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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
6312001
Data-driven modelling: some past experiences and new approaches
DP Solomatine, A Ostfeld
Journal of hydroinformatics 10 (1), 3-22, 2008
5502008
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
5292014
Model trees as an alternative to neural networks in rainfall—runoff modelling
PS DIMITRI, ND KHADA
Hydrological Sciences Journal 48 (3), 399-411, 2003
452*2003
Neural networks and M5 model trees in modelling water level–discharge relationship
B Bhattacharya, DP Solomatine
Neurocomputing 63, 381-396, 2005
3332005
Machine learning approaches for estimation of prediction interval for the model output
DL Shrestha, DP Solomatine
Neural networks 19 (2), 225-235, 2006
3312006
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
3242004
River flow forecasting using artificial neural networks
YB Dibike, DP Solomatine
Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere …, 2001
2972001
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
2732012
Data-driven modelling: concepts, approaches and experiences
D Solomatine, LM See, RJ Abrahart
Practical hydroinformatics, 17-30, 2009
2582009
AdaBoost. RT: a boosting algorithm for regression problems
DP Solomatine, DL Shrestha
2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No …, 2004
2582004
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
2532008
Experiments with AdaBoost. RT, an improved boosting scheme for regression
DL Shrestha, DP Solomatine
Neural computation 18 (7), 1678-1710, 2006
2232006
A novel method to estimate model uncertainty using machine learning techniques
DP Solomatine, DL Shrestha
Water Resources Research 45 (12), 2009
2122009
Machine learning approach to modeling sediment transport
B Bhattacharya, RK Price, DP Solomatine
Journal of Hydraulic Engineering 133 (4), 440-450, 2007
2122007
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
1772010
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
1762012
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
1502010
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
1501999
Machine learning in soil classification
B Bhattacharya, DP Solomatine
Neural networks 19 (2), 186-195, 2006
1462006
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