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Recovery from the Flint Water Crisis has been hindered by uncertainty in boththe water testing process and the causes of contamination. In this work, wedevelop an ensemble of predictive models to assess the risk of leadcontamination in individual homes and neighborhoods. To train these models, weutilize a wide range of data sources, including voluntary residential watertests, historical records, and city infrastructure data. Additionally, we useour models to identify the most prominent factors that contribute to a highrisk of lead contamination. In this analysis, we find that lead service linesare not the only factor that is predictive of the risk of lead contamination ofwater. These results could be used to guide the long-term recovery efforts inFlint, minimize the immediate damages, and improve resource-allocationdecisions for similar water infrastructure crises.

Flint Water Crisis: Data-Driven Risk Assessment Via Residential Water Testing