Kyritsakas, G., Boxall, J. orcid.org/0000-0002-4681-6895 and Speight, V. (2023) Forecasting bacteriological presence in treated drinking water using machine learning. Frontiers in Water, 5. 1199632. ISSN 2624-9375
Abstract
A novel data-driven model for the prediction of bacteriological presence, in the form of total cell counts, in treated water exiting drinking water treatment plants is presented. The model was developed and validated using a year of hourly online flow cytometer data from an operational drinking water treatment plant. Various machine learning methods are compared (random forest, support vector machines, k-Nearest Neighbors, Feed-forward Artificial Neural Network, Long Short Term Memory and RusBoost) and different variables selection approaches are used to improve the model's accuracy. Results indicate that the model could accurately predict total cell counts 12 h ahead for both regression and classification-based forecasts—NSE = 0.96 for the best regression model, using the K-Nearest Neighbors algorithm, and Accuracy = 89.33% for the best classification model, using the combined random forest, K-neighbors and RusBoost algorithms. This forecasting horizon is sufficient to enable proactive operational interventions to improve the treatment processes, thereby helping to ensure safe drinking water.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 Kyritsakas, Boxall and Speight. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
Keywords: | drinking water treatment; machine learning; online flow cytometry; total cell counts prediction; forecasting mode |
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 ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL UNSPECIFIED COMMERCIAL MASTER ACCOUNT UNSPECIFIED Engineering and Physical Sciences Research Council EP/S023666/1 Engineering and Physical Sciences Research Council EP/N010124/1 ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL EP/N010124/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 07 Jul 2023 10:28 |
Last Modified: | 07 Jul 2023 10:28 |
Status: | Published |
Publisher: | Frontiers Media SA |
Refereed: | Yes |
Identification Number: | 10.3389/frwa.2023.1199632 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:201315 |