Burst Detection in District Metering Areas Using Deep Learning Method

Abstract

Water loss reduction is important in sustainable water resource management. As one of the main water loss control methods, early detection of hydraulic accidents in district metering areas (DMAs) has emerged as a research focus. This study presents a data-driven method for burst detection which consists of three stages: prediction, classification and correction. A prediction stage is used to improve accuracy of flow prediction, a classification stage utilizes multiple thresholds to make the method robust to time variation, and an outlier feedback correction stage allows consecutive detection of outliers. The proposed method was capable of triggering burst alarms with 99.80% detection accuracy (DA), 85.71% true-positive rate (TPR), and 0.14% false-positive rate (FPR) in simulated experiments, and 99.77% DA, 94.82% TPR and 0.21% FPR in synthetic experiments over a 10-min detection time in a real-life DMA. The identifiable minimum burst rate was as low as 2.79% of average DMA inflow. The proposed method outperformed the single threshold-based method, window size–based method, and clustering-based method. It provides a sensitive and effective solution for burst detection in water distribution systems.