Dr Greer Humphrey

Dr Greer Humphrey
 Position Research Associate
 Org Unit School of Public Health
 Email greer.humphrey@adelaide.edu.au
 Telephone +61 8 8313 1692
 Location Floor/Room WS9050.15 ,  Adelaide Health and Medical Sciences ,   North Terrace
  • Biography/ Background

    Employment History

    • 2011 - 2016        Senior Research Associate, School of Civil, Environmental and Mining
                                 Engineering, University of Adelaide, South Australia.
    • 2012                   Lecturer, School of  Civil, Environmental and Mining Engineering,
                                 University of Adelaide, South Australia.
    • 2009 - 2010        Senior Catastrophe Risk Modeller, Risk Management Solutions,
                                 London, UK.
    • 2007 - 2009        Flood Risk Engineer, HR Wallingford, Wallingford, Oxfordshire, UK.
    • 2006 - 2007        Senior Research Associate, School of Civil and Environmental
                                 Engineering, University of Adelaide, South Australia.
    • 2006                   Lecturer (Casual), School of Civil and Environmental Engineering,
                                 University of Adelaide, South Australia.
    • 2002 - 2006        PhD Candidate, School of Civil and Environmental Engineering,
                                 University of Adelaide, South Australia.
    • 2000 - 2001        Graduate Engineer, Tonkin Consulting, Wayville, South Australia.

    Academic Qualifications

    • 2006     Doctor of Philosophy in Water Resources Engineering,
                   The University of Adelaide, South Australia.
                   Thesis Title:
        Bayesian artificial neural networks in water resources engineering.
    • 2000     Bachelor of Civil and Environmental Engineering, The University of Adelaide,
                   South Australia. Awarded First-Class Honours.


    Prizes and Awards

    • 2008              International Association of Hydro-Environment Engineering and Research
                           (IAHR) UK Young Person Papers Competition, 2nd prize.
    • 2002 - 2005   Australian Postgraduate Award (APA)
    • 2003              Student Commendation for Excellence, International Congress on Modelling
                            and Simulation (MODSIM 2003).
  • Research Interests

    The application of machine learning and statistical modelling techniques, including:

    • artificial neural networks
    • Bayesian statistics
    • evolutionary optimisation
    • uncertainty/risk analysis

    in problems related to the management of water.

    Current Research Project

    Improved modelling of the catchments and drainage network in the Upper South East for management outcomes.

    Supervisor(s)

    Professor Holger Maier - Professor, School of Civil, Environmental and Mining Engineering, University of Adelaide.

    Professor Graeme Dandy - Professor, School of Civil, Environmental and Mining Engineering, University of Adelaide.

  • Publications

    Books

    Kingston G. B., Maier H. R. and M. F. Lambert (2010) Bayesian Artificial Neural Networks: with Applications in Water Resources Engineering, VDM Verlag, Saarbrücken, Germany, ISBN:978-3-639-22324-8, 340p.

    Book Chapters

    Kingston, G. B., G. C. Dandy and H. R. Maier (2008), AI Techniques for Hydrological Modeling and Water Resources Management. Part 2 - Optimization, in L. N. Robinson (editor) Water Resources Research Progress, Nova Science Publishers, pp. 67-99.

    Kingston, G. B., H. R. Maier, and G. C. Dandy (2008), AI Techniques for Hydrological Modeling and Water Resources Management. Part 1 - Simulation, in L. N. Robinson (editor) Water Resources Research Progress, Nova Science Publishers, pp. 15-65.

    Journal Publications

    Galelli S., Humphrey G. B., Maier H. R., Castelletti A., Dandy G. C. and Gibbs M. S. (2014), An evaluation framework for input variable selection algorithms for environmental data-driven modelsEnvironmental Modelling and Software, 62, 33–51, doi:10.1016/j.envsoft.2014.08.015. [IVS4EM]

    Kingston, G. B., M. Rajabalinejad, B. P. Gouldby, and P. H. A. J. M. Van Gelder (2008), Computational intelligence methods for efficient reliability analysis of complex flood
    defence structures
    , Structural Safety, 33(1), 64–73, doi:10.1016/j.strusafe.2010.08.002.

    Kingston, G. B., H. R. Maier, and M. F. Lambert (2007), Bayesian model selection applied to artificial neural networks used for water resources modeling, Water Resources Research, 44, W04419, doi:10.1029/2007WR006155.

    Kingston, G. B., H. R. Maier, and M. F. Lambert (2006), A probabilistic method to assist knowledge extraction from artificial neural networks used for hydrological prediction, Mathematical and Computer Modelling, 44(5–6), 499–512, doi:10.1016/j.mcm.2006.01.008.

