Articles with public access mandates - Dapeng FengLearn more
Not available anywhere: 1
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
Mandates: National Natural Science Foundation of China
Available somewhere: 19
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
Mandates: US National Science Foundation, US Department of Energy
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
Mandates: US National Science Foundation, US Department of Energy
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
Mandates: US National Science Foundation, US Department of Energy
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
Mandates: US National Science Foundation, US Department of Energy, European Commission
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
Mandates: US National Science Foundation, US Department of Energy
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
Mandates: US National Science Foundation
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
Mandates: US National Science Foundation, US Department of Energy
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
Mandates: US National Science Foundation
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), 2357-2373, 2023
Mandates: US National Science Foundation, US Department of Energy, US National Oceanic …
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
Mandates: US National Science Foundation
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
Mandates: US National Science Foundation, US Department of Energy, US National Oceanic …
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
Mandates: National Natural Science Foundation of China
Identifying structural priors in a hybrid differentiable model for stream water temperature modeling
F Rahmani, A Appling, D Feng, K Lawson, C Shen
Water Resources Research 59 (12), e2023WR034420, 2023
Mandates: US National Science Foundation, US National Oceanic and Atmospheric …
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
Mandates: US National Science Foundation, US National Oceanic and Atmospheric …
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
Mandates: US National Science Foundation
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
Mandates: US National Oceanic and Atmospheric Administration
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
Mandates: US Department of Energy
Global Daily Discharge Estimation Based on Grid-Scale Long Short-Term Memory (LSTM) Model and River Routing
Y Yang, D Feng, HE Beck, W Hu, A Sengupta, L Delle Monache, ...
Authorea Preprints, 2023
Mandates: US National Aeronautics and Space Administration
When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling
Y Song, WJM Knoben, MP Clark, D Feng, KE Lawson, C Shen
Hydrology and Earth System Sciences Discussions 2023, 1-35, 2023
Mandates: US National Science Foundation, US Department of Energy, US National Oceanic …
Publication and funding information is determined automatically by a computer program