| Enhancing streamflow forecast and extracting insights using long‐short term memory networks with data integration at continental scales D Feng, K Fang, C Shen Water Resources Research 56 (9), e2019WR026793, 2020 | 237 | 2020 |
| From hydrometeorology to river water quality: can a deep learning model predict dissolved oxygen at the continental scale? W Zhi, D Feng, WP Tsai, G Sterle, A Harpold, C Shen, L Li Environmental science & technology 55 (4), 2357-2368, 2021 | 149 | 2021 |
| From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling WP Tsai, D Feng, M Pan, H Beck, K Lawson, Y Yang, J Liu, C Shen Nature communications 12 (1), 5988, 2021 | 117 | 2021 |
| Transferring hydrologic data across continents–leveraging data‐rich regions to improve hydrologic prediction in data‐sparse regions K Ma, D Feng, K Lawson, WP Tsai, C Liang, X Huang, A Sharma, C Shen Water Resources Research 57 (5), e2020WR028600, 2021 | 75 | 2021 |
| Differentiable modelling to unify machine learning and physical models for geosciences C Shen, AP Appling, P Gentine, T Bandai, H Gupta, A Tartakovsky, ... Nature Reviews Earth & Environment 4 (8), 552-567, 2023 | 61* | 2023 |
| Differentiable, learnable, regionalized process‐based models with multiphysical outputs can approach state‐of‐the‐art hydrologic prediction accuracy D Feng, J Liu, K Lawson, C Shen Water Resources Research 58 (10), e2022WR032404, 2022 | 61 | 2022 |
| Mitigating prediction error of deep learning streamflow models in large data‐sparse regions with ensemble modeling and soft data D Feng, K Lawson, C Shen Geophysical Research Letters 48 (14), e2021GL092999, 2021 | 53 | 2021 |
| The data synergy effects of time‐series deep learning models in hydrology K Fang, D Kifer, K Lawson, D Feng, C Shen Water Resources Research 58 (4), e2021WR029583, 2022 | 51 | 2022 |
| The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment D Feng, H Beck, K Lawson, C Shen Hydrology and Earth System Sciences 27 (12), 2023 | 39* | 2023 |
| An integrated hydrological modeling approach for detection and attribution of climatic and human impacts on coastal water resources D Feng, Y Zheng, Y Mao, A Zhang, B Wu, J Li, Y Tian, X Wu Journal of hydrology 557, 305-320, 2018 | 39 | 2018 |
| Continental-scale streamflow modeling of basins with reservoirs: Towards a coherent deep-learning-based strategy W Ouyang, K Lawson, D Feng, L Ye, C Zhang, C Shen Journal of Hydrology 599, 126455, 2021 | 38 | 2021 |
| Improving large-basin streamflow simulation using a modular, differentiable, learnable graph model for routing T Bindas, WP Tsai, J Liu, F Rahmani, D Feng, Y Bian, K Lawson, C Shen Authorea Preprints, 2022 | 7 | 2022 |
| Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning T Bindas, WP Tsai, J Liu, F Rahmani, D Feng, Y Bian, K Lawson, C Shen Water Resources Research 60 (1), e2023WR035337, 2024 | 5 | 2024 |
| Transferring hydrologic data across continents--leveraging US data to improve hydrologic prediction in other countries K Ma, D Feng, K Lawson, WP Tsai, C Liang, X Huang, A Sharma, C Shen Authorea Preprints, 2022 | 5 | 2022 |
| Deep dive into global hydrologic simulations: Harnessing the power of deep learning and physics-informed differentiable models (δHBV-globe1. 0-hydroDL) D Feng, H Beck, J de Bruijn, RK Sahu, Y Satoh, Y Wada, J Liu, M Pan, ... Geoscientific Model Development Discussions 2023, 1-23, 2023 | 4 | 2023 |
| Improving large-basin river routing using a differentiable Muskingum-Cunge model and physics-informed machine learning T Bindas, WP Tsai, J Liu, F Rahmani, D Feng, Y Bian, K Lawson, C Shen Authorea Preprints, 2023 | 4 | 2023 |
| Prediction in ungauged regions with sparse flow duration curves and input-selection ensemble modeling D Feng, K Lawson, C Shen arXiv preprint arXiv:2011.13380, 2020 | 4 | 2020 |
| From hydrometeorology to water quality: can a deep learning model learn the dynamics of dissolved oxygen at the continental scale? Z Wei, F Dapeng, T Wen-Ping, S Gary, H Adrian, S Chaopeng, L Li Authorea Preprints, 2022 | 3 | 2022 |
| Can transfer learning improve hydrological predictions in the alpine regions? Y Yao, Y Zhao, X Li, D Feng, C Shen, C Liu, X Kuang, C Zheng Journal of Hydrology 625, 130038, 2023 | 2 | 2023 |
| Metamorphic Testing of Machine Learning and Conceptual Hydrologic Models P Reichert, K Ma, M Höge, F Fenicia, M Baity-Jesi, D Feng, C Shen Hydrology and Earth System Sciences Discussions 2023, 1-37, 2023 | 2 | 2023 |
Chaopeng ShenAssociate Professor in Civil Engineering, Pennsylvania State UniversityVerified email at engr.psu.edu
