图 书 馆
使用机器学习评估和预防水管破裂的风险Using Machine Learning to Assess the Risk of and Prevent Water Main Breaks |
|
| 课程网址: | http://videolectures.net/kdd2018_kumar_water_main_breaks/ |
| 主讲教师: | Avishek Kumar |
| 开课单位: | 芝加哥大学数据科学和公共政策中心 |
| 开课时间: | 2018-11-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | 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. |
| 关 键 词: | 美国的水基础设施; 供水系统故障; 水管发生故障; 评估水管破裂的风险 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-02-09：cyh |
| 最后编审: | 2023-02-09：cyh |
| 阅读次数: | 30 |