    Kingston, G. B., M. F. Lambert, and H. R. Maier (2005), Bayesian training of artificial neural networks used for water resources modeling, Water Resources Research, 41(12), W12409, doi:10.1029/2005WR004152.

    Kingston, G. B., H. R. Maier, and M. F. Lambert (2005), Calibration and validation of neural networks to ensure physically plausible hydrological modeling, Journal of Hydrology, 314(1–4), 158–176, doi:10.1016/j.jhydrol.2005.03.013.

    Maier, H. M, G. B. Kingston, T. Clark, A. Frazer, and A. Sanderson (2004), A risk based approach for assessing the effectiveness of flow management in controlling cyanobacterial blooms in rivers, River Research and Applications, 20(4), 459–471, doi:10.1002/rra.760.

    Refereed Conference Publications

    Humphrey, G. B., Galelli, S., Maier, H. R., Castelletti, A., Dandy, G. C., Gibbs, M. S. (2014) Automatic input selection for hydrological modelling: a comparative analysis. In European Geosciences Union (EGU) General Assembly, Vienna (Austria), 27 April-4 May.

    Humphrey, G. B., Galelli, S., Castelletti, A., Maier, H. R., Dandy, G. C., Gibbs., M. S. (2014) A new evaluation framework for input variable selection algorithms used in environmental modelling International Environmental Modelling and Software Society (iEMSs) 7th Intl. Congress on Env. Modelling and Software, San Diego, California, USA Daniel P. Ames, Nigel W. T. Quinn, Andrea E. Rizzoli (Eds.)

    Kingston, G. B., D. I. Robinson, B. P Gouldby, and T. A. Pullen (2008), Reliable prediction of wave overtopping volumes using Bayesian neural networks, FLOODrisk 2008 European Conference on Flood Risk Management, Oxford, United Kingdom.

    Kingston, G. B., H. R Maier, and M. F. Lambert (2006), Forecasting cyanobacteria with Bayesian and deterministic artificial neural networks, IEEE World Congress on Computational Intelligence (WCCI 2006), IJCNN1869, pp. 4870-4877. Vancouver, Canada.

    Kingston, G. B., H. R Maier, and M. F. Lambert (2005), A Bayesian approach to artificial neural network model selection, in Zerger, A. and Argent, R. M. (eds), MODSIM 2005 International Congress on Modelling and Simulation, pp. 1853–1859. Melbourne, Australia.

    Kingston, G. B., H. R Maier, and M. F. Lambert (2005), A Bayesian method to improve the extrapolation ability of ANNs, in Hamza, M. H. (ed), IASTED International Conference on Applied Simulation and Modelling, no. 469–126. Benalmádena, Spain.

    Kingston, G. B., H. R. Maier, and M. F. Lambert (2004), A statistical input pruning method for artificial neural networks used in environmental modelling, in Pahl-Wostl, C., Schmidt, S., Rizzoli, A. E. and Jakeman, A. J. (eds), Complexity and Integrated Resources Management, 2nd Biennial Meeting of the International Environmental Modelling and Software Society, Volume 1, pp. 87–92. Osnabrück, Germany.

    Kingston, G. B., T. M. Heneker, M. F. Lambert, and H. R. Maier (2003), A comparison of conceptual and empirical modelling methods for simulation of catchment runoff, 28th Hydrology and Water Resources Symposium, Volume 2, pp. 299–306. Wollongong, Australia.

    Kingston, G. B., H. R. Maier, and M. F. Lambert (2003), Understanding the mechanisms modelled by artificial neural networks for hydrological prediction, in Post, D. A. (ed), MODSIM 2003 International Congress on Modelling and Simulation, Volume 2, pp. 825–830. Townsville, Australia.

    Kingston, G. B., M. F. Lambert, and H. R. Maier, (2003), Development of stochastic artificial neural networks for hydrological prediction, in Post, D. A. (ed), MODSIM 2003 International Congress on Modelling and Simulation, Volume 2, pp. 837–842. Townsville, Australia.

    Clark, T., G. B. Kingston, A. Frazer, A. Sanderson, and H. R. Maier (2002) Effectiveness of flow management in reducing the risk of cyanobacterial blooms in rivers. 27th Hydrology and Water Resources Symposium, The Institution of Engineers, Australia, Melbourne, Australia, Proceedings on CD-ROM.

    Journal Review

    Reviewer, Water Resources Research
    Reviewer, Journal of Hydrology
    Reviewer, Environmental Modelling and Software
    Reviewer, Journal of Environmental Management
    Reviewer, Journal of Hydraulic Engineering (ASCE)
    Reviewer, Journal of Computing in Civil Engineering (ASCE)

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Entry last updated: Friday, 22 Feb 2019

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