Using Machine Learning to Assess the Risk of and Prevent Water Main Breaks
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.
水道管の破損リスク予測
主電源が故障した履歴データ、パイプの記述子、およびその他のデータソースを使用して、いくつかのモデルの機能を評価しました。？
３年後の破損を評価
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. GBDTを使用
エキスパートの経験則を超えた
Syracuseでデプロイされている
予測でリスクの高い52のうち42は実際に破損した
https://www.youtube.com/watch?v=-tc5tkpICHc