Simulation of regional groundwater levels in arid regions using interpretable machine learning models.

Liu, Qi; Gui, Dongwei; Zhang, Lei; Niu, Jie; Dai, Heng; Wei, Guanghui; Hu, Bill X

Liu, Qi; Gui, Dongwei; Zhang, Lei; Niu, Jie; Dai, Heng; Wei, Guanghui; Hu, Bill X.

Affiliation

Liu Q; State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Urumqi, Xinjiang, China; College of Life Science and Technology, Jinan University, Guangzhou, Guangdong, China.
Gui D; State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Urumqi, Xinjiang, China. Electronic address: guidwei@ms.xjb.ac.cn.
Zhang L; State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Urumqi, Xinjiang, China.
Niu J; College of Life Science and Technology, Jinan University, Guangzhou, Guangdong, China. Electronic address: jniu@jnu.edu.cn.
Dai H; State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan, China.
Wei G; Xinjiang Tarim River Basin Management Bureau, Korla, Xinjiang, China.
Hu BX; School of Water Conservancy and Environment, University of Jinan, Shandong, China.

Sci Total Environ ; 831: 154902, 2022 Jul 20.

Article de En | MEDLINE | ID: mdl-35364142

Sujet(s)
Mots clés

Arid regions; Deep learning; Ecological water conveyance; Groundwater level; Machine learning; Shapely additive explanations

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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Nappe phréatique / Écosystème Type d'étude: Prognostic_studies Limites: Humans Langue: En Journal: Sci Total Environ Année: 2022 Type de document: Article Pays d'affiliation: Chine Pays de publication: Pays-Bas

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