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A review of data-driven approaches for burst detection in water distribution systems
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.
A review of data-driven approaches for burst detection in water distribution systems
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.
A review of data-driven approaches for burst detection in water distribution systems
Wu, Yipeng (author) / Liu, Shuming
Urban water journal ; 14
2017
Article (Journal)
English
Water distribution systems , Categories , Water engineering , Bursting , Classification , data-driven , False alarms , Uncertainty , Lifting tackle , Water distribution , water distribution system , Methodology , uncertainty , Detection , Burst detection , Data processing , Solutions , Data , Uncertainty analysis , Multivariate analysis , Evaluation , district metering area , Clustering , Cluster analysis , Methods , Burst size
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