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.
Highlights
Water leakage in water distribution systems (WDS) is a common issue and has caused widespread concern in recent years [1]
This paper presents the application of the Dynamic Time Warping (DTW) algorithm algorithm to study the similarity of patterns in such flow data
According to the analysis results, it indicated that patterns of water demand and supervised learning methods can improve the effect of burst-event detection
Summary
Water leakage in water distribution systems (WDS) is a common issue and has caused widespread concern in recent years [1]. One of the major forms of leakage are those caused by burst events (high volume and short duration). An underground burst in WDS may not be reported for a long time, resulting in a large amount of leakage [2]. Burst detection is a challenging problem that plagues water supply industries. Timely leakage detection in WDS is of great significance for ensuring the continuance of water supply, as well as public safety.
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