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Review of machine learning-based surrogate models of groundwater contaminant modeling.
Luo, Jiannan; Ma, Xi; Ji, Yefei; Li, Xueli; Song, Zhuo; Lu, Wenxi.
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  • Luo J; Key Laboratory of Groundwater Resources and Environment (Jilin University), Ministry of Education, Changchun 130021, China; Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun 130021, China; College of New Energy and Environment, Jilin University, Changchu
  • Ma X; Key Laboratory of Groundwater Resources and Environment (Jilin University), Ministry of Education, Changchun 130021, China; Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun 130021, China; College of New Energy and Environment, Jilin University, Changchu
  • Ji Y; Songliao Water Resources Commission, Ministry of Water Resources, Changchun 130021, China.
  • Li X; Key Laboratory of Groundwater Resources and Environment (Jilin University), Ministry of Education, Changchun 130021, China; Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun 130021, China; College of New Energy and Environment, Jilin University, Changchu
  • Song Z; Key Laboratory of Groundwater Resources and Environment (Jilin University), Ministry of Education, Changchun 130021, China; Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun 130021, China; College of New Energy and Environment, Jilin University, Changchu
  • Lu W; Key Laboratory of Groundwater Resources and Environment (Jilin University), Ministry of Education, Changchun 130021, China; Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun 130021, China; College of New Energy and Environment, Jilin University, Changchu
Environ Res ; 238(Pt 2): 117268, 2023 12 01.
Article en En | MEDLINE | ID: mdl-37776938
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Agua Subterránea / Modelos Teóricos Tipo de estudio: Guideline / Prognostic_studies Idioma: En Revista: Environ Res Año: 2023 Tipo del documento: Article
Texto completo
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Agua Subterránea / Modelos Teóricos Tipo de estudio: Guideline / Prognostic_studies Idioma: En Revista: Environ Res Año: 2023 Tipo del documento: Article