Coupling Machine Learning Into Hydrodynamic Models to Improve River Modeling With Complex Boundary Conditions-Reference-Cited by-同舟云学术

Coupling Machine Learning Into Hydrodynamic Models to Improve River Modeling With Complex Boundary Conditions

Published:2022-10 Issue:10 Volume:58 Page:
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resources Research

Author:

Huang Sheng¹²^ORCID,Xia Jun¹²³⁴,Wang Yueling⁴^ORCID,Wang Wenyucheng¹²,Zeng Sidong⁵,She Dunxian¹²^ORCID,Wang Gangsheng¹²³^ORCID

Affiliation:

1. State Key Laboratory of Water Resources and Hydropower Engineering Science Wuhan University Wuhan China

2. Hubei Key Laboratory of Water System Science for Sponge City Construction Wuhan University Wuhan China

3. Institute for Water‐Carbon Cycles & Carbon Neutrality Wuhan University Wuhan China

4. Key Laboratory of Water Cycle and Related Land Surface Processes Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

5. Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China

Funder

Chinese Academy of Sciences

National Natural Science Foundation of China

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2022WR032183

Reference122 articles.

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2. Real-Time Hydraulic and Hydrodynamic Model of the St. Clair River, Lake St. Clair, Detroit River System

3. Coupling a hybrid CNN-LSTM deep learning model with a Boundary Corrected Maximal Overlap Discrete Wavelet Transform for multiscale Lake water level forecasting

4. Quantifying local rainfall dynamics and uncertain boundary conditions into a nested regional-local flood modeling system

5. Measuring flow in non-ideal conditions for short-term projects: Uncertainties associated with the use of stage-discharge rating curves

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