A Machine Learning Approach to Identify Houses with High Lead Tap Water Concentrations

Abstract

Over a century separates initial lead service lateral installations from the federal regulation of lead in drinking water. As such, municipalities often do not have adequate information describing installations of lead plumbing. Municipalities thus face challenges such as reducing exposure to lead in drinking water, spreading scarce resources for gathering information, adopting short-term protection measures (e.g., providing filters), and developing longer-term prevention strategies (e.g., replacing lead laterals). Given the spatial and temporal patterns to properties, machine learning is seen as a useful tool to reduce uncertainty in decision making by authorities when addressing lead in water. The Pittsburgh Water and Sewer Authority (PWSA) is currently addressing these challenges in Pittsburgh and this paper describes the development and application of a model predicting high tap water concentrations (> 15 ppb) for PWSA customers. The model was developed using spatial cross validation to support PWSA’s interest in applying predictions in areas without training data. The model’s AUROC is 71.6% and primarily relies on publicly available property tax assessment data and indicators of lateral material collected by PWSA as they meet regulatory requirements.