Getting the Lead Out: Data Science and Water Service Lines in Flint (and beyond)
Jared Webb, University of Michigan
Abstract
I give a brief outline of the Flint Water Crisis and our previous work making lead service line discovery more efficient. I also compare the efficacy of our data driven models to a non-data driven approach run by an independent engineering firm in 2018. The data show that using a machine learning approach significantly improves the efficiency of the lead service line replacement program in Flint.
I give a brief outline of the Flint Water Crisis and our previous work making lead service line discovery more efficient. I also compare the efficacy of our data driven models to a non-data driven approach run by an independent engineering firm in 2018. The data show that using a machine learning approach significantly improves the efficiency of the lead service line replacement program in Flint.
Bio
Jared Webb is passionate about making machine learning work for everyone. He has been working as a data scientist for the University of Michigan to support the lead service line replacement program in Flint, Michigan since 2016. His publications and presentations on the topic have won various awards, including the Best Student Paper Award (Applied Data Science) at KDD 2018.
