A Statistical Approach for Identifying Private Wells Susceptible to Perfluoroalkyl Substances (PFAS) Contamination

Cindy Hu, Beverly Ge, Bridger J. Ruyle, Jennifer Sun and Elsie M. Sunderland

Mathematica Policy Research Reports from Mathematica Policy Research

Abstract: Monitoring PFAS contamination can be costly and time consuming. We developed and evaluated a machine learning model to identify private wells susceptible to PFAS contamination.

Keywords: Environment; Drinking Water; PFAS; Machine Learning; Predictive Analytics (search for similar items in EconPapers)
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