WU Yi-wen,DU Kun,WU Han?qing,et al.Water Supply Network Burst Detection Based on Least Squares Support Vector Machine Interactive Prediction[J].China Water & Wastewater,2022,38(9):58-63.
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Water Supply Network Burst Detection Based on Least Squares Support Vector Machine Interactive Prediction

China Water & Wastewater[ISSN:1000-4062/CN:12-1073/TU] volume: 第38卷 Number: 第9期 Page: 58-63 Column: Date of publication: 2022-05-01
Author(s):
WU Yi-wen1;  DU Kun1;  WU Han?qing2;  LUO Xiong-wu3;  SONG Zhi-gang1;  HUANG Le-feng1
（1. College of Civil Engineering and Architecture， Kunming University of Science and Technology， Kunming 650500， China； 2. Urban Planning & Design Institute of Yunnan Province， Kunming 650228， China； 3. Kunming Guanfang Architecture & Design Inc.， Kunming 650051， China）
Keywords:
water supply network;  pipe burst;  LSSVM;  interactive prediction
Abstract:
With an increasing number of SCADA system installed in the water supply network, the method of pipe burst detection based on data prediction has been paid more and more attention. Most of the traditional methods based on data prediction predict the current monitoring value according to the historical monitoring data of single point flow and pressure. When the difference between the predicted value and the monitoring value exceeds the threshold, the pipe burst was identified. However, practical experience shows that the loss and error of monitoring data will seriously affect the single point prediction results, and then cause frequent false positives and missing reports. Considering the spatial correlation between actual water consumption and monitoring data (for example, the closer the distance of water pressure monitoring points is, the greater the correlation of monitoring data is), water supply network pipe burst was detected based on least squares support vector machine (LSSVM) interactive prediction. A multi input and single output LSSVM interactive prediction model was constructed based on the monitoring data of different positions in the pipe network. A double standard deviation was selected as the threshold value for pipe burst detection, and the detection results were compared with those of traditional Kalman filtering. LSSVM interactive prediction model could reduce the influence of data loss and data error on the prediction results, and was more sensitive to small pipe burst, thus effectively improving the performance of pipe burst detection based on data prediction.
Last Update: 2022-05-01