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

E

• Eby, W.
  • A flexible family of hypertabastic models
    

    Singh, K., Harrison, N., Eby, W., Tabatabai, M., Bursac, Z., Uhm, M., Bartolucci, A. and Bae, S.
    https://doi.org/10.36334/modsim.2019.I1.singh

• Edgar, R.A.
  • Channel fire plume dynamics in sloping terrains
    

    Edgar, R.A., Sharples, J.J. and Sidhu, H.S.
    https://doi.org/10.36334/modsim.2019.H7.edgar

• Edraki, M.
  • Exploring post 2011–12 drought in the Murray–Darling Basin
    

    Nahar, J., Watson, M., Edraki, M., Walker, M. and George, B.
    https://doi.org/10.36334/modsim.2019.K22.nahar

• El-Mahassni, E.
  • Modelling time decay in uncertain implication rules of evidence theory with a focus on cyber attacks
    

    El-Mahassni, E.
    https://doi.org/10.36334/modsim.2019.B1.el-mahassni

  • A protocol for selecting a rule of combination for fusion of imprecise sensor data
    

    El-Mahassni, E.
    https://doi.org/10.36334/modsim.2019.B1.el-mahassni2

• Elsawah, S.
  • Repair priorities in repairable spare part supply systems: a simulation-optimization approach
    

    Turan, H.H., Elsawah, S. and Ryan, M.J.
    https://doi.org/10.36334/modsim.2019.B8.turan

• Esu, O.-O.
  • Irrigation water resource management: 'IW-QC2' software tool
    

    Awad, J., Pezzaniti, D., Vanderzalm, J., Esu, O.-O. and van Leeuwen, J.
    https://doi.org/10.36334/modsim.2019.K9.awad

• Evans, J.P.
  • 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

• Eyles, M.
  • Modelling nitrogen transport in sugar cane from soil to runoff from banded surface and buried fertiliser using HYDRUS2D and a post-processing algorithm
    

    Cook, F.J., Eyles, M., Cowie, B., Bosomworth, B. and Silburn, M.
    https://doi.org/10.36334/modsim.2019.K15.cook

F

• Feng, Y.
  • 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

• Fentie, B.
  • Estimating bankfull channel geometry, dimensions and associated hydraulic attributes using high resolution DEM generated from LiDAR data
    

    Fentie, B.
    https://doi.org/10.36334/modsim.2019.K15.fentie

• Ferguson, A.J.P.
  • A collaborative review process supports better outcomes from environmental modelling
    

    Robson, B.J., Cuddy, S.M., Glamore, W., Rogers, Z., Ferguson, A.J.P. and Scanes, P.
    https://doi.org/10.36334/modsim.2019.K1.robson

• Filar, J.A.
  • POMDPs for sustainable fishery management
    

    Filar, J.A., Qiao, Z. and Ye, N.
    https://doi.org/10.36334/modsim.2019.G2.filar

• Filkov, A.
  • Quantifying dynamic fire behaviour phenomena using Unmanned Aerial Vehicle technology
    

    Filkov, A., Cirulis, B. and Penman, T.
    https://doi.org/10.36334/modsim.2019.H7.filkov

• Fitch, P.
  • Machine learning approaches to improve and predict water quality data
    

    Zhang, Y., Thorburn, P.J., Vilas, M.P. and Fitch, P.
    https://doi.org/10.36334/modsim.2019.D5.zhangYiF

• Fleming, C.
  • Bilevel optimisation as a solution method for an agri-environmental principal-agent problem
    

    Barnhart, B.L., Bostian, M.B., Hasan, S., Smart, J.C.R. and Fleming, C.
    https://doi.org/10.36334/modsim.2019.J6.barnhart

• Ford, J.J.
  • Bushfire emergency response simulation
    

    Bruggemann, T., Ford, J.J., White, G. and Perez, T.
    https://doi.org/10.36334/modsim.2019.A2.bruggemann2

  • Bushfire emergency response uncertainty quantification
    

    Bruggemann, T., Ford, J.J., White, G., Perez, T. and Power, W.
    https://doi.org/10.36334/modsim.2019.A2.bruggemann

• Fournier, D.
  • Modelling a multi-modal logistic network with agents and dynamic graphs
    

    Démare, T., Balev, S., Bertelle, C., Dutot, A., Fournier, D. and Sanlaville, E.
    https://doi.org/10.36334/modsim.2019.B8.demare

• Fraser, G.
  • Improving evapotranspiration estimation in pasture and native vegetation models using flux tower data, remote sensing and global optimisation
    

    Owens, J., Carter, J., Fraser, G., Cleverly, J., Hutley, L. and Barnetson, J.
    https://doi.org/10.36334/modsim.2019.C6.owens

• Frederiksen, C.S.
  • The role of climate drift in seasonal forecasts
    

    Osbrough, S.L., Frederiksen, J.S. and Frederiksen, C.S.
    https://doi.org/10.36334/modsim.2019.H8.osbrough

• Frederiksen, J.S.
  • The role of climate drift in seasonal forecasts
    

    Osbrough, S.L., Frederiksen, J.S. and Frederiksen, C.S.
    https://doi.org/10.36334/modsim.2019.H8.osbrough

• Freebairn, L.
  • “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

• Fu, B.
  • 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

• Fukazawa, S.
  • Development of an Atmosphere-Ocean-Geosphere-Coupled Climate Model and its application to the Kanto plain, Japan
    

