  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.
