A novel hybrid random forest linear model approach for forecasting groundwater fluoride contamination.

Nafouanti, Mouigni Baraka; Li, Junxia; Nyakilla, Edwin E; Mwakipunda, Grant Charles; Mulashani, Alvin

Nafouanti, Mouigni Baraka; Li, Junxia; Nyakilla, Edwin E; Mwakipunda, Grant Charles; Mulashani, Alvin.

Afiliação

Nafouanti MB; State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan, 430074, China. mouignibarakanafouanti@gmail.com.
Li J; State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan, 430074, China.
Nyakilla EE; China Laboratory of Basin Hydrology and Wetland Eco-restoration, China University of Geosciences, Wuhan, 430074, China.
Mwakipunda GC; Department of Petroleum Engineering, Faculty of Earth Resources, China University of Geosciences, Wuhan, 430074, China.
Mulashani A; Department of Petroleum Engineering, Faculty of Earth Resources, China University of Geosciences, Wuhan, 430074, China.

Environ Sci Pollut Res Int ; 30(17): 50661-50674, 2023 Apr.

Article em En | MEDLINE | ID: mdl-36800089

Assuntos

Fluoretos; Água Subterrânea; Fluoretos/análise; Modelos Lineares; Monitoramento Ambiental/métodos; Algoritmo Florestas Aleatórias; Água Subterrânea/análise

Palavras-chave

Extreme gradient boosting; Fluoride; Groundwater; Hybrid random forest linear model; Light gradient boosting machine

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Água Subterrânea / Fluoretos Tipo de estudo: Clinical_trials / Prognostic_studies Idioma: En Revista: Environ Sci Pollut Res Int Assunto da revista: SAUDE AMBIENTAL / TOXICOLOGIA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China

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