Dr Greer Humphrey
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Biography/ Background
Greer Humphrey is a biostatistician working in the BetterStart Child Health and Development research group within the School of Public Health. She has previous experience working as a statistical modeller in the fields of water engineering and catastrophe risk. Her current research focus involves using linked data sources to analyse contact with the child protection system and the associated risk factors and outcomes.
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Qualifications
- 2018 Master of Biostatistics, The University of Adelaide, South Australia. Star Graduate.
- 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.
- 2018 Master of Biostatistics, The University of Adelaide, South Australia. Star Graduate.
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Research Interests
The development and application of statistical modelling methods, particularly those with roots in data mining and artificial intelligence, in problems related to child health and development. Specific areas of interest include:
- risk prediction
- Bayesian statistics
- artificial neural networks
- child protection system involvement
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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
Gibbs, M., McInerney, D., Humphrey, G., Thyer, M., Maier, H., Dandy, G., and Kavetski, D. (2018). State updating and calibration period selection to improve dynamic monthly streamflow forecasts for an environmental flow management application. Hydrology and Earth System Sciences Discussions, 22(1), 871-887, doi:10.5194/hess-22-871-2018.
Improved validation framework and R-package for artificial neural network models. Environmental Modelling and Software, 92, 82-106, doi:10.1016/j.envsoft.2017.01.023
Humphrey, G., Gibbs, M., Dandy, G., and Maier, H. (2016). A hybrid approach to monthly streamflow forecasting: integrating hydrological model outputs into a Bayesian artificial neural network. Journal of Hydrology, 540, 623-640, doi:10.1016/j.jhydrol.2016.06.026
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 models, Environmental 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.
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Entry last updated: Tuesday, 16 Apr 2019