Kyritsakas, G. orcid.org/0000-0003-0945-3754, Boxall, J.B. orcid.org/0000-0002-4681-6895 and Speight, V.L. orcid.org/0000-0001-7780-7863 (2023) A Big Data framework for actionable information to manage drinking water quality. AQUA — Water Infrastructure, Ecosystems and Society, 72 (5). pp. 701-720. ISSN 2709-8028
Abstract
Water utilities collect vast amounts of data, but they are stored and utilised in silos. Machine learning (ML) techniques offer the potential to gain deeper insight from such data. We set out a Big Data framework that for the first time enables a structured approach to systematically progress through data storage, integration, analysis, and visualisation, with applications shown for drinking water quality. A novel process for the selection of the appropriate ML method, driven by the insight required and the available data, is presented. Case studies for a water utility supplying 5.5 million people validate the framework and provide examples of its use to derive actionable information from data to help ensure the delivery of safe drinking water.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | Big Data analytics; data management; drinking water quality; machine learning; water supply systems |
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 Sciences Research Council EP/L015412/1 ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL EP/W037270/1 Engineering and Physical Sciences Research Council EP/N010124/1 ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL EP/N010124/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 04 May 2023 12:54 |
Last Modified: | 03 Oct 2024 14:51 |
Status: | Published |
Publisher: | IWA Publishing |
Refereed: | Yes |
Identification Number: | 10.2166/aqua.2023.218 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:198897 |