text: 138,78,996006570868822016,364410282,Kevin H. Wilson,"Old project new paper acceptance! ""Using Machine Learning to Assess the Risk of and Prevent Water Main Breaks"" @ KDD. <LINK> 1/ This was a project at @datascifellows. We had a _lot_ of co-authors (which I think is awesome). @samedelstein, @adriaf, @CKenneyJr, @rayidghani, Avishek Kumar, Ali Rizvi, Ben Brooks, Ali Vanderveld, Andrew Maxwell, and Joe Zuckerbraun 2/ Water main breaks are a huge problem in Rust Belt cities. The infrastructure was built assuming a growing population and growing suburanization. The tax base hasn't kept up with original projections, so maintenance $$$s are declining. 3/ <LINK> On the other hand, people aren't moving into the city core, so cities can't simply decomission parts of their infrastructure without cutting water off to residents. The result? <LINK> 4/ We also know that preventive maintenance is chepar than reactive maintenance. A bunch of restaurants with no water for a day is a _really bad thing_. So how should we order maintenance? 5/ A first pass indicates that if you just replaced 50 pipes that have broken in the past, you would stop about 24 water main breaks. This indicates that the way that pipes are temporarily repaired doesn't hold for very long. 6/ On the other hand, a lot of pipes have broken in the past. Can we do even better? Accounting for pipe age, diameter, and material, as well as the surrounding soil type, we could go from preventing 24 breaks to 31 breaks / 50 repaired. 7/ <LINK> The methods we used (GBDT, a little clever GIS trickery) aren't revolutionary. The key part of this project was that it took subject matter experts in data science, local government, and water maintenance and asked what they could do. 8/ In particular, our data was very messy. When water mains were laid, meticulous notes were kept about them. But they were laid in the late 1800s, so those notes look like this: 9/ <LINK> Without Sam, Adria, Joe, and Andrew, this sort of data would never have been able to go anywhere. 10/ Nowadays I'm doing more of this sort of work with @TheLab_DC, finding housing violations, getting people enrolled for benefits, and tackling the rat problem in the District. If you're interested in this kind of work, hit me up. 11/11",https://arxiv.org/abs/1805.03597,"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. ","Using Machine Learning to Assess the Risk of and Prevent Water Main