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

author: Avishek Kumar, Center for Data Science and Public Policy, University of Chicago
published: Nov. 23, 2018,   recorded: August 2018,   views: 0
Categories

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

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 with limited resources is the city’s inability to properly prioritize the allocation of its resources. We built a Machine Learning 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 best out of several algorithms and expert heuristics, achieving precision at 1% ([email protected]) 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 currently deployed in the City of Syracuse. We are conducting 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 42 breaks on our riskiest 52 mains. This has been a successful initiative for the city of Syracuse in improving its infrastructure and we believe this approach can be applied to other cities.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: