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Physics-Guided Long Short-Term Memory Network for Streamflow and Flood Simulations in the Lancang-Mekong River Basin

文献类型：期刊论文

作者Liu, Binxiao1,2; Tang, Qiuhong1,2; Zhao, Gang3; Gao, Liang4,5; Shen, Chaopeng6; Pan, Baoxiang7
刊名WATER
出版日期2022-05-01
卷号14期号:9页码:16
关键词hydrological modeling data-driven modeling physics-guided neural network (PGNN)
DOI10.3390/w14091429
通讯作者Tang, Qiuhong(tangqh@igsnrr.ac.cn)
英文摘要A warming climate will intensify the water cycle, resulting in an exacerbation of water resources crises and flooding risks in the Lancang-Mekong River Basin (LMRB). The mitigation of these risks requires accurate streamflow and flood simulations. Process-based and data-driven hydrological models are the two major approaches for streamflow simulations, while a hybrid of these two methods promises advantageous prediction accuracy. In this study, we developed a hybrid physics-data (HPD) methodology for streamflow and flood prediction under the physics-guided neural network modeling framework. The HPD methodology leveraged simulation information from a process-based model (i.e., VIC-CaMa-Flood) along with the meteorological forcing information (precipitation, maximum temperature, minimum temperature, and wind speed) to simulate the daily streamflow series and flood events, using a long short-term memory (LSTM) neural network. This HPD methodology outperformed the pure process-based VIC-CaMa-Flood model or the pure observational data driven LSTM model by a large margin, suggesting the usefulness of introducing physical regularization in data-driven modeling, and the necessity of observation-informed bias correction for process-based models. We further developed a gradient boosting tree method to measure the information contribution from the process-based model simulation and the meteorological forcing data in our HPD methodology. The results show that the process-based model simulation contributes about 30% to the HPD outcome, outweighing the information contribution from each of the meteorological forcing variables (<20%). Our HPD methodology inherited the physical mechanisms of the process-based model, and the high predictability capability of the LSTM model, offering a novel way for making use of incomplete physical understanding, and insufficient data, to enhance streamflow and flood predictions.
WOS关键词CLIMATE-CHANGE ; NEURAL-NETWORK ; MODEL ; PRECIPITATION ; IMPACTS ; SCIENCE ; DATASET ; SYSTEM ; WATER
资助项目National Natural Science Foundation of China[41730645] ; National Natural Science Foundation of China[41790424] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA20060402] ; CAS-CSIRO Joint Project[131A11KYSB20180034]
WOS研究方向Environmental Sciences & Ecology ; Water Resources
语种英语
WOS记录号WOS:000795274200001
出版者MDPI
资助机构National Natural Science Foundation of China ; Strategic Priority Research Program of Chinese Academy of Sciences ; CAS-CSIRO Joint Project
源URL[http://ir.igsnrr.ac.cn/handle/311030/176875]  
专题中国科学院地理科学与资源研究所
通讯作者Tang, Qiuhong
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proces, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Carnegie Inst Sci, Dept Global Ecol, Stanford, CA 94305 USA
4.Univ Macau, State Key Lab Internet Things Smart City, Macau 999078, Peoples R China
5.Univ Macau, Dept Civil & Environm Engn, Macau 999078, Peoples R China
6.Penn State Univ, Civil & Environm Engn, State Coll, PA 16801 USA
7.Lawrence Livermore Natl Lab, Atmospher Earth & Energy Div, Livermore, CA 94550 USA
推荐引用方式
GB/T 7714		 Liu, Binxiao,Tang, Qiuhong,Zhao, Gang,et al. Physics-Guided Long Short-Term Memory Network for Streamflow and Flood Simulations in the Lancang-Mekong River Basin[J]. WATER,2022,14(9):16.
APA Liu, Binxiao,Tang, Qiuhong,Zhao, Gang,Gao, Liang,Shen, Chaopeng,&Pan, Baoxiang.(2022).Physics-Guided Long Short-Term Memory Network for Streamflow and Flood Simulations in the Lancang-Mekong River Basin.WATER,14(9),16.
MLA Liu, Binxiao,et al."Physics-Guided Long Short-Term Memory Network for Streamflow and Flood Simulations in the Lancang-Mekong River Basin".WATER 14.9(2022):16.

入库方式： OAI收割

来源：地理科学与资源研究所

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