Kazemi, E., Kyritsakas, G., Husband, S. orcid.org/0000-0002-2771-1166 et al. (3 more authors) (2023) Predicting iron exceedance risk in drinking water distribution systems using machine learning. In: IOP Conference Series: Earth and Environmental Science. 14th International Conference on Hydroinformatics, 04-08 Jul 2022, Bucharest, Romania. IOP Publishing , 012047.
Abstract
A Machine Learning approach has been developed to predict iron threshold exceedances in sub-regions of a drinking water distribution network from data collected the previous year. Models were trained using parameters informed by Self-Organising Map analysis based on ten years of water quality sampling data, pipe data and discolouration customer contacts from a UK network supplying over 2.3 million households. Twenty combinations of input parameters (network conditions) and three learning algorithms (Random Forests, Support Vector Machines and RUSBoost Trees) were tested. The best performing model was found to be Random Forests with input parameters of iron, turbidity, 3-day Heterotrophic Plate Counts, and high priority dead ends per District Metered Area. Different exceedance levels were tested and prediction accuracies of above 70% were achieved for UK regulatory concentration of 200 µg/L. Predicted probabilities per network sub-region were used to provide relative risk ranking to inform proactive management and investment decisions.
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) |
Funding Information: | Funder Grant number YORKSHIRE WATER SERVICES LIMITED YW.200035 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 27 Jan 2023 09:46 |
Last Modified: | 27 Jan 2023 10:07 |
Status: | Published |
Publisher: | IOP Publishing |
Refereed: | Yes |
Identification Number: | 10.1088/1755-1315/1136/1/012047 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:195577 |
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