Artikler med mandater om offentlig tilgang - Chaopeng ShenLes mer
Ikke tilgjengelige noe sted: 4
Physics-guided deep learning for rainfall-runoff modeling by considering extreme events and monotonic relationships
K Xie, P Liu, J Zhang, D Han, G Wang, C Shen
Journal of Hydrology 603, 127043, 2021
Mandater: National Natural Science Foundation of China
Applications of deep learning in hydrology
C Shen, K Lawson
Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote …, 2021
Mandater: US National Science Foundation, US Department of Energy
Applying transfer learning techniques to enhance the accuracy of streamflow prediction produced by long Short-term memory networks with data integration
Y Khoshkalam, AN Rousseau, F Rahmani, C Shen, K Abbasnezhadi
Journal of Hydrology 622, 129682, 2023
Mandater: Natural Sciences and Engineering Research Council of Canada
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
Mandater: National Natural Science Foundation of China
Tilgjengelige et eller annet sted: 64
A transdisciplinary review of deep learning research and its relevance for water resources scientists
C Shen
Water Resources Research 54 (11), 8558-8593, 2018
Mandater: US National Science Foundation, US Department of Energy, US Department of …
An overview of current applications, challenges, and future trends in distributed process-based models in hydrology
S Fatichi, ER Vivoni, FL Ogden, VY Ivanov, B Mirus, D Gochis, ...
Journal of Hydrology 537, 45-60, 2016
Mandater: US National Science Foundation, US National Oceanic and Atmospheric …
Improving the representation of hydrologic processes in Earth System Models
MP Clark, Y Fan, DM Lawrence, JC Adam, D Bolster, DJ Gochis, ...
Water Resources Research 51 (8), 5929-5956, 2015
Mandater: US Department of Energy
Hillslope hydrology in global change research and earth system modeling
Y Fan, M Clark, DM Lawrence, S Swenson, LE Band, SL Brantley, ...
Water Resources Research 55 (2), 1737-1772, 2019
Mandater: US National Science Foundation, US Department of Energy
Surface‐subsurface model intercomparison: A first set of benchmark results to diagnose integrated hydrology and feedbacks
RM Maxwell, M Putti, S Meyerhoff, JO Delfs, IM Ferguson, V Ivanov, J Kim, ...
Water resources research 50 (2), 1531-1549, 2014
Mandater: Government of Italy
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
Mandater: US National Science Foundation, US Department of Energy
Prolongation of SMAP to spatiotemporally seamless coverage of continental US using a deep learning neural network
K Fang, C Shen, D Kifer, X Yang
Geophysical Research Letters 44 (21), 11,030-11,039, 2017
Mandater: US National Science Foundation, US Department of Energy
HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community
C Shen, E Laloy, A Elshorbagy, A Albert, J Bales, FJ Chang, S Ganguly, ...
Hydrology and Earth System Sciences 22 (11), 5639-5656, 2018
Mandater: US National Science Foundation, US Department of Energy, Natural Sciences …
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
Mandater: 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
Mandater: 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
Mandater: US National Science Foundation, US Department of Energy, European Commission
The value of SMAP for long-term soil moisture estimation with the help of deep learning
K Fang, M Pan, C Shen
IEEE Transactions on Geoscience and Remote Sensing 57 (4), 2221-2233, 2018
Mandater: US National Science Foundation, US National Aeronautics and Space Administration
Near-real-time forecast of satellite-based soil moisture using long short-term memory with an adaptive data integration kernel
K Fang, C Shen
Journal of Hydrometeorology 21 (3), 399-413, 2020
Mandater: US National Science Foundation, US Department of Energy
Hybrid forecasting: using statistics and machine learning to integrate predictions from dynamical models
L Slater, L Arnal, MA Boucher, AYY Chang, S Moulds, C Murphy, ...
Hydrology and Earth System Sciences Discussions 2022, 1-35, 2022
Mandater: US Department of Defense, Science Foundation Ireland, UK Natural Environment …
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
Mandater: 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
Mandater: US National Science Foundation
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