中文版 | English
Title

Toward improved lumped groundwater level predictions at catchment scale: Mutual integration of water balance mechanism and deep learning method

Author
Corresponding AuthorShi,Haiyun
Publication Years
2022-10-01
DOI
Source Title
ISSN
0022-1694
EISSN
1879-2707
Volume613
Abstract
Model development in groundwater simulation and physics informed deep learning (DL) has been advancing separately with limited integration. This study develops a general hybrid model for groundwater level (GWL) simulations, wherein water balance-based groundwater processes are embedded as physics constrained recurrent neural layers into prevalent DL architectures. Because of the automatic parameterizing process, physics-informed deep learning algorithm (DLA) equips the hybrid model with enhanced abilities of inferring geological structures of catchment and unobserved groundwater-related processes implicitly. The main purposes of this study are: 1) to explore an optimized data-driven method as alternative to complicated groundwater models; 2) to improve the awareness of hydrological knowledge of DL model for lumped GWL simulation; and 3) to explore the lumped data-driven groundwater models for cross-region applications. The 91 illustrative cases of GWL modeling across the middle eastern continental United States (CONUS) demonstrate that the hybrid model outperforms the pure DL models in terms of prediction accuracy, generality, and robustness. More specifically, the hybrid model outperforms the pure DL models in 78 % of catchments with the improved Δ NSE = 0.129. Meanwhile, the hybrid model simulates more stably with different input strategies. This study reveals the superiority and powerful simulation ability of the DL model with physical constraints, which increases trust in data-driven approaches on groundwater modellings.
Keywords
URL[Source Record]
Indexed By
Language
English
SUSTech Authorship
Corresponding
Funding Project
[51909117] ; [JCYJ20210324105014039]
WOS Research Area
Engineering ; Geology ; Water Resources
WOS Subject
Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS Accession No
WOS:000868341800002
Publisher
ESI Research Field
ENGINEERING
Scopus EID
2-s2.0-85139037756
Data Source
Scopus
Citation statistics
Cited Times [WOS]:3
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/406192
DepartmentSchool of Environmental Science and Engineering
Affiliation
1.Department of Civil and Environmental Engineering,National University of Singapore,Singapore
2.State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control,School of Environmental Science and Engineering,Southern University of Science and Technology,Shenzhen,Guangdong,China
3.Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control,School of Environmental Science and Engineering,Southern University of Science and Technology,Shenzhen,Guangdong,China
4.Center for Climate Physics,Institute for Basic Science,Busan,South Korea
5.Department of Computational Hydrosystems,Helmholtz Centre for Environmental Research,Leipzig,Germany
First Author AffilicationSchool of Environmental Science and Engineering
Corresponding Author AffilicationSchool of Environmental Science and Engineering
Recommended Citation
GB/T 7714
Cai,Hejiang,Liu,Suning,Shi,Haiyun,et al. Toward improved lumped groundwater level predictions at catchment scale: Mutual integration of water balance mechanism and deep learning method[J]. JOURNAL OF HYDROLOGY,2022,613.
APA
Cai,Hejiang,Liu,Suning,Shi,Haiyun,Zhou,Zhaoqiang,Jiang,Shijie,&Babovic,Vladan.(2022).Toward improved lumped groundwater level predictions at catchment scale: Mutual integration of water balance mechanism and deep learning method.JOURNAL OF HYDROLOGY,613.
MLA
Cai,Hejiang,et al."Toward improved lumped groundwater level predictions at catchment scale: Mutual integration of water balance mechanism and deep learning method".JOURNAL OF HYDROLOGY 613(2022).
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