Durga Lal Shrestha
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Machine learning approaches for estimation of prediction interval for the model output
DL Shrestha, DP Solomatine
Neural Networks 19 (2), 225-235, 2006
AdaBoost. RT: a boosting algorithm for regression problems
DP Solomatine, DL Shrestha
2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No …, 2004
Experiments with AdaBoost. RT, an improved boosting scheme for regression
DL Shrestha, DP Solomatine
Neural computation 18 (7), 1678-1710, 2006
A novel method to estimate model uncertainty using machine learning techniques
DP Solomatine, DL Shrestha
Water Resources Research 45 (12), 2009
A log‐sinh transformation for data normalization and variance stabilization
QJ Wang, DL Shrestha, DE Robertson, P Pokhrel
Water Resources Research 48 (5), 2012
A novel approach to parameter uncertainty analysis of hydrological models using neural networks
DL Shrestha, N Kayastha, DP Solomatine
Hydrology and Earth System Sciences 13 (7), 1235, 2009
Ensemble dressing for hydrological applications
TC Pagano, DL Shrestha, QJ Wang, D Robertson, P Hapuarachchi
Hydrological Processes 27 (1), 106-116, 2013
Post processing rainfall forecasts from numerical weather prediction models for short term streamflow forecasting.
DE Robertson, DL Shrestha, QJ Wang
Hydrology & Earth System Sciences Discussions 10 (5), 2013
Instance‐based learning compared to other data‐driven methods in hydrological forecasting
DP Solomatine, M Maskey, DL Shrestha
Hydrological Processes: An International Journal 22 (2), 275-287, 2008
Data‐driven approaches for estimating uncertainty in rainfall‐runoff modelling
DL Shrestha, DP Solomatine
International Journal of River Basin Management 6 (2), 109-122, 2008
Evaluation of numerical weather prediction model precipitation forecasts for short-term streamflow forecasting purpose
DL Shrestha, DE Robertson, QJ Wang, TC Pagano, HAP Hapuarachchi
Hydrology and Earth System Sciences 17 (5), 1913, 2013
A System for Continuous Hydrological Ensemble Forecasting (SCHEF) to lead times of 9 days
JC Bennett, DE Robertson, DL Shrestha, QJ Wang, D Enever, ...
Journal of Hydrology 519, 2832-2846, 2014
Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments
N Dogulu, PL López, DP Solomatine, AH Weerts, DL Shrestha
Hydrology and Earth System Sciences 19 (7), 3181-3201, 2015
Improving precipitation forecasts by generating ensembles through postprocessing
DL Shrestha, DE Robertson, JC Bennett, QJ Wang
Monthly Weather Review 143 (9), 3642-3663, 2015
Neural networks in reconstructing missing wave data in sedimentation modelling
B Bhattacharya, DL Shrestha, DP Solomatine
Proceedings of the XXXth IAHR Congress 500, 770-778, 2003
Uncertainty analysis in rainfall-runoff modelling: Application of machine learning techniques
DL Shrestha
IHE Delft Institute for Water Education, 2009
Encapsulation of parametric uncertainty statistics by various predictive machine learning models: MLUE method
DL Shrestha, N Kayastha, D Solomatine, R Price
Journal of Hydroinformatics 16 (1), 95-113, 2014
Eager and lazy learning methods in the context of hydrologic forecasting
DP Solomatine, M Maskey, DL Shrestha
The 2006 IEEE International Joint Conference on Neural Network Proceedings …, 2006
Evaluation of ensemble precipitation forecasts generated through post-processing in a Canadian catchment
SK Jha, DL Shrestha, T Stadnyk, P Coulibaly
Hydrology and Earth System Sciences 22 (3), 1957-1969, 2018
Assessing model prediction limits using fuzzy clustering and machine learning
DL Shrestha, J Rodriguez, RK Price, DP Solomatine
Proc. 7th Int. Conf. On Hydroinformatics, 4-8, 2006
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