Litcius/Paper detail

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

Wenjing Guo, Zhipeng Gao, Huaming Guo, Wengeng Cao

2023The Science of The Total Environment28 citationsDOI

Topics & Concepts

GroundwaterSedimentArsenicEnvironmental scienceHydrology (agriculture)Structural basinSoil scienceGeologyGeomorphologyGeotechnical engineeringChemistryOrganic chemistryArsenic contamination and mitigationHeavy metals in environmentGroundwater and Isotope Geochemistry
Hydrogeochemical and sediment parameters improve predication accuracy of arsenic-prone groundwater in random forest machine-learning models | Litcius