| Model induction with support vector machines: introduction and applications YB Dibike, S Velickov, D Solomatine, MB Abbott Journal of Computing in Civil Engineering 15 (3), 208-216, 2001 | 560 | 2001 |
| River flow prediction using artificial neural networks: generalisation beyond the calibration range CE Imrie, S Durucan, A Korre Journal of hydrology 233 (1-4), 138-153, 2000 | 474 | 2000 |
| 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 | 461 | 2014 |
| Data-driven modelling: some past experiences and new approaches DP Solomatine, A Ostfeld Journal of hydroinformatics 10 (1), 3-22, 2008 | 434 | 2008 |
| Model trees as an alternative to neural networks in rainfall—runoff modelling PS DIMITRI, ND KHADA Hydrological Sciences Journal 48 (3), 399-411, 2003 | 415* | 2003 |
| Neural networks and M5 model trees in modelling water level–discharge relationship B Bhattacharya, DP Solomatine Neurocomputing 63, 381-396, 2005 | 309 | 2005 |
| M5 model trees and neural networks: application to flood forecasting in the upper reach of the Huai River in China DP Solomatine, Y Xue Journal of Hydrologic Engineering 9 (6), 491-501, 2004 | 295 | 2004 |
| Machine learning approaches for estimation of prediction interval for the model output DL Shrestha, DP Solomatine Neural Networks 19 (2), 225-235, 2006 | 280 | 2006 |
| River flow forecasting using artificial neural networks YB Dibike, DP Solomatine Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere …, 2001 | 268 | 2001 |
| Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting RJ Abrahart, F Anctil, P Coulibaly, CW Dawson, NJ Mount, LM See, ... Progress in Physical Geography 36 (4), 480-513, 2012 | 236 | 2012 |
| A framework for uncertainty analysis in flood risk management decisions J Hall, D Solomatine International Journal of River Basin Management 6 (2), 85-98, 2008 | 226 | 2008 |
| Machine learning approach to modeling sediment transport B Bhattacharya, RK Price, DP Solomatine Journal of Hydraulic Engineering 133 (4), 440-450, 2007 | 211 | 2007 |
| AdaBoost. RT: a boosting algorithm for regression problems DP Solomatine, DL Shrestha 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No …, 2004 | 211 | 2004 |
| Data-driven modelling: concepts, approaches and experiences D Solomatine, LM See, RJ Abrahart Practical hydroinformatics, 17-30, 2009 | 209 | 2009 |
| Experiments with AdaBoost. RT, an improved boosting scheme for regression DL Shrestha, DP Solomatine Neural computation 18 (7), 1678-1710, 2006 | 202 | 2006 |
| A novel method to estimate model uncertainty using machine learning techniques DP Solomatine, DL Shrestha Water Resources Research 45 (12), 2009 | 180 | 2009 |
| Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology-Part 1: Concepts and methodology A Elshorbagy, G Corzo, S Srinivasulu, DP Solomatine Hydrology and Earth System Sciences 14 (10), 1931-1941, 2010 | 161 | 2010 |
| River cross-section extraction from the ASTER global DEM for flood modeling TZ Gichamo, I Popescu, A Jonoski, D Solomatine Environmental Modelling & Software 31, 37-46, 2012 | 150 | 2012 |
| Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology-Part 2: Application A Elshorbagy, G Corzo, S Srinivasulu, DP Solomatine Hydrology and Earth System Sciences 14 (10), 1943-1961, 2010 | 139 | 2010 |
| On the encapsulation of numerical-hydraulic models in artificial neural network YB Dibike, D Solomatine, MB Abbott Journal of Hydraulic research 37 (2), 147-161, 1999 | 138 | 1999 |
