Flint Water Crisis: Data-Driven Risk Assessment Via Residential Water Testing
This work addresses uncertainty in water testing and contamination causes for Flint's recovery efforts, with potential applications to similar infrastructure crises.
The researchers tackled the problem of assessing lead contamination risk in Flint's water crisis by developing an ensemble of predictive models using residential water tests and other data, finding that lead service lines are not the only predictive factor.
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.