In early 2016, months after Flint’s water crisis created international headlines, a group of University of Michigan professors and students realized no one had answered some of residents’ biggest questions.
Among them: How many of Flint homes and buildings tapped lead-tainted water? And where were they?
The academics searched for answers alongside city officials. The city’s water testing data covered just a third of Flint’s nearly 33,000 occupied structures at the time. And more daunting, investigators knew almost nothing about which structures were linked to pipes that were more susceptible to problems.
Knowing which homes had lead or galvanized steel pipelines that may have corroded and leached lead into Flint’s drinking water — as opposed to safer copper lines — would shed light on which homes to dig into, and which to ignore.
But that wasn’t simple, either. Records of Flint’s buried pipelines were a scattered mess, and the city had neither the time nor the millions of dollars to blindly excavate beneath each home to examine its pipelines. There had to be another way to unravel the riddle, the researchers figured.
It involved statistics.
Using machine learning, any data they could scrounge, and a $150,000 grant from Google, researchers constructed a model that could predict lead risks by entering such factors such as a building’s age, size, location and value. The researchers teamed with more experts at UM-Flint to build a web application for residents wanting to see the risk in their homes — all at no cost to the city.
In 2017, the university team’s research revealed more than 20,000 structures that likely had unsafe lead or galvanized steel service lines — far above the city’s original estimate of 15,000.