Predicting Groundwater Indicator Concentration Based on Long Short-Term Memory Neural Network: A Case Study.

Liu, Chao; Xu, Mingshuang; Liu, Yufeng; Li, Xuefei; Pang, Zonglin; Miao, Sheng

Liu, Chao; Xu, Mingshuang; Liu, Yufeng; Li, Xuefei; Pang, Zonglin; Miao, Sheng.

Afiliação

Liu C; School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao 266033, China.
Xu M; School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao 266033, China.
Liu Y; School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China.
Li X; School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao 266033, China.
Pang Z; School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao 266033, China.
Miao S; School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China.

Int J Environ Res Public Health ; 19(23)2022 11 24.

Article em En | MEDLINE | ID: mdl-36497698

Assuntos

Monitoramento Ambiental; Água Subterrânea; Humanos; Monitoramento Ambiental/métodos; Memória de Curto Prazo; Qualidade da Água; Redes Neurais de Computação

Palavras-chave

LSTM; deep learning; groundwater quality; predictive modeling

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Água Subterrânea / Monitoramento Ambiental Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Int J Environ Res Public Health Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China

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