Detection of Anomalies in Water Networks by Functional Data Analysis
Scholar | Other documents of the author: Millán Roures, Laura; Epifanio, Irene; Martínez García, Vicente
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TitleDetection of Anomalies in Water Networks by Functional Data Analysis
PublisherHindawi Publishing Corporation
A functional data analysis (FDA) based methodology for detecting anomalous flows in urban water networks is introduced. Primary hydraulic variables are recorded in real-time by telecontrol systems, so they are functional ... [+]
A functional data analysis (FDA) based methodology for detecting anomalous flows in urban water networks is introduced. Primary hydraulic variables are recorded in real-time by telecontrol systems, so they are functional data (FD). In the first stage, the data are validated (false data are detected) and reconstructed, since there could be not only false data, but also missing and noisy data. FDA tools are used such as tolerance bands for FD and smoothing for dense and sparse FD. In the second stage, functional outlier detection tools are used in two phases. In Phase I, the data are cleared of anomalies to ensure that data are representative of the in-control system. The objective of Phase II is system monitoring. A new functional outlier detection method is also proposed based on archetypal analysis. The methodology is applied and illustrated with real data. A simulated study is also carried out to assess the performance of the outlier detection techniques, including our proposal. The results are very promising. [-]
Investigation projectDPI2017-87333-R ; UJI-B2017-13
Bibliographic citationLaura Millán-Roures, Irene Epifanio, and Vicente Martínez, “Detection of Anomalies in Water Networks by Functional Data Analysis,” Mathematical Problems in Engineering, vol. 2018, Article ID 5129735, 13 pages, 2018. https://doi.org/10.1155/2018/5129735.
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Except where otherwise noted, this item's license is described as Copyright © 2018 Laura Millan-Roures et al. Tis is an open access article distributed under the Creative Commons Attribution ´ License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.