| Physics guided machine learning methods for hydrology A Khandelwal, S Xu, X Li, X Jia, M Stienbach, C Duffy, J Nieber, V Kumar arXiv preprint arXiv:2012.02854, 2020 | 63 | 2020 |
| Regionalization in a global hydrologic deep learning model: from physical descriptors to random vectors X Li, A Khandelwal, X Jia, K Cutler, R Ghosh, A Renganathan, S Xu, ... Water Resources Research 58 (8), e2021WR031794, 2022 | 30 | 2022 |
| Robust inverse framework using knowledge-guided self-supervised learning: An application to hydrology R Ghosh, A Renganathan, K Tayal, X Li, A Khandelwal, X Jia, C Duffy, ... Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 25 | 2022 |
| Physics-guided meta-learning method in baseflow prediction over large regions S Chen, Y Xie, X Li, X Liang, X Jia Proceedings of the 2023 SIAM International Conference on Data Mining (SDM …, 2023 | 12 | 2023 |
| Mini-Batch Learning Strategies for modeling long term temporal dependencies: a study in environmental applications S Xu, A Khandelwal, X Li, X Jia, L Liu, J Willard, R Ghosh, K Cutler, ... Proceedings of the 2023 SIAM International Conference on Data Mining (SDM …, 2023 | 6 | 2023 |
| Estimating lake water volume with regression and machine learning methods C Delaney, X Li, K Holmberg, B Wilson, A Heathcote, J Nieber Frontiers in Water 4, 886964, 2022 | 5 | 2022 |
| Probabilistic inverse modeling: An application in hydrology S Sharma, R Ghosh, A Renganathan, X Li, S Chatterjee, J Nieber, C Duffy, ... Proceedings of the 2023 SIAM International Conference on Data Mining (SDM …, 2023 | 4 | 2023 |
| Machine learning applications in vadose zone hydrology: A review X Li, JL Nieber, V Kumar Vadose Zone Journal, e20361, 2024 | 1 | 2024 |
| Realization of causal representation learning to adjust confounding bias in latent space J Li, X Li, X Jia, M Steinbach, V Kumar arXiv preprint arXiv:2211.08573, 2022 | 1 | 2022 |
| Uncertainty Quantification in Inverse Models in Hydrology SS Chatterjee, R Ghosh, A Renganathan, X Li, S Chatterjee, J Nieber, ... arXiv preprint arXiv:2310.02193, 2023 | | 2023 |
| Analysis of Groundwater Age Distributions in Complex Aquifer Systems to Evaluate Best Management Practice Efficacy P Margarit, J Nieber, J Magner, X Li, S Luzzi, K Holmberg, A Runkel, ... AGU Fall Meeting Abstracts 2021, H15A-1038, 2021 | | 2021 |
| Are Long short-term memory (LSTM) model simulations of watershed discharge improved when water storage is included as input? The Case Study at Rum River Watershed, MN PF Teng, J Nieber, X Li, C Regan, C Duffy, M Steinbach, V Kumar AGU Fall Meeting Abstracts 2021, H33J-11, 2021 | | 2021 |
| Effectiveness of Basin Aware Modulation in a Global Hydrologic Deep Learning Model: from Physical Descriptors to Random Vectors X Li, A Khandelwal, R Ghosh, A Renganathan, J Nieber, C Duffy, ... AGU Fall Meeting Abstracts 2021, H22G-08, 2021 | | 2021 |
| Source Aware Modulation for leveraging limited data from heterogeneous sources X Li, A Khandelwal, R Ghosh, A Renganathan, J Willard, S Xu, X Jia, ... | | 2021 |
| Physics Guided Deep Learning Models for Hydrology X Li, A Khandelwal, S Xu, JL Nieber, V Kumar, C Duffy, M Steinbach, ... AGU Fall Meeting Abstracts 2020, H049-01, 2020 | | 2020 |
| Regionalization in a global hydrologic deep learning X Li, A Khandelwal, X Jia, K Cutler, R Ghosh, A Renganathan, S Xu, ... | | |