Burst detection based on multi-time monitoring data from multiple pressure sensors in district metering areas

This research article presents a data-driven approach for detecting bursts in water distribution networks (WDNs). The framework uses spatiotemporal information from monitoring pressure and unsupervised learning model. This approach employs three stages: (1) benchmark dataset acquisition, (2) spatiotemporal information analysis, and (3) burst detection model construction. First, the benchmark datasets were the normal dataset initially obtained by the clustering algorithm. Second, spatiotemporal information features are extracted from multimoment time windows from multiple sensors, including the distance and shape features. Third, burst detection was performed based on the isolation forest technique. A WDN is used to evaluate the performance of the method. Results show that the method can effectively detect the burst.