Flint Water Crisis: Data-Driven Risk Assessment Via Residential Water Testing
Abstract
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.
Keywords
Cite
@article{arxiv.1610.00580,
title = {Flint Water Crisis: Data-Driven Risk Assessment Via Residential Water Testing},
author = {Jacob Abernethy and Cyrus Anderson and Chengyu Dai and Arya Farahi and Linh Nguyen and Adam Rauh and Eric Schwartz and Wenbo Shen and Guangsha Shi and Jonathan Stroud and Xinyu Tan and Jared Webb and Sheng Yang},
journal= {arXiv preprint arXiv:1610.00580},
year = {2016}
}
Comments
Presented at the Data For Good Exchange 2016