  Recovery from the Flint Water Crisis has been hindered by uncertainty in both
the water testing process and the causes of contamination. In this work, we
develop an ensemble of predictive models to assess the risk of lead
contamination in individual homes and neighborhoods. To train these models, we
utilize a wide range of data sources, including voluntary residential water
tests, historical records, and city infrastructure data. Additionally, we use
our models to identify the most prominent factors that contribute to a high
risk of lead contamination. In this analysis, we find that lead service lines
are not the only factor that is predictive of the risk of lead contamination of
water. These results could be used to guide the long-term recovery efforts in
Flint, minimize the immediate damages, and improve resource-allocation
decisions for similar water infrastructure crises.

Machine Learning;Applications