Burst detection in district metering areas using flow subsequences clustering–reconstruction analysis

Abstract

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.