Burst detection in district metering areas using a data driven clustering algorithm.

This paper describes a novel methodology for burst detection in a water distribution system. The proposed method has two stages. In the first stage, a clustering algorithm was employed for outlier detection, while the second stage identified the presence of bursts. An important feature of this method is that data analysis is carried out dependent on multiple flow meters whose measurements vary simultaneously in a district metering area (DMA). Moreover, the clustering-based method can automatically cope with non-stationary conditions in historical data; namely, the method has no prior data selection process. An example application of this method has been implemented to confirm that relatively large bursts (simulated by flushing) with short duration can be detected effectively. Noticeably, the method has a low false positive rate compared with previous studies and appearance of detected abnormal water usage consists with weather changes, showing great promise in real application to multi-inlet and multi-outlet DMAs.