A review of data-driven approaches for burst detection in water distribution systems

作者：Wu Yipeng; Liu Shuming^*

来源：Urban Water Journal, 2017, 14(9): 972-983.

DOI：10.1080/1573062X.2017.1279191

• Burst detection
• data-driven
• water distribution system
• district metering area
• uncertainty

This study focuses on data-driven approaches for burst detection and classifies them into three categories: classification method, prediction-classification method and statistical method. The performance of these methods is discussed. By analysing uncertainty in burst detection, this paper revealed that non-stationary monitoring data and limitations present in these methods challenge the reliability of detection results. Data pre-processing and probabilistic solutions to deal with the uncertainty are summarised. From these findings and discussions, this paper concludes and recommends that: a) data-driven approaches are promising in real-life burst detection and reducing false alarms is an important issue; b) more comprehensive performance evaluation might be necessary, in particular regarding detectable burst size; c) further research on new methods employing multivariate analysis and a new category based on clustering analysis would be beneficial to tackle uncertainty; d) more focus on the use of pressure data might facilitate burst location and reduce investment in burst detection.

• 出版日期2017
• 单位清华大学