Modeling and uncertainty analysis of seawater intrusion based on surrogate models.

Miao, Tiansheng; Lu, Wenxi; Guo, Jiayuan; Lin, Jin; Fan, Yue

Miao, Tiansheng; Lu, Wenxi; Guo, Jiayuan; Lin, Jin; Fan, Yue.

Afiliação

Miao T; College of New Energy and Environment, Jilin University, Changchun, 130021, China.
Lu W; Key Laboratory of Groundwater Resources and Environment (Jilin University), Ministry of Education, Changchun, 130021, China.
Guo J; Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun, 130021, China.
Lin J; College of New Energy and Environment, Jilin University, Changchun, 130021, China. luwx999@163.com.
Fan Y; Key Laboratory of Groundwater Resources and Environment (Jilin University), Ministry of Education, Changchun, 130021, China. luwx999@163.com.

Environ Sci Pollut Res Int ; 26(25): 26015-26025, 2019 Sep.

Article em En | MEDLINE | ID: mdl-31273667

Assuntos

Método de Monte Carlo; Modelos Teóricos; Água do Mar; Incerteza

Palavras-chave

RBF neural network; Sea level rise; Seawater intrusion; Surrogate model; Uncertainty analysis

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Método de Monte Carlo Tipo de estudo: Health_economic_evaluation / Prognostic_studies Idioma: En Ano de publicação: 2019 Tipo de documento: Article

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