Burst Detection in District Metering Areas Using Deep Learning Method
Publication: Journal of Water Resources Planning and Management
Volume 146, Issue 6
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.
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Data Availability Statement
The following data and the model used in this study can be made available by the corresponding author on request: data of simulated experiments, data of synthetic experiments, and codes for the proposed method in Spyder 3.2.4.
Acknowledgments
This work was jointly supported by the National Natural Science Foundation of China (Grant No. 5187090620) and the China Postdoctoral Science Foundation (Grant No. 2018M631495).
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Published In
Journal of Water Resources Planning and Management
Volume 146 • Issue 6 • June 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Apr 19, 2019
Accepted: Jan 7, 2020
Published online: Mar 23, 2020
Published in print: Jun 1, 2020
Discussion open until: Aug 23, 2020
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