ID:
publications-3701
Type:
article
Year:
2017
Authors:
Wu, Yipeng and Wu, Yipeng and Liu, Shuming and Liu, Shuming
Title:
A review of data-driven approaches for burst detection in water distribution systems
Venue/Journal:
Urban Water Journal
DOI:
10.1080/1573062x.2017.1279191
Research type:
Water System:
Technical Focus:
Abstract:
AbstractThis 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...
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