A data-driven analysis for understanding and risk estimation of discolouration in drinking water distribution systems

Kyritsakas, G. orcid.org/0000-0003-0945-3754, Husband, S. orcid.org/0000-0002-2771-1166, Gleeson, K. et al. (2 more authors) (2024) A data-driven analysis for understanding and risk estimation of discolouration in drinking water distribution systems. In: Alvisi, S., Franchini, F. and Marsili, V., (eds.) Engineering proceedings. International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024), 01-04 Jul 2024, Ferrara, Italy. MDPI

Abstract

Metadata

Item Type: Proceedings Paper
Authors/Creators:
Editors:
  • Alvisi, S.
  • Franchini, F.
  • Marsili, V.
Copyright, Publisher and Additional Information:

© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Keywords: discolouration; machine learning; complex network theory; big data
Dates:
  • Published: 11 November 2024
  • Published (online): 11 November 2024
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > School of Mechanical, Aerospace and Civil Engineering
Depositing User: Symplectic Sheffield
Date Deposited: 17 Dec 2024 10:22
Last Modified: 17 Dec 2024 11:00
Status: Published
Publisher: