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Lookup NU author(s): Dr Wanqing ZhaoORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2013 IEEE. The prediction of combined sewer overflow (CSO) operation in urban environments presents a challenging task for water utilities. The operation of CSOs (most often in heavy rainfall conditions) prevents houses and businesses from flooding. However, sometimes, CSOs do not operate as they should, potentially bringing environmental pollution risks. Therefore, CSOs should be appropriately managed by water utilities, highlighting the need for adapted decision support systems. This paper proposes an automated CSO predictive model construction methodology using field monitoring data, as a substitute for the commonly established hydrological-hydraulic modeling approach for time-series prediction of CSO statuses. It is a systematic methodology factoring in all monitored field variables to construct time-series dependencies for CSO statuses. The model construction process is largely automated with little human intervention, and the pertinent variables together with their associated time lags for every CSO are holistically and automatically generated. A fast least absolute shrinkage and selection operator solution generating scheme is proposed to expedite the model construction process, where matrix inversions are effectively eliminated. The whole algorithm works in a stepwise manner, invoking either an incremental or decremental movement for including or excluding one model regressor into, or from, the predictive model at every step. The computational complexity is thereby analyzed with the pseudo code provided. Actual experimental results from both single-step ahead (i.e., 15 min) and multistep ahead predictions are finally produced and analyzed on a U.K. pilot area with various types of monitoring data made available, demonstrating the efficiency and effectiveness of the proposed approach.
Author(s): Zhao W, Beach TH, Rezgui Y
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Systems, Man, and Cybernetics: Systems
Year: 2019
Volume: 49
Issue: 6
Pages: 1254-1269
Online publication date: 21/08/2017
Acceptance date: 22/06/2017
Date deposited: 22/09/2022
ISSN (print): 2168-2216
ISSN (electronic): 2168-2232
Publisher: IEEE
URL: https://doi.org/10.1109/TSMC.2017.2724440
DOI: 10.1109/TSMC.2017.2724440
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