    Vuillaume, J.F., Fukazawa, S., Goto, K., Hazart, A., Konishi, Y., Onishi, R., Sugiyama, T., Tada, K., Tawara, Y., Tosaka, H. and Takahashi, K.
    https://doi.org/10.36334/modsim.2019.K9.vuillaume

G

• Gallant, J.
  • Merging lidar with coarser DEMs for hydrodynamic modelling over large areas
    

    Gallant, J.
    https://doi.org/10.36334/modsim.2019.K24.gallant

• Gan, C.
  • Investment location selection: Strategies for foreign direct investment in Vietnam
    

    Ho, L.T., Gan, C. and Hu, B.
    https://doi.org/10.36334/modsim.2019.E2.ho

  • Impacts of Microfinance Programs in Thailand
    

    Hemtanon, W. and Gan, C.
    https://doi.org/10.36334/modsim.2019.E5.hemtanon

  • Inadequate corporate profitability and marketability efficiency: A case of Vietnam manufacturing enterprises
    

    Anh, D.L.T. and Gan, C.
    https://doi.org/10.36334/modsim.2019.E2.anh

• Garg, N.
  • Wind-terrain effects on firebrand dynamics
    

    Hilton, J.E., Sharples, J.J., Garg, N., Rudman, M., Swedosh, W. and Commins, D.
    https://doi.org/10.36334/modsim.2019.H7.hilton

  • Urban sensing and weather prediction: Can IOT devices be used to improve weather prediction in cities?
    

    Garg, N., Prakash, M., Thatcher, M. and Sankaran, R.
    https://doi.org/10.36334/modsim.2019.J4.garg

  • Incorporating firebrands and spot fires into vorticity-driven wildfire behaviour models
    

    Hilton, J.E., Garg, N. and Sharples, J.J.
    https://doi.org/10.36334/modsim.2019.H7.hilton2

• Gato-Trinidad, S.
  • 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

• George, B.
  • Exploring post 2011–12 drought in the Murray–Darling Basin
    

    Nahar, J., Watson, M., Edraki, M., Walker, M. and George, B.
    https://doi.org/10.36334/modsim.2019.K22.nahar

• Georgiou, F.
  • 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

• Giampietro, M.
  • 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

• Gibson, A.J.
  • Centennial-scale variability of soil moisture in eastern Australia
    

    Gibson, A.J., Hancock, G.R., Verdon-Kidd, D.C. and Wells, T.
    https://doi.org/10.36334/modsim.2019.K22.gibson

• Gill, A.
  • Two common pitfalls applying design of experiments (and hopefully how to avoid them!)
    

    Gill, A.
    https://doi.org/10.36334/modsim.2019.B7.gill

• Glamore, W.
  • A collaborative review process supports better outcomes from environmental modelling
    

    Robson, B.J., Cuddy, S.M., Glamore, W., Rogers, Z., Ferguson, A.J.P. and Scanes, P.
    https://doi.org/10.36334/modsim.2019.K1.robson

• Gonzalez-Barras, R.M.
  • Air quality modelling of 2016 Madrid air pollution episode
    

    San José, R., Pérez-Camaño, J.L., Pérez, L. and Gonzalez-Barras, R.M.
    https://doi.org/10.36334/modsim.2019.G7.sanjose

• Goto, K.
  • Development of an Atmosphere-Ocean-Geosphere-Coupled Climate Model and its application to the Kanto plain, Japan
    

    Vuillaume, J.F., Fukazawa, S., Goto, K., Hazart, A., Konishi, Y., Onishi, R., Sugiyama, T., Tada, K., Tawara, Y., Tosaka, H. and Takahashi, K.
    https://doi.org/10.36334/modsim.2019.K9.vuillaume

• Graham, K.I.
  • “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

• Grêt-Regamey, A.
  • Where do we recreate? Comparison of different methods to determine importance of site characteristics for outdoor recreation
    

    Willibald, F., Van Strien, M. and Grêt-Regamey, A.
    https://doi.org/10.36334/modsim.2019.G7.willibald

• Grice, V.
  • 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

• Grixti-Cheng, D.
  • Stratified space learning: Reconstructing embedded graphs
    

    Bokor, Y., Grixti-Cheng, D., Hegland, M., Roberts, S. and Turner, K.
    https://doi.org/10.36334/modsim.2019.A3.bokor

• Grozev, G.
  • Hydrogen as a future fuel, challenges and opportunities: An overview of modelling approaches
    

    Grozev, G., Ramezani, F., Perez, P., Whetton, S. and Young, M.D.
    https://doi.org/10.36334/modsim.2019.F5.grozev

• Guillaume, J.H.A.
  • Downscaling runoff products using areal interpolation: a combined pycnophylactic-dasymetric method
    

    Kallio, M., Virkki, V., Guillaume, J.H.A. and Van Dijk, A.I.J.M.
    https://doi.org/10.36334/modsim.2019.K8.kallio

  • Constructing customized modeling guidelines: a Participatory Integrated Assessment and Modeling example
    

    Zare, F., Guillaume, J.H.A. and Jakeman, A.J.
    https://doi.org/10.36334/modsim.2019.K1.zare

• Guo, 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

• Guo, L.
  • 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

• Gupta, D.
  • Multistage flow shop scheduling with bi-criteria objective using sequence dependent setup times
    

    Nailwal, K.K., Gupta, D. and Sharma, S.
    https://doi.org/10.36334/modsim.2019.A5.nailwal