| Hydrologically informed machine learning for rainfall-runoff modelling: towards distributed modelling HMVV Herath, J Chadalawada, V Babovic Hydrology and Earth System Sciences Discussions 2020, 1-42, 2020 | 89 | 2020 |
| Hydrologically informed machine learning for rainfall‐runoff modeling: A genetic programming‐based toolkit for automatic model induction J Chadalawada, H Herath, V Babovic Water Resources Research 56 (4), e2019WR026933, 2020 | 83 | 2020 |
| Genetic programming for hydrological applications: to model or to forecast that is the question HMVV Herath, J Chadalawada, V Babovic Journal of Hydroinformatics 23 (4), 740-763, 2021 | 27 | 2021 |
| Identification of Dominant Runoff Controls Using Hydrologically Informed Machine Learning Approach HMVV Herath, J Chadalawada, V Babovic | 1 | 2021 |
| WRF weather downscaling model coupled with runoff model for anticipatory flood water management of the Polgolla Barrage H Herath, R Dayananda, M Madakumbura, SB Weerakoon METHODOLOGY 3, 4, 2016 | 1 | 2016 |
| CLIMATE CHANGE IMPACT PREDICTION IN UPPER MAHAWELI BASIN H Herath, R Dayananda, SB Weerakoon Sc. Research Report, University of Peradeniya, 2015 | 1 | 2015 |
| Hydrologically Informed Machine Learning for Rainfall-Runoff Modelling HMVV Herath PQDT-Global, 2021 | | 2021 |
| Physics Informed Machine Learning of Rainfall-Runoff Processes V Babovic, J Chadalawada, H Mudiyanselage Viraj Vidura Herath EGU General Assembly Conference Abstracts, 12303, 2020 | | 2020 |
JAYASHREE CHADALAWADARESEARCH FELLOW, NATIONAL UNIVERSITY OF SINGAPOREVerified email at u.nus.edu
