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Graeme Dandy
Graeme Dandy
Emeritus Professor of Civil and Environmental Engineering, University of Adelaide
Email confirmado em adelaide.edu.au
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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
29072000
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
10061994
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
9692010
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
7861996
An improved genetic algorithm for pipe network optimization
GC Dandy, AR Simpson, LJ Murphy
Water resources research 32 (2), 449-458, 1996
7381996
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
6762005
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
6272014
Review of input variable selection methods for artificial neural networks
R May, G Dandy, H Maier
Artificial neural networks-methodological advances and biomedical …, 2011
4692011
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
3972002
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
3552008
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
3002014
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
2861998
Data splitting for artificial neural networks using SOM-based stratified sampling
RJ May, HR Maier, GC Dandy
Neural Networks 23 (2), 283-294, 2010
2782010
Neural network based modelling of environmental variables: a systematic approach
HR Maier, GC Dandy
Mathematical and Computer Modelling 33 (6-7), 669-682, 2001
2682001
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
2592005
Estimating residential water demand in the presence of free allowances
G Dandy, T Nguyen, C Davies
Land Economics, 125-139, 1997
2491997
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
2371998
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
2342016
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
2252008
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
2192014
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