| 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 | 2903 | 2000 |
| 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 | 1006 | 1994 |
| 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 | 968 | 2010 |
| 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 | 786 | 1996 |
| An improved genetic algorithm for pipe network optimization GC Dandy, AR Simpson, LJ Murphy Water resources research 32 (2), 449-458, 1996 | 738 | 1996 |
| 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 | 676 | 2005 |
| 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 | 626 | 2014 |
| Review of input variable selection methods for artificial neural networks R May, G Dandy, H Maier Artificial neural networks-methodological advances and biomedical …, 2011 | 469 | 2011 |
| 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 | 397 | 2002 |
| 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 | 355 | 2008 |
| 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 | 300 | 2014 |
| 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 | 286 | 1998 |
| Data splitting for artificial neural networks using SOM-based stratified sampling RJ May, HR Maier, GC Dandy Neural Networks 23 (2), 283-294, 2010 | 278 | 2010 |
| Neural network based modelling of environmental variables: a systematic approach HR Maier, GC Dandy Mathematical and Computer Modelling 33 (6-7), 669-682, 2001 | 268 | 2001 |
| 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 | 259 | 2005 |
| Estimating residential water demand in the presence of free allowances G Dandy, T Nguyen, C Davies Land Economics, 125-139, 1997 | 249 | 1997 |
| 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 | 237 | 1998 |
| 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 | 233 | 2016 |
| 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 | 224 | 2008 |
| 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 | 219 | 2014 |
