Burst Detection by Analyzing Shape Similarity of Time Series Subsequences in District Metering Areas
Publication: Journal of Water Resources Planning and Management
Volume 146, Issue 1
Abstract
The paper proposes a burst detection method that relies on shape similarity analysis of time series subsequences (i.e., slices of time series). Subsequence libraries are constructed using flow (or water demand) data. Increase-rate distance is used to evaluate the shape similarity between subsequences, and abnormal subsequences are those that have low shape similarity with others. An abnormal subsequence searching algorithm first is used to remove abnormal subsequences, and the remaining subsequences are used to form reference libraries. Then the shape similarity between newly collected subsequences and reference libraries is evaluated to detect bursts. In the detection, a modified version of the abnormal subsequence searching algorithm can reduce the number of false alarms by finding the don’t-care segment in subsequences and improve the method’s detection ability by crossover between night subsequences. The method was applied to a network’s hydraulic model and three real-life district metering areas. Results show that the method’s detection performance is only slightly affected by seasonal changes of data and is insensitive to data sets from different networks.
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Data Availability Statement
The following data, models, or code generated or used during the study are available from the corresponding author by request: Data generated by the hydraulic model, data of the three DMAs, and code for the proposed SSB method in MATLAB R2016b.
Acknowledgments
This work was jointly supported by the Water Major Program [2017ZX07201002]; and the National Key Research and Development Program of China for International Science & Innovation Cooperation Major Project between Governments [2016YFE0118800].
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©2019 American Society of Civil Engineers.
History
Received: Oct 25, 2018
Accepted: May 10, 2019
Published online: Oct 31, 2019
Published in print: Jan 1, 2020
Discussion open until: Mar 31, 2020
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