Greer Humphrey

Adelaide Medical School

Faculty of Health and Medical Sciences

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.

Career
Publications
Contact

Appointments

Date	Position	Institution name
2019 - ongoing	Biostatistician	University of Adelaide
2011 - 2016	Post-doctoral Research Fellow	University of Adelaide
2009 - 2011	Senior Catastrophe Risk Modeller	Risk Management Solutions (United Kingdom)
2007 - 2009	Engineer, Flood Management	HR Wallingford
2006 - 2009	Post-doctoral Research Fellow	University of Adelaide

Education

Date	Institution name	Country	Title
2016 - 2018	University of Adelaide	Australia	Master of Biostatistics (Star Graduate)
2002 - 2006	University of Adelaide	Australia	PhD, Water Resources Engineering
1997 - 2000	University of Adelaide	Australia	Bachelor of Civil and Environmental Engineering (1st class hons.)

Research Interests

Child and Adolescent Health Biostatistics

Journals

Year	Citation
2024	Humphrey, G. B., Inacio, M. C., Lang, C., Churches, O. F., Sluggett, J. K., Williams, H., . . . Caughey, G. E. (2024). Estimating potential palliative care needs for residential aged care: A population-based retrospective cohort study. Australasian Journal on Ageing, 1-10. DOI
2020	Kelly, T. -L., Humphrey, G., Miller, C., Bowden, J. A., Dono, J., & Phillips, P. A. (2020). Clustering of age standardised COVID-19 infection fatality ratios and death trajectories. DOI
2018	Gibbs, M. S., McInerney, D., Humphrey, G., Thyer, M. A., Maier, H. R., Dandy, G. C., & 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, 22(1), 871-887. DOI Scopus33 WoS27
2017	Humphrey, G., Maier, H., Wu, W., Mount, N., Dandy, G., Abrahart, R., & Dawson, C. (2017). Improved validation framework and R-package for artificial neural network models. Environmental Modelling and Software, 92, 82-106. DOI Scopus53 WoS45
2016	Humphrey, G., Gibbs, M., Dandy, G., & 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 Scopus198 WoS155
2014	Galelli, S., Humphrey, G., Maier, H., Castelletti, A., Dandy, G., & Gibbs, M. (2014). An evaluation framework for input variable selection algorithms for environmental data-driven models. Environmental Modelling & Software, 62, 33-51. DOI Scopus174 WoS147
2011	Kingston, G. B., Rajabalinejad, M., Gouldby, B. P., & Van Gelder, P. H. A. J. M. (2011). Computational intelligence methods for the efficient reliability analysis of complex flood defence structures. Structural Safety, 33(1), 64-73. DOI Scopus25 WoS20
2008	Humphrey, G., Maier, H., & Lambert, M. (2008). Bayesian model selection applied to artificial neural networks used for water resources modeling. Water Resources Research, 44(4), 04419-01-04419-12. DOI Scopus35 WoS29
2006	Humphrey, G., Maier, H., & Lambert, M. (2006). A probabilistic method for assisting knowledge extraction from artificial neural networks used for hydrological prediction. Mathematical and Computer Modelling, 44(5-6), 499-512. DOI Scopus32 WoS24
2005	Humphrey, G., Lambert, M., & Maier, H. (2005). Bayesian training of artificial neural networks used for water resources modeling. Water Resources Research, 41(12), 12409-12419. DOI Scopus98 WoS83
2005	Humphrey, G., Maier, H., & Lambert, M. (2005). Calibration and validation of neural networks to ensure physically plausible hydrological modeling. Journal of Hydrology, 314(1-4), 158-176. DOI Scopus63 WoS52
2004	Maier, H., Humphrey, G., Clark, T., Frazer, A., & Sanderson, A. (2004). 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 Scopus28 WoS25

Books

Year Citation

2010 Humphrey, G., Maier, H., & Lambert, M. (2010). Bayesian artificial neural networks : with applications water resources engineering. USA: Verlag Dr. Muller.

Year	Citation
2010	Humphrey, G., Maier, H., & Lambert, M. (2010). Bayesian artificial neural networks : with applications water resources engineering. USA: Verlag Dr. Muller.

Book Chapters

Year	Citation
2008	Humphrey, G., Dandy, G., & Maier, H. (2008). AI techniques for hydrological Modeling and mangement. II: Optimization. In L. Robinson (Ed.), Water Resources Research Progress (pp. 67-99). New York: Nova Science Publishers. Scopus2
2008	Humphrey, G., Maier, H., & Dandy, G. (2008). AI Techniques for hydrological Modelling and Management. I: Simulation. In L. Robinson (Ed.), Water Resources Research Progress (pp. 15-65). New York: Nova Science Publishers.
2008	Kingston, G. B., Maier, H. R., & Dandy, G. C. (2008). AI techniques for hydrological modeling and management. I: Simulation. In L. N. Robinson (Ed.), Water Resources Research Progress (pp. 13-66). Nova Science Publishers, Inc.. Scopus2
2008	van Gelder, P., Buijs, F., ter Horst, W., Kanning, W., Van, C., Rajabalinejad, M., . . . Lambrecht, H. -J. (2008). Reliability analysis of flood defence structures and systems in Europe. In Flood Risk Management: Research and Practice (pp. 603-611). CRC Press. DOI

Conference Papers

Year	Citation
2021	Humphrey, G., Maier, H., Wu, W., Mount, N., Dandy, G., & Dawson, C. (2021). Beyond validation: assessing the legitimacy of artificial neural network models. In Proceedings of the 24th International Congress on Modelling and Simulation (MODSIM2021) (pp. 85-91). Canberra, ACT, Australia: Modelling and Simulation Society of Australia and New Zealand. DOI
2020	Kingston, G., Maier, H., & Lambert, M. (2020). A Bayesian approach to artificial neural network model selection. In MODSIM 2005 - International Congress on Modelling and Simulation: Advances and Applications for Management and Decision Making, Proceedings (pp. 1853-1859).
2014	Humphrey, G., Galelli, S., Maier, H., Castelletti, A., Dandy, G., & Gibbs, M. (2014). A new evaluation framework for input variable selection algorithms used in environmental modelling. In J. Piantadosi, R. Anderssen, & B. J (Eds.), MODSIM2013, 20th International Congress on Modelling and Simulation Vol. 3 (pp. 1623-1630). online: International Environmental Modelling and Software Society. Scopus2
2014	humphrey, G., galelli, S., castelletti, A., maier, H., dandy, G., & Gibbs, M. (2014). Automatic input selection for hydrological modelling: a comparative analysis. In EGU General Assembly Conference Abstracts, 2014 Vol. 16 (pp. 2621 pages). Vienna (Austria).
2006	Kingston, G., Maier, H., & Lambert, M. (2006). Forecasting cyanobacteria with Bayesian and deterministic artificial neural networks. In G. Yen (Ed.), Proceedings of the 2006 IEEE World Congress on Computational Intelligence (pp. 4870-4877). CDROM: IEEE. DOI Scopus5 WoS2
2005	Humphrey, G., Maier, H., & Lambert, M. (2005). A Bayesian method to improve the extrapolation ability of ANNs. In M. Hamza (Ed.), Proceedings of the 14th International Conference on Applied Simulation and Modelling (pp. 126-131). Anaheim, CA, USA: ACTA Press.
2005	Humphrey, G., Maier, H., & Lambert, M. (2005). A Bayesian approach to artificial neural network model selection. In A. Zerger, & R. Argent (Eds.), Proceedings of the International Congress on Modelling and Simulation (pp. 1853-1859). http://mssanz.org.au/modsim05/authorsH-K.htm: Modelling and Simulation Society of Australia and New Zealand Inc.. Scopus6
2004	Humphrey, G., Maier, H., & Lambert, M. (2004). A statistical input pruning method for artificial neural networks used in environmental modelling. In C. Pahl Wostl, S. Schmidt, A. Rizzoli, & A. Jakeman (Eds.), Proceedings of the International Environmental Modelling & Software Society iEMSs 2004 International Conference (pp. CD-ROM 87-CD-ROM 92). CD-ROM: iEMSs 2004.
2003	Humphrey, G., Heneker, T., Lambert, M., & Maier, H. (2003). A comparison of conceptual and empirical modelling methods for simulation of catchment runoff. In M. Boyd, J. Ball, M. Babister, & J. Green (Eds.), Symposium Proceedings (pp. 299-306). 11 National Circuit, Barton, ACT: Institution of Engineers Australia.
2003	Humphrey, G., Lambert, M., & Maier, H. (2003). Development of stochastic artificial neural networks for hydrological prediction. In D. Post (Ed.), Proceedings of MODSIM 2003 International Congress on Modelling and Simulation (pp. 837-842). IAS, ANU, Canberra: The Modelling and Simulation Soc of Aust and NZ Inc. WoS1
2003	Humphrey, G., Maier, H., & Lambert, M. (2003). Understanding the mechanisms modelled by artificial neural networks for hydrological prediction. In D. Post (Ed.), Proceedings of MODSIM 2003 International Congress on Modelling and Simulation (pp. 825-830). IAS, ANU, Canberra: The Modelling and Simulation Soc of Aust and NZ Inc. WoS3
2002	Clark, T., Humphrey, G., Frazer, A., Sanderson, A., & Maier, H. (2002). Effectiveness of flow management in reducing the risk of cyanobacterial blooms in rivers. In David Sheehan (Ed.), Hydrology and Water Resources Symposium 2002 (pp. CDROM 1-CDROM 8). CDROM: Institution of Engineers.

Email: greer.humphrey@adelaide.edu.au

Greer Humphrey

Appointments

Education

Research Interests

Journals

Books

Book Chapters

Conference Papers

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