Using Machine Learning to Assess the Risk of and Prevent Water Main Breaks

TL;DR

This paper presents a machine learning system that predicts water main failures three years in advance, enabling proactive maintenance and infrastructure management in Syracuse, with promising results outperforming heuristics and baselines.

Contribution

The study develops and deploys a gradient boosted decision tree model for water main failure prediction, demonstrating its effectiveness over heuristics and baselines in a real city setting.

Findings

Model achieved P@1 of 0.62, outperforming heuristics and baseline.

33 water main breaks occurred on the riskiest mains during pilot.

System is operational in Syracuse, aiding proactive infrastructure maintenance.

Abstract

Water infrastructure in the United States is beginning to show its age, particularly through water main breaks. Main breaks cause major disruptions in everyday life for residents and businesses. Water main failures in Syracuse, N.Y. (as in most cities) are handled reactively rather than proactively. A barrier to proactive maintenance is the city's inability to predict the risk of failure on parts of its infrastructure. In response, we worked with the city to build a ML system to assess the risk of a water mains breaking. Using historical data on which mains have failed, descriptors of pipes, and other data sources, we evaluated several models' abilities to predict breaks three years into the future. Our results show that our system using gradient boosted decision trees performed the best out of several algorithms and expert heuristics, achieving precision at 1\% (P@1) of 0.62. Our model outperforms a random baseline (P@1 of 0.08) and expert heuristics such as water main age (P@1 of 0.10) and history of past main breaks (P@1 of 0.48). The model is deployed in the City of Syracuse. We are running a pilot by calculating the risk of failure for each city block over the period 2016-2018 using data up to the end of 2015 and, as of the end of 2017, there have been 33 breaks on our riskiest 52 mains. This has been a successful initiative for the city of Syracuse in improving their infrastructure and we believe this approach can be applied to other cities.