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Real-Time Burst Detection in District Metering Areas in Water Distribution System Based on Patterns of Water Demand with Supervised Learning

State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
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Water 2018, 10(12), 1765; https://doi.org/10.3390/w10121765
Received: 9 November 2018 / Accepted: 28 November 2018 / Published: 1 December 2018
(This article belongs to the Section Water Use and Scarcity)
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Abstract

This paper proposes a new method to detect bursts in District Metering Areas (DMAs) in water distribution systems. The methodology is divided into three steps. Firstly, Dynamic Time Warping was applied to study the similarity of daily water demand, extract different patterns of water demand, and remove abnormal patterns. In the second stage, according to different water demand patterns, a supervised learning algorithm was adopted for burst detection, which established a leakage identification model for each period of time, respectively, using a sliding time window. Finally, the detection process was performed by calculating the abnormal probability of flow during a certain period by the model and identifying whether a burst occurred according to the set threshold. The method was validated on a case study involving a DMA with engineered pipe-burst events. The results obtained demonstrate that the proposed method can effectively detect bursts, with a low false-alarm rate and high accuracy. View Full-Text
Keywords: burst detection; district metering areas; dynamic time warping; patterns of water demand; supervised learning burst detection; district metering areas; dynamic time warping; patterns of water demand; supervised learning
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Huang, P.; Zhu, N.; Hou, D.; Chen, J.; Xiao, Y.; Yu, J.; Zhang, G.; Zhang, H. Real-Time Burst Detection in District Metering Areas in Water Distribution System Based on Patterns of Water Demand with Supervised Learning. Water 2018, 10, 1765.

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