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Graeme Dandy
Graeme Dandy
Emeritus Professor of Civil and Environmental Engineering, University of Adelaide
Verified email at adelaide.edu.au
Title
Cited by
Cited by
Year
Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications
HR Maier, GC Dandy
Environmental modelling & software 15 (1), 101-124, 2000
29232000
Genetic algorithms compared to other techniques for pipe optimization
AR Simpson, GC Dandy, LJ Murphy
Journal of water resources planning and management 120 (4), 423-443, 1994
10121994
Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions
HR Maier, A Jain, GC Dandy, KP Sudheer
Environmental modelling & software 25 (8), 891-909, 2010
9792010
The use of artificial neural networks for the prediction of water quality parameters
HR Maier, GC Dandy
Water resources research 32 (4), 1013-1022, 1996
7931996
An improved genetic algorithm for pipe network optimization
GC Dandy, AR Simpson, LJ Murphy
Water resources research 32 (2), 449-458, 1996
7431996
Input determination for neural network models in water resources applications. Part 1—background and methodology
GJ Bowden, GC Dandy, HR Maier
Journal of Hydrology 301 (1-4), 75-92, 2005
6792005
Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions
HR Maier, Z Kapelan, J Kasprzyk, J Kollat, LS Matott, MC Cunha, ...
Environmental Modelling & Software 62, 271-299, 2014
6362014
Review of input variable selection methods for artificial neural networks
R May, G Dandy, H Maier
Artificial neural networks-methodological advances and biomedical …, 2011
4732011
Optimal division of data for neural network models in water resources applications
GJ Bowden, HR Maier, GC Dandy
Water resources research 38 (2), 2-1-2-11, 2002
4002002
Non-linear variable selection for artificial neural networks using partial mutual information
RJ May, HR Maier, GC Dandy, TMKG Fernando
Environmental Modelling & Software 23 (10-11), 1312-1326, 2008
3592008
Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling
W Wu, GC Dandy, HR Maier
Environmental Modelling & Software 54, 108-127, 2014
3102014
The effect of internal parameters and geometry on the performance of back-propagation neural networks: an empirical study
HR Maier, GC Dandy
Environmental Modelling & Software 13 (2), 193-209, 1998
2881998
Data splitting for artificial neural networks using SOM-based stratified sampling
RJ May, HR Maier, GC Dandy
Neural Networks 23 (2), 283-294, 2010
2832010
Neural network based modelling of environmental variables: a systematic approach
HR Maier, GC Dandy
Mathematical and Computer Modelling 33 (6-7), 669-682, 2001
2702001
Input determination for neural network models in water resources applications. Part 2. Case study: forecasting salinity in a river
GJ Bowden, HR Maier, GC Dandy
Journal of Hydrology 301 (1-4), 93-107, 2005
2612005
Estimating residential water demand in the presence of free allowances
G Dandy, T Nguyen, C Davies
Land Economics, 125-139, 1997
2491997
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 540, 623-640, 2016
2412016
Use of artificial neural networks for modelling cyanobacteria Anabaena spp. in the River Murray, South Australia
HR Maier, GC Dandy, MD Burch
Ecological Modelling 105 (2-3), 257-272, 1998
2391998
Application of partial mutual information variable selection to ANN forecasting of water quality in water distribution systems
RJ May, GC Dandy, HR Maier, JB Nixon
Environmental Modelling & Software 23 (10-11), 1289-1299, 2008
2262008
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
2232014
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