ActiveRemediation: The Search for Lead Pipes in Flint, Michigan

TL;DR

This paper presents a machine learning-based approach to identify and replace lead pipes in Flint, Michigan, aiming to improve public health outcomes amid infrastructure challenges and limited records.

Contribution

It introduces predictive models and procedural tools for detecting lead pipes, integrating statistical methods with government collaboration to enhance infrastructure remediation.

Findings

Effective machine learning models for pipe detection

Adaptive statistical approach improves inspection accuracy

Potential for scalable application nationwide

Abstract

We detail our ongoing work in Flint, Michigan to detect pipes made of lead and other hazardous metals. After elevated levels of lead were detected in residents' drinking water, followed by an increase in blood lead levels in area children, the state and federal governments directed over $125 million to replace water service lines, the pipes connecting each home to the water system. In the absence of accurate records, and with the high cost of determining buried pipe materials, we put forth a number of predictive and procedural tools to aid in the search and removal of lead infrastructure. Alongside these statistical and machine learning approaches, we describe our interactions with government officials in recommending homes for both inspection and replacement, with a focus on the statistical model that adapts to incoming information. Finally, in light of discussions about increased spending on infrastructure development by the federal government, we explore how our approach generalizes beyond Flint to other municipalities nationwide.