A Big Data framework for actionable information to manage drinking water quality

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

Metadata

Item Type: Article
Authors/Creators:
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:
  • Published: 1 May 2023
  • Published (online): 26 April 2023
  • Accepted: 8 April 2023
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):

Download

Export

Statistics