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Burst detection in district metering areas using flow subsequences clustering–reconstruction analysis
Frequent burst events in water distribution systems cause severe water loss and other environmental issues such as contamination and carbon emissions. The availability of massive monitored data has facilitated the development of data-driven burst detection methods. This paper proposes the flow subsequences clustering–reconstruction analysis method for burst detection in district metering areas (DMAs). The sliding window is used to create flow subsequence libraries for all time points of a day using a historical data set and thereafter the clustering–reconstruction analysis is conducted to obtain flow pattern libraries and reconstruction error subsequences. The threshold vector is determined by the detection matrix extracted from the reconstruction error subsequences at each time point. At the detection stage, the new flow subsequence is created and its reconstruction version is obtained based on the flow pattern library at the same time point. The new detection vector is extracted and compared with the threshold vector to identify bursts. The proposed method is applied to two real-world DMAs and its detection performance is demonstrated and compared with two previous methods. The proposed method is proven to be effective in detecting burst events with fewer false alarms. HIGHLIGHTS The flow subsequences clustering–reconstruction analysis method is proposed for burst detection.; The impact of parameterization on the detection performance is discussed.; The selection of the length of the historical data set directly affects the detection performance.; The proposed method performs better than the cumulative sum and shape similarity-based methods on two real-world DMA cases.;
Burst detection in district metering areas using flow subsequences clustering–reconstruction analysis
Frequent burst events in water distribution systems cause severe water loss and other environmental issues such as contamination and carbon emissions. The availability of massive monitored data has facilitated the development of data-driven burst detection methods. This paper proposes the flow subsequences clustering–reconstruction analysis method for burst detection in district metering areas (DMAs). The sliding window is used to create flow subsequence libraries for all time points of a day using a historical data set and thereafter the clustering–reconstruction analysis is conducted to obtain flow pattern libraries and reconstruction error subsequences. The threshold vector is determined by the detection matrix extracted from the reconstruction error subsequences at each time point. At the detection stage, the new flow subsequence is created and its reconstruction version is obtained based on the flow pattern library at the same time point. The new detection vector is extracted and compared with the threshold vector to identify bursts. The proposed method is applied to two real-world DMAs and its detection performance is demonstrated and compared with two previous methods. The proposed method is proven to be effective in detecting burst events with fewer false alarms. HIGHLIGHTS The flow subsequences clustering–reconstruction analysis method is proposed for burst detection.; The impact of parameterization on the detection performance is discussed.; The selection of the length of the historical data set directly affects the detection performance.; The proposed method performs better than the cumulative sum and shape similarity-based methods on two real-world DMA cases.;
Burst detection in district metering areas using flow subsequences clustering–reconstruction analysis
Mengke Zhao (author) / Haixing Liu (author) / Gengyan Li (author) / Chi Zhang (author)
2024
Article (Journal)
Electronic Resource
Unknown
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Distance-Based Burst Detection Using Multiple Pressure Sensors in District Metering Areas
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