Shepherd, W. orcid.org/0000-0003-4434-9442, Mounce, S., Gaffney, J. et al. (5 more authors) (2023) Cloud-based artificial Iintelligence analytics to assess combined sewer overflow performance. Journal of Water Resources Planning and Management, 149 (10). 04023051. ISSN 0733-9496
Abstract
Discharges from combined sewer overflows (CSO) are unacceptable, particularly when they are not linked to wet weather. This paper presents an evaluation of an online artificial-intelligence-based analytics system to give early warning of such overflows due to system degradation. It integrates a cloud-based data-driven system using artificial neural networks and fuzzy logic with near real-time communications, taking advantage of the increasingly available real-time monitoring of water depths in CSO chambers. The data-driven system has been developed to be applicable to the vast majority of CSO and requiring a minimum period of data for training. Results are presented for a live assessment of 50 CSO assets over a six-month period, demonstrating continuous assessment of performance and reduction of CSO discharges. The system achieved a high true positive rate (86.7% on confirmed positives) and low false positive rate (3.4%). Such early warnings of CSO performance degradation are vital to proactively manage our aging water infrastructure and to achieve acceptable environmental, regulatory, and reputational performance. The system enables improved performance from legacy infrastructure without gross capital investment.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 ASCE. This is an author-produced version of a paper subsequently published in Journal of Water Resources Planning and Management. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Combined sewer overflows; Artificial neural networks; Fuzzy inference system; Cloud computing; Internet of Things; Rainfall radar; Depth prediction |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Civil and Structural Engineering (Sheffield) |
Funding Information: | Funder Grant number SIEMENS PLC UNSPECIFIED SIEMENS PLC UNSPECIFIED |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 06 Jun 2023 16:15 |
Last Modified: | 02 Aug 2023 15:19 |
Status: | Published |
Publisher: | American Society of Civil Engineers |
Refereed: | Yes |
Identification Number: | 10.1061/JWRMD5.WRENG-5859 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:199848 |