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Kuai Fang
Kuai Fang
Postdoc Stanford
在 stanford.edu 的电子邮件经过验证
标题
引用次数
引用次数
年份
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
2692020
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
2602017
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
2212018
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
1132018
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
1072020
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
872023
Evaluating the potential and challenges of an uncertainty quantification method for long short‐term memory models for soil moisture predictions
K Fang, D Kifer, K Lawson, C Shen
Water Resources Research 56 (12), e2020WR028095, 2020
802020
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
612022
Full‐flow‐regime storage‐streamflow correlation patterns provide insights into hydrologic functioning over the continental US
K Fang, C Shen
Water Resources Research 53 (9), 8064-8083, 2017
412017
The fan of influence of streams and channel feedbacks to simulated land surface water and carbon dynamics
C Shen, WJ Riley, KR Smithgall, JM Melack, K Fang
Water Resources Research 52 (2), 880-902, 2016
382016
Improving Budyko curve‐based estimates of long‐term water partitioning using hydrologic signatures from GRACE
K Fang, C Shen, JB Fisher, J Niu
Water Resources Research 52 (7), 5537-5554, 2016
342016
Quantifying the effects of data integration algorithms on the outcomes of a subsurface–land surface processes model
C Shen, J Niu, K Fang
Environmental Modelling & Software 59, 146-161, 2014
342014
Combining a land surface model with groundwater model calibration to assess the impacts of groundwater pumping in a mountainous desert basin
K Fang, X Ji, C Shen, N Ludwig, P Godfrey, T Mahjabin, C Doughty
Advances in Water Resources 130, 12-28, 2019
132019
Evaluating aleatoric and epistemic uncertainties of time series deep learning models for soil moisture predictions
K Fang, C Shen, D Kifer
arXiv preprint arXiv:1906.04595, 2019
112019
HESS Opinions: Deep learning as a promising avenue toward knowledge discovery in water sciences
C Shen, E Laloy, A Albert, FJ Chang, A Elshorbagy, S Ganguly, K Hsu, ...
Hydrology and Earth System Sciences Discussions 2018, 1-21, 2018
102018
Revealing causal controls of storage-streamflow relationships with a data-centric bayesian framework combining machine learning and process-based modeling
WP Tsai, K Fang, X Ji, K Lawson, C Shen
Frontiers in Water 2, 583000, 2020
72020
On the data synergy effect of large-sample multi-physics catchment modeling with machine learning
C Shen, F Rahmani, K Fang, Z Wei, WP Tsai
EGU General Assembly Conference Abstracts, EGU21-16108, 2021
2021
Transcending the uniqueness of places with large-sample multi-physics catchment modeling based on machine learning
C Shen, F Rahmani, W Zhi, K Fang, WP Tsai, L Li, K Lawson
AGU Fall Meeting Abstracts 2020, H103-08, 2020
2020
Multisource Data Integration under a Deep Learning Framework to Improve Streamflow Forecast Ability
D Feng, C Shen, K Fang
100th American Meteorological Society Annual Meeting, 2020
2020
Applying deep learning in estimating parameters for hydrologic model
WP Tsai, C Shen, K Fang
AGU Fall Meeting Abstracts 2019, H31E-01, 2019
2019
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