应用聚类算法识别供水管网爆管事故

doi:10.16511/j.cnki.qhdxxb.2017.25.051

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摘要作为现代都市的生命线，供水管网在人们的日常生活和城市的发展中占据不可替代的地位，然而爆管问题一直困扰着整个供水行业。针对供水管网爆管事故，该文以流量监测数据为基础，通过分析计量分区（district metering area，DMA）各个出入口流量计的数据关联性，使用聚类算法检测各种事件引起的异常流量数据，然后依据其出入口流量的变化特征识别管线爆管事故。结果表明：该方法应用在多出入口DMA时，识别效果会受到DMA出入口的数量与位置的影响，在出入口数量较少且位置适宜的前提下，能够准确识别爆管事故，并具有较低误报率。

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	刘书明
	吴以朋
	王晓婷
	刘友飞
	李佳杰

关键词 ：爆管识别, 聚类算法, 计量分区 (DMA), 供水管网

Abstract：Pipe bursts are a universal problem in water supply systems, which can severely disrupt daily life and urban development. This study correlates flow measurements collected from inlets and outlets of a district metering area (DMA) to quickly identify bursts. The analysis uses a clustering algorithm to detect abnormal flow data i.e., outliers. Then, some outliers are identified as bursts according to the inlet and outlet flow fluctuation characteristics. The results indicate that the number and locations of the inlets and outlets affect the detection performance. The system can accurately detect bursts and insure a low false positive rate when there are a relatively small number of inlets and outlets and the locations are well placed.

Key words： burst detection clustering algorithm district metering area (DMA) water distribution system

收稿日期: 2016-07-08 出版日期: 2017-10-15

ZTFLH:

TU991.33

引用本文:

刘书明, 吴以朋, 王晓婷, 刘友飞, 李佳杰. 应用聚类算法识别供水管网爆管事故[J]. 清华大学学报（自然科学版）, 2017, 57(10): 1096-1101.
LIU Shuming, WU Yipeng, WANG Xiaoting, LIU Youfei, LI Jiajie. Clustering algorithm for burst detection in water distribution systems. Journal of Tsinghua University(Science and Technology), 2017, 57(10): 1096-1101.

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http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2017.25.051 或 http://jst.tsinghuajournals.com/CN/Y2017/V57/I10/1096

[1]	Mounce S R, Khan A, Wood A S, et al. Sensor-fusion of hydraulic data for burst detection and location in a treated water distribution system[J]. Information Fusion, 2003, 4(3):217-229.
[2]	Mounce S R, Boxall J B, Machell J. Development and verification of an online artificial intelligence system for detection of bursts and other abnormal flows[J]. Journal of Water Resources Planning and Management, 2010, 136(3):309-318.
[3]	Romano M, Kapelan Z, Savic D A. Automated detection of pipe bursts andother events in water distribution systems[J]. Journal of Water Resources Planning and Management, 2014, 140(4):457-467.
[4]	Ye G L, Fenner R A. Kalman filtering of hydraulic measurements for burst detection in water distribution systems[J]. Journal of Pipeline Systems Engineering and Practice, 2011, 2(1):14-22.
[5]	Ye G L, Fenner R A. Weighted least squares with expectation-maximization algorithm for burst detection in U.K. water distribution systems[J]. Journal of Water Resources Planning and Management, 2014, 140(4):417-424.
[6]	凌文翠, 张涛, 强志民, 等. 城市供水管网独立计量区域的研究与应用进展[J]. 中国给水排水, 2011, 27(13):46-50.LING Wencui, ZHANG Tao, QIANG Zhimin, et al. Research and application of district metering area for urban water distribution network[J]. China Water & Wastewater, 2011, 27(13):46-50. (in Chinese)
[7]	Wu Y, Liu S, Wu X, et al. Burst detection in district metering areas using a data driven clustering algorithm[J]. Water Research, 2016, 100:28-37.
[8]	Tan P N, Steinbach M, Kumar V. Introduction to Data Mining[M]. New Jersey:Addison-Wesley, 2005.
[9]	Rodriguez A, Laio A. Clustering by fast search and find of density peaks[J]. Science, 2014, 344(6191):1492-1496.
[10]	Loureiro D, Amado C, Martins A, et al. Water distribution systems flow monitoring and anomalous event detection:A practical approach[J]. Urban Water Journal, 2016, 13(3):242-252.
[11]	Metz C E. Basic principles of ROC analysis[J]. Seminars in Nuclear Medicine, 1978, 8(4):283-298.