Hydrogeochemical and sediment parameters improve predication accuracy of arsenic-prone groundwater in random forest machine-learning models.

Guo, Wenjing; Gao, Zhipeng; Guo, Huaming; Cao, Wengeng

Guo, Wenjing; Gao, Zhipeng; Guo, Huaming; Cao, Wengeng.

Afiliación

Guo W; State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Beijing 100083, PR China; MOE Key Laboratory of Groundwater Circulation and Environmental Evolution, School of Water Resources and Environment, China University of Geosciences (Beijing), Beijing 100083, PR
Gao Z; State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Beijing 100083, PR China; MOE Key Laboratory of Groundwater Circulation and Environmental Evolution, School of Water Resources and Environment, China University of Geosciences (Beijing), Beijing 100083, PR
Guo H; State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Beijing 100083, PR China; MOE Key Laboratory of Groundwater Circulation and Environmental Evolution, School of Water Resources and Environment, China University of Geosciences (Beijing), Beijing 100083, PR
Cao W; Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences, Shijiazhuang 050061, PR China.

Sci Total Environ ; 897: 165511, 2023 Nov 01.

Article en En | MEDLINE | ID: mdl-37442467

Palabras clave

Arsenic; Groundwater; Hetao Basin; Prediction; Random forest model

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Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Clinical_trials / Prognostic_studies Idioma: En Revista: Sci Total Environ Año: 2023 Tipo del documento: Article País de afiliación: Puerto Rico

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