Kyritsakas, G., Speight, V. and Boxall, J. orcid.org/0000-0002-4681-6895 (2023) A data-driven model for the prediction of chlorine losses in water distribution trunk mains. In: IOP Conference Series: Earth and Environmental Science. 14th International Conference on Hydroinformatics, 04-08 Jul 2022, Bucharest, Romania. IOP Publishing , 012048.
Abstract
A data-driven model that uses 4 different machine learning (ML) algorithms (Feed forward artificial neural networks (ANN), Nonlinear autoregressive exogeneous (NARX) ANN, support vector machine and Random Forest) was designed for the prediction of chlorine loss events in water distribution trunk mains. The model, firstly, identifies past chlorine loss events and their associate flow or temperature events. Then, the detected past flow events and their associate past chlorine loss events are used to train the ML algorithms. The model was tested in 3 trunk mains of the same drinking water distribution system with similar diameter but with different characteristics, using each time a different combination of parameters (flow (input) - past chlorine losses (output) or flow, temperature, and chlorine (input) - past chlorine losses (output)) and machine learning algorithms. Results indicate that the model could predict a future chlorine loss event with a period between 2 to 10 hours depending on the parameter and ML algorithm used and the trunk mains’ hydraulic characteristics.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 Published under licence by IOP Publishing Ltd. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence (http://creativecommons.org/licenses/by/3.0). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. |
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) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 27 Jan 2023 10:32 |
Last Modified: | 27 Jan 2023 10:36 |
Status: | Published |
Publisher: | IOP Publishing |
Refereed: | Yes |
Identification Number: | 10.1088/1755-1315/1136/1/012048 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:195574 |
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