CHEN Jing-yu,XIAO Shi-yun,FENG Xin.A Machine Learning Method for Leakage Localization of Water Distribution Network[J].China Water & Wastewater,2021,37(7 7):58-65.
A Machine Learning Method for Leakage Localization of Water Distribution Network
China Water & Wastewater[ISSN:1000-4062/CN:12-1073/TU]
volume:
第37卷
Number:
7 7
Page:
58-65
Column:
Date of publication:
2021-04-01
- Keywords:
- leakage zone identification; binary iteration method; k-means; random forest classifier; feature selection; mean decrease accuracy (MDA)
- Abstract:
- When pattern recognition is used to identify the leakage of water distribution network, due to the similarity of leakage characteristics, the accuracy of model training will be relatively low if each node is used as the category label of the classifier. Therefore, nodes with similar leakage characteristics can be clustered to form regions, and each region can be used as a category label of the classifier to improve the accuracy of model training.A method called binary iteration based on random forest classifier was proposed to identify the leakage zone. According to the leakage matrix of the leakage zone identified by the last iteration, k-means clustering was used to cluster the nodes into two kinds(zones that contained leaks and zones that did not contain leaks), so as to identify the region containing the leaks. As the candidate leakage zone decreased, the number of sensors useful to identify the leakage also decreased, so mean decrease accuracy (MDA) was used to select the features required by the classifier, so as to reduce the features required by the classifier training under the condition that the identification accuracy was constant. Compared with the method that the leakage zone was identified directly, binary iteration method could reduce the blindness of selecting the division number, and it was more specific for the identification of leakage zone,which improved the accuracy and efficiency of the identification of the leakage zone.
Last Update:
2021-04-01