| Data-driven modelling: some past experiences and new approaches DP Solomatine, A Ostfeld Journal of hydroinformatics 10 (1), 3-22, 2008 | 773 | 2008 |
| 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 | 760 | 2001 |
| 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 | 664 | 2014 |
| Model trees as an alternative to neural networks in rainfall—runoff modelling PS DIMITRI, ND KHADA Hydrological Sciences Journal 48 (3), 399-411, 2003 | 499* | 2003 |
| Machine learning approaches for estimation of prediction interval for the model output DL Shrestha, DP Solomatine Neural networks 19 (2), 225-235, 2006 | 450 | 2006 |
| Data-driven modelling: concepts, approaches and experiences D Solomatine, LM See, RJ Abrahart Practical hydroinformatics: Computational intelligence and technological …, 2008 | 405 | 2008 |
| Neural networks and M5 model trees in modelling water level–discharge relationship B Bhattacharya, DP Solomatine Neurocomputing 63, 381-396, 2005 | 399 | 2005 |
| AdaBoost. RT: a boosting algorithm for regression problems DP Solomatine, DL Shrestha 2004 IEEE international joint conference on neural networks (IEEE Cat. No …, 2004 | 397 | 2004 |
| 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 | 394 | 2004 |
| River flow forecasting using artificial neural networks YB Dibike, DP Solomatine Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere …, 2001 | 342 | 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 | 338 | 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 | 300 | 2008 |
| Experiments with AdaBoost. RT, an improved boosting scheme for regression DL Shrestha, DP Solomatine Neural computation 18 (7), 1678-1710, 2006 | 281 | 2006 |
| A novel method to estimate model uncertainty using machine learning techniques DP Solomatine, DL Shrestha Water Resources Research 45 (12), 2009 | 275 | 2009 |
| Machine learning approach to modeling sediment transport B Bhattacharya, RK Price, DP Solomatine Journal of Hydraulic Engineering 133 (4), 440-450, 2007 | 259 | 2007 |
| Machine learning in soil classification B Bhattacharya, DP Solomatine Neural networks 19 (2), 186-195, 2006 | 229 | 2006 |
| 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 | 215 | 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 | 205 | 2012 |
| Comparison of the performance of six drought indices in characterizing historical drought for the upper Blue Nile basin, Ethiopia Y Bayissa, S Maskey, T Tadesse, SJ Van Andel, S Moges, ... Geosciences 8 (3), 81, 2018 | 180 | 2018 |
| A review of low‐cost space‐borne data for flood modelling: topography, flood extent and water level K Yan, G Di Baldassarre, DP Solomatine, GJP Schumann Hydrological processes 29 (15), 3368-3387, 2015 | 174 | 2015 |
