Predicting arsenic and manganese contamination in private well water with Machine Learning: An integrated analysis of geologic, well construction, and permitting data.

ABSTRACT: Exposure to arsenic (As) and manganese (Mn) through private well water is a pressing public health issue in North Carolina (NC). The goal of this study was to identify geologic, geographic, well construction, and water characteristics (e.g. pH) that are predictive of As and Mn concentrations in private wells. Data collected from over 700 private well water tests from Union County, NC, were analyzed, including thirteen variables spanning well construction and water characteristics that were joined with geologic and geographic features. These data were used to develop random forest (RF) and support vector machine (SVM) models to predict whether wells exceeded their respective maximum contaminant level (MCL) or health advisory level (HAL) (10 ppb for As; 300 ppb for Mn) using four different v

SUBMITTER: Payton A 

PROVIDER: S-EPMC12755026 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature