Water Supply Pipeline Operation Anomaly Mining and Spatiotemporal Correlation Study

• Published in: Journal of pipeline systems 2024-11, Vol.15 (4)
• Main Authors: Yang, Yanmei, Liu, Ao, Wang, Zegen, Yong, Zhiwei, Sun, Tao, Li, Jie, Ma, Guoli
• Format: Article
• Language: English
• Subjects: Algorithms; Anomalies; City centres; Clustering; Confidence intervals; Correlation; Euclidean geometry; Hydrostatic pressure; Monitoring; Pressure; Railway stations; Statistical analysis; Statistical methods; Technical Papers; Water pipelines; Water pressure; Water supply; Water supply systems
• ISSN: 1949-1190; 1949-1204
• DOI: 10.1061/JPSEA2.PSENG-1589

AbstractThe recurrent manifestation of anomalies in water supply network systems exerts a profound influence on individuals’ daily lives. Despite this impact, contemporary research on urban water supply networks reveals a conspicuous lack in the thorough examination of spatiotemporal patterns and the relevance of these anomalies. This investigation meticulously scrutinizes anomalies within a specified segment of the water supply pipe network located in a county in southwest China. Clustering algorithms [K-means and density-based spatial clustering of applications with noise (DBSCAN)] and statistical methods (standard deviation) identify anomalous water pressure. Subsequently, the Apriori algorithm is utilized to extract association rules for different types of anomalies, and these rules are compared with user similarity, quantified through standard Euclidean distance. The key findings are as follows. First, anomalies in water pressure are predominantly concentrated in May, September, and November. On a 24-h scale, the highest incidence of anomalies occurs between 6:00 a.m. and 9:00 a.m. Areas with the highest anomaly occurrence are primarily situated near the city center and the railway station. Second, correlation rules exist among occurrences of anomalous values at various monitoring sites within the study area. In concrete terms, identical water pressure abnormal types frequently co-occur (confidence level >50%, support level >3%) at diverse monitoring sites, with this correlation linked to the types of users around the monitoring sites. Finally, the categorization of anomalies results in significantly enhanced accuracy in correlation rule outcomes, surpassing the comprehensive analysis of anomalies overall.