Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

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

Zhao, Wanqing ORCID:, Beach, Thomas H. ORCID: and Rezgui, Yacine ORCID: 2019. Automated model construction for combined sewer overflow prediction based on efficient LASSO algorithm. IEEE Transactions on Systems Man and Cybernetics: Systems 49 (6) , pp. 1254-1269. 10.1109/TSMC.2017.2724440

[thumbnail of 08013727.pdf]
PDF - Published Version
Available under License Creative Commons Attribution.

Download (1MB) | Preview


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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Additional Information: This is an open access article under the terms of the CC-BY Attribution 4.0 International license.
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: 07 May 2023 10:52

Citation Data

Cited 19 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item


Downloads per month over past year

View more statistics