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Study on spatio-temporal simulation and prediction of regional deep soil moisture using machine learning.
A, Yinglan; Jiang, Xiaoman; Wang, Yuntao; Wang, Libo; Zhang, Zihao; Duan, Limin; Fang, Qingqing.
  • A Y; State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China.
  • Jiang X; State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China.
  • Wang Y; State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China; Center for Geodata and Analysis, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China. Electronic address: ytwang@bnu.edu.cn.
  • Wang L; State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China.
  • Zhang Z; State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China.
  • Duan L; Water Conservancy and Civil Engineering College, Inner Mongolia Agricultural University, Hohhot 010018, China.
  • Fang Q; School of Water Conservancy and Hydropower Engineering, North China Electric Power University, Beijing 102206, China.
J Contam Hydrol ; 258: 104235, 2023 Sep.
Article en En | MEDLINE | ID: mdl-37651919
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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Año: 2023 Tipo del documento: Article
Texto completo
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XML
PubMed Links
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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Año: 2023 Tipo del documento: Article