D., Ryu, D., Webb, J.A., Liu, S. and Western, A.W.
    https://doi.org/10.36334/modsim.2019.K15.guo

• McDonnell, G.
  • “Stopping before you start”: Reducing and preventing initiation of tobacco use in the ACT
    

    Freebairn, L., Kreuger, K., Watson, C., Yang, O., Prodan, A., Graham, K.I., Draper, G., McDonnell, G., Nixon, E. and Atkinson, J-A.
    https://doi.org/10.36334/modsim.2019.I2.freebairn

• McIntosh, A.C.
  • 2D chaotic flow in competitive exothermic-endothermic reactions
    

    Huang, Z., Watt, S.D., Sidhu, H.S., McIntosh, A.C., Brindley, J., Jovanoski, Z. and Towers, I.N.
    https://doi.org/10.36334/modsim.2019.A5.huang

• McLennan-Smith, T.A.
  • Exploiting ecological non-trophic models in representations of warfare
    

    McLennan-Smith, T.A., Kalloniatis, A.C., Sidhu, H.S., Jovanoski, Z., Watt, S.D. and Towers, I.N.
    https://doi.org/10.36334/modsim.2019.B3.mclennan-smith

  • Stochastic processes in a discrete model of ground combat
    

    McLennan-Smith, T.A., Nelson, M., Jovanoski, Z., Rodrigo, M. and Sidhu, H.S.
    https://doi.org/10.36334/modsim.2019.A5.mclennansmith

• McNaughton, R.
  • An integrated optimisation functionality for Workspace
    

    Potter, D., Hetherton, L., Thomas, D.G., McNaughton, R. and Watkins, D.
    https://doi.org/10.36334/modsim.2019.D2.potter

• McRae, R.H.D.
  • Evolution of southeast Australian fire activity seasons
    

    McRae, R.H.D.
    https://doi.org/10.36334/modsim.2019.H7.mcrae

  • Insights into the role of fire geometry and violent pyroconvection
    

    Badlan, R.L., Sharples, J.J., Evans, J.P. and McRae, R.H.D.
    https://doi.org/10.36334/modsim.2019.H7.badlan

• Measham, P.
  • Area-wide management guidelines for Sterile Insect Technique developed through interdisciplinary modelling research
    

    Parry, H., Capon, T., Clarke, A., Crisp, P., Hulthen, A., Kandulu, J., Kranz, B., Loechel, B., Mankad, A., Measham, P., Merkel, K., Murray, J., Reynolds, O., Schellhorn, N., Schwarzmueller, F., Tam, M., Tapsuwan, S., van Klinken, R., Whitten, S. and Willi
    https://doi.org/10.36334/modsim.2019.G3.parry

• Mendes, A.
  • Modelling railway traffic management through multi-agent systems and reinforcement learning
    

    Bretas, A., Mendes, A., Chalup, S., Jackson, M., Clement, R. and Sanhueza, C.
    https://doi.org/10.36334/modsim.2019.B6.bretas

• Meng, P.
  • Flow balancing in a wastewater network by optimising storage in pumping stations
    

    Burt, C., Candy, B., D'Arpa, R., Koh, E.L., Meng, P., Salamone, D., Sondalini, T. and Taylor, P.
    https://doi.org/10.36334/modsim.2019.B6.burt

• Merkel, K.
  • Area-wide management guidelines for Sterile Insect Technique developed through interdisciplinary modelling research
    

    Parry, H., Capon, T., Clarke, A., Crisp, P., Hulthen, A., Kandulu, J., Kranz, B., Loechel, B., Mankad, A., Measham, P., Merkel, K., Murray, J., Reynolds, O., Schellhorn, N., Schwarzmueller, F., Tam, M., Tapsuwan, S., van Klinken, R., Whitten, S. and Willi
    https://doi.org/10.36334/modsim.2019.G3.parry

• Merritt, W.S.
  • Reflecting on integrated assessment in the Socially Inclusive Agricultural Intensification (SIAGI) project
    

    Merritt, W.S., Hamilton, S.H., Carter, L., Cosijn, M., Lim-Camacho, L. and Roth, C.H.
    https://doi.org/10.36334/modsim.2019.J9.merritt

  • What do we need from catchment water quality models in Australia?
    

    Fu, B., Merritt, W.S., Cuddy, S.M., Jakeman, A.J., Croke, B., Weber, T., Hall, J., Waters, D.K. and Baker, D.
    https://doi.org/10.36334/modsim.2019.K15.fuB

  • Integrated assessment frameworks for understanding pathways for socially inclusive agricultural intensification
    

    Hamilton, S.H., Merritt, W.S., Lim-Camacho, L., Carter, L., Cosijn, M. and Roth, C.H.
    https://doi.org/10.36334/modsim.2019.J7.hamilton

• Mewett, J.L.
  • The collaboration model delivering land use information for Australia
    

    Stewart, J.B. and Mewett, J.L.
    https://doi.org/10.36334/modsim.2019.K1.stewart

• Miller, D.G.
  • Science for Sustainability: Using Societal Metabolism Analysis to check the robustness of European Union policy narratives in the water, energy and food nexus
    

    Matthews, K.B., Waylen, K.A., Blackstock, K.L., Juarez-Bourke, A., Miller, D.G., Wardell-Johnson, D.H., Rivington, M. and Giampietro, M.
    https://doi.org/10.36334/modsim.2019.J5.matthews

• Mills, G.
  • Victorian fire weather trends and variability
    

    Harris, S., Mills, G. and Brown, T.
    https://doi.org/10.36334/modsim.2019.H7.harris

• Mitchell, P.J.
  • Pasture API: A digital platform to support grazing management for southern Australia
    

    Thomas, D.T., Mitchell, P.J., Zurcher, E.J., Herrmann, N.I., Pasanen, J., Sharman, C. and Henry, D.A.
    https://doi.org/10.36334/modsim.2019.C1.thomas

• Mo, X.
  • Evaporation rates for a coral island by field observation and simulation
    

    Han, S.S., Liu, S.X., Hu, S., Mo, X. and Song, X.
    https://doi.org/10.36334/modsim.2019.A4.han

• Mueller, N.
  • Burn extent and severity mapping by spectral anomaly detection in the Landsat data cube
    

    Renzullo, L.J., Tian, S., Van Dijk, A.I.J.M., Larraondo, P.R., Yebra, M., Yuan, F. and Mueller, N.
    https://doi.org/10.36334/modsim.2019.H7.renzullo

• Mukai, N.
  • Particle based droplet simulation in liquid-liquid two-phase flow
    

    Natsume, T., Oishi, M., Oshima, M. and Mukai, N.
    https://doi.org/10.36334/modsim.2019.I3.natsume

• Murphy, A.B.
  • ArcWeld: A case study of the extensibility of software applications built using Workspace architecture
    

    Thomas, D.G., Murphy, A.B., Chen, F.F., Xiang, J. and Feng, Y.
    https://doi.org/10.36334/modsim.2019.D2.thomasDG

• Murray, J.
  • Area-wide management guidelines for Sterile Insect Technique developed through interdisciplinary modelling research
    

    Parry, H., Capon, T., Clarke, A., Crisp, P., Hulthen, A., Kandulu, J., Kranz, B., Loechel, B., Mankad, A., Measham, P., Merkel, K., Murray, J., Reynolds, O., Schellhorn, N., Schwarzmueller, F., Tam, M., Tapsuwan, S., van Klinken, R., Whitten, S. and Willi
    https://doi.org/10.36334/modsim.2019.G3.parry