| 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 | 92 | 2020 |
| Hydrologically informed machine learning for rainfall–runoff modelling: towards distributed modelling HMVV Herath, J Chadalawada, V Babovic Hydrology and Earth System Sciences 25 (8), 4373-4401, 2021 | 89 | 2021 |
| Effect of randomly distributed geofibers on the piping behaviour of embankments constructed with fly ash as a fill material A Das, C Jayashree, BVS Viswanadham Geotextiles and Geomembranes 27 (5), 341-349, 2009 | 66 | 2009 |
| Review and comparison of performance indices for automatic model induction J Chadalawada, V Babovic Journal of Hydroinformatics 21 (1), 13-31, 2019 | 48 | 2019 |
| A genetic programming approach to system identification of rainfall-runoff models J Chadalawada, V Havlicek, V Babovic Water Resources Management 31, 3975-3992, 2017 | 48 | 2017 |
| 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 | 32 | 2021 |
| Predictors of academic performance of medical undergraduate students of microbiology class in Kolkata SS Roy, J Chadalawada International Journal of Medicine and Public Health 4 (4), 2014 | 29 | 2014 |
| 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 | 20 | 2020 |
| Uncertainty matters: Bayesian probabilistic forecasting for residential smart meter prediction, segmentation, and behavioral measurement and verification J Roth, J Chadalawada, RK Jain, C Miller Energies 14 (5), 1481, 2021 | 15 | 2021 |
| Rainfall–runoff modeling based on genetic programming V Babovic, X Li, J Chadalawada, P Maurice Encyclopedia of Water: Science, Technology, and Society 5, 1081-1096, 2017 | 6 | 2017 |
| Assessment of Earth Observation data content based on data compression-application to settlements understanding J Chadalawada, D Espinoza-Molina, M Datcu 2012 IEEE International Geoscience and Remote Sensing Symposium, 6130-6133, 2012 | 6 | 2012 |
| SAR image content retrieval by speckle robust compression based methods D Espinoza-Molina, J Chadalawada, M Datcu EUSAR 2014; 10th European Conference on Synthetic Aperture Radar, 1-4, 2014 | 3 | 2014 |
| Identification of Dominant Runoff Controls Using Hydrologically Informed Machine Learning Approach HMVV Herath, J Chadalawada, V Babovic | 1 | 2021 |
| Genetic programming for automatic hydrological modelling J Chadalawada, V Babovic EGU general assembly conference abstracts, 15990, 2017 | 1 | 2017 |
| 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 |
| Data Driven Modelling and Knowledge Discovery in Water Resources Engineering J Chadalawada PQDT-Global, 2017 | | 2017 |
| Towards improving the knowledge of underlying mechanisms of Rainfall-Runoff process using Genetic Programming J Chadalawada, V Babovic EGU General Assembly Conference Abstracts, EPSC2016-5457, 2016 | | 2016 |
Vladan BabovicDepartment of Civil and Environmental Engineering, National University Singapore在 nus.edu.sg 的电子邮件经过验证
