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

文献类型：期刊论文

作者Huang, Sheng4,5; Xia, Jun1,3,4,5; Wang, Yueling1; Wang, Wenyucheng4,5; Zeng, Sidong2; She, Dunxian4,5; Wang, Gangsheng3,4,5
刊名WATER RESOURCES RESEARCH
出版日期2022-10-01
卷号58期号:10页码:15
ISSN号0043-1397
关键词river modeling machine learning hydrodynamic models water exchange downstream boundary conditions
DOI10.1029/2022WR032183
通讯作者Xia, Jun(xiajun666@whu.edu.cn)
英文摘要Rivers play an important role in water supply, irrigation, navigation, and ecological maintenance. Forecasting the river hydrodynamic changes is critical for flood management under climate change and intensified human activities. However, efficient and accurate river modeling is challenging, especially with complex lake boundary conditions and uncontrolled downstream boundary conditions. Here, we proposed a coupled framework by taking the advantages of interpretability of physical hydrodynamic modeling and the adaptability of machine learning. Specifically, we coupled the Gated Recurrent Unit (GRU) with a 1-D HydroDynamic model (GRU-HD) and applied it to the middle and lower reaches of the Yangtze River, the longest river in China. We show that the GRU-HD model could quickly and accurately simulate the water levels, streamflow, and water exchange rates between the Yangtze River and two important lakes (Poyang and Dongting), with most of the Kling-Gupta efficiency coefficient (KGE $\mathrm{K}\mathrm{G}\mathrm{E}$) above 0.90. Using machine learning-based predicted water levels, instead of the rating curve approach, as the downstream boundary conditions could improve the accuracy of modeling the downstream water levels of the lake-connected river system. The GRU-HD model is dedicated to the synergy of physical modeling and machine learning, providing a powerful avenue for modeling rivers with complex boundary conditions.
WOS关键词LAKE WATER-LEVEL ; 3 GORGES DAM ; YANGTZE-RIVER ; POYANG LAKE ; BLACK-BOX ; FLOW ; UNCERTAINTY ; ROUGHNESS ; DISCHARGE ; REGRESSION
资助项目Strategic Priority Research Program of the Chinese Academy of Sciences[XDA23040502] ; National Natural Science Foundation of China[41890823]
WOS研究方向Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources
语种英语
出版者AMER GEOPHYSICAL UNION
WOS记录号WOS:000864162000001
资助机构Strategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/185835]  
专题中国科学院地理科学与资源研究所
通讯作者Xia, Jun
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing, Peoples R China
2.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing, Peoples R China
3.Wuhan Univ, Inst Water Carbon Cycles & Carbon Neutral, Wuhan, Peoples R China
4.Wuhan Univ, Hubei Key Lab Water Syst Sci Sponge City Construc, Wuhan, Peoples R China
5.Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan, Peoples R China
推荐引用方式
GB/T 7714		 Huang, Sheng,Xia, Jun,Wang, Yueling,et al. Coupling Machine Learning Into Hydrodynamic Models to Improve River Modeling With Complex Boundary Conditions[J]. WATER RESOURCES RESEARCH,2022,58(10):15.
APA Huang, Sheng.,Xia, Jun.,Wang, Yueling.,Wang, Wenyucheng.,Zeng, Sidong.,...&Wang, Gangsheng.(2022).Coupling Machine Learning Into Hydrodynamic Models to Improve River Modeling With Complex Boundary Conditions.WATER RESOURCES RESEARCH,58(10),15.
MLA Huang, Sheng,et al."Coupling Machine Learning Into Hydrodynamic Models to Improve River Modeling With Complex Boundary Conditions".WATER RESOURCES RESEARCH 58.10(2022):15.

入库方式： OAI收割

来源：地理科学与资源研究所

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