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Greer Humphrey (nee Kingston)
Greer Humphrey (nee Kingston)
Biostatistician, University of Adelaide
Verified email at adelaide.edu.au
Title
Cited by
Cited by
Year
An evaluation framework for input variable selection algorithms for environmental data-driven models
S Galelli, GB Humphrey, HR Maier, A Castelletti, GC Dandy, MS Gibbs
Environmental Modelling & Software 62, 33-51, 2014
1982014
A hybrid approach to monthly streamflow forecasting: Integrating hydrological model outputs into a Bayesian artificial neural network.
GB Humphrey, MS Gibbs, GC Dandy, HR Maier
Journal of Hydrology, 2016
1772016
Bayesian training of artificial neural networks used for water resources modeling
GB Kingston, MF Lambert, HR Maier
Water resources research 41 (12), W12409, 2005
1252005
Calibration and validation of neural networks to ensure physically plausible hydrological modeling
GB Kingston, HR Maier, MF Lambert
Journal of Hydrology 314 (1), 158-176, 2005
822005
Improved validation framework and R-package for artificial neural network models
GB Humphrey, HR Maier, W Wu, NJ Mount, GC Dandy, RJ Abrahart, ...
Environmental Modelling & Software 92, 82-106, 2017
552017
A probabilistic method for assisting knowledge extraction from artificial neural networks used for hydrological prediction
GB Kingston, HR Maier, MF Lambert
Mathematical and computer modelling 44 (5), 499-512, 2006
432006
Bayesian model selection applied to artificial neural networks used for water resources modeling
GB Kingston, HR Maier, MF Lambert
Water Resources Research 44 (4), W04419, 2008
412008
Risk‐based approach for assessing the effectiveness of flow management in controlling cyanobacterial blooms in rivers
HR Maier, GB Kingston, T Clark, A Frazer, A Sanderson
River Research and Applications 20 (4), 459-471, 2004
382004
Computational intelligence methods for the efficient reliability analysis of complex flood defence structures
GB Kingston, M Rajabalinejad, BP Gouldby, PH Van Gelder
Structural Safety 33 (1), 64-73, 2011
352011
Bayesian artificial neural networks in water resources engineering.
GB Kingston
282006
Reliability analysis of flood defence structures and systems in Europe
P van Gelder, F Buijs, W ter Horst, W Kanning, CM Van, M Rajabalinejad, ...
Flood risk management: Research and practice. Leiden, the Netherlands: CRC …, 2009
192009
Reliability analysis of flood defence structures and systems in Europe
P Gelder, F Buijs, W Horst, W Kanning, CM Van, M Rajabalinejad, E Boer, ...
CRC Press, 2008
19*2008
Review of artificial intelligence techniques and their applications to hydrological modeling and water resources management Part 2–optimization
GB Kingston, GC Dandy, HR Maier
Water Resources Research Progress, 67-99, 2008
112008
A statistical input pruning method for artificial neural networks used in environmental modelling
GB Kingston, HR Maier, MF Lambert
iEMSs 2004: Biennial Meeting of the International Environmental Modelling …, 2004
112004
A Bayesian approach to artificial neural network model selection
GB Kingston, HR Maier, MF Lambert
International Congress on Modelling and Simulation (2005: Melbourne, Vic …, 2005
102005
Uncertainty and sensitivity analysis method for flood risk analysis
B Gouldby, G Kingston
FLOODsite Report, 2007
92007
A new evaluation framework for input variable selection algorithms used in environmental modelling
GB Humphrey, S Galelli, A Castelletti, HR Maier, GC Dandy, MS Gibbs
7th International Congress on Environmental Modelling and Software: San …, 2014
72014
Reliable prediction of wave overtopping volumes using Bayesian neural networks
GB Kingston, DI Robinson, BP Gouldby, T Pullen
Flood Risk Management: Research and Practice: Extended Abstracts Volume (332 …, 2008
62008
Understanding the mechanisms modelled by artificial neural networks for hydrological prediction
GB Kingston, HR Maier, MF Lambert
International Congress on Modelling and Simulation (15th: 2003: Townsville …, 2003
62003
Development of stochastic Artificial Neural Networks for hydrological prediction
GB Kingston, MF Lambert, HR Maier
Proceedings for Modsim, International Congress on Modelling and Simulation …, 2003
62003
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