Dimitri Solomatine
Dimitri Solomatine
UNESCO-IHE Institute fro Water Education
Verified email at unesco-ihe.org
Title
Cited by
Cited by
Year
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
5122001
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
4452000
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
4212014
Data-driven modelling: some past experiences and new approaches
DP Solomatine, A Ostfeld
Journal of hydroinformatics 10 (1), 3-22, 2008
3922008
Model trees as an alternative to neural networks in rainfall—runoff modelling
PS DIMITRI, ND KHADA
Hydrological Sciences Journal 48 (3), 399-411, 2003
389*2003
Neural networks and M5 model trees in modelling water level–discharge relationship
B Bhattacharya, DP Solomatine
Neurocomputing 63, 381-396, 2005
2852005
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
2782004
River flow forecasting using artificial neural networks
YB Dibike, DP Solomatine
Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere …, 2001
2442001
Machine learning approaches for estimation of prediction interval for the model output
DL Shrestha, DP Solomatine
Neural Networks 19 (2), 225-235, 2006
2362006
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
2202012
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
2142008
AdaBoost. RT: a boosting algorithm for regression problems
DP Solomatine, DL Shrestha
2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No …, 2004
1842004
Experiments with AdaBoost. RT, an improved boosting scheme for regression
DL Shrestha, DP Solomatine
Neural computation 18 (7), 1678-1710, 2006
1822006
Data-driven modelling: concepts, approaches and experiences
D Solomatine, LM See, RJ Abrahart
Practical hydroinformatics, 17-30, 2009
1792009
Machine learning approach to modeling sediment transport
B Bhattacharya, RK Price, DP Solomatine
Journal of Hydraulic Engineering 133 (4), 440-450, 2007
1772007
A novel method to estimate model uncertainty using machine learning techniques
DP Solomatine, DL Shrestha
Water Resources Research 45 (12), 2009
1642009
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, 2010
1482010
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
1352012
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, 2010
1352010
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
1321999
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