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Automated model construction for combined sewer overflow (CSO) prediction based on efficient LASSO algorithm

Zhao, Wanqing, Beach, Thomas and Rezgui, Yacine 2017. Automated model construction for combined sewer overflow (CSO) prediction based on efficient LASSO algorithm. IEEE Transactions on Systems, Man, and Cybernetics: Systems 49 (6) , pp. 1254-1269. 10.1109/TSMC.2017.2724440

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Abstract

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 modelling 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 LASSO (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 analysed with the pseudo code provided. Actual experimental results from both single-step ahead (i.e., 15 mins) and multi-step ahead predictions are finally produced and analysed on a UK pilot area with various types of monitoring data made available, demonstrating the efficiency and effectiveness of the proposed approach.

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Engineering
Publisher: IEEE
ISSN: 2168-2216
Funders: EU Seventh Framework Programme
Date of First Compliant Deposit: 1 August 2017
Date of Acceptance: 22 June 2017
Last Modified: 03 Jul 2019 04:01
URI: http://orca.cf.ac.uk/id/eprint/103195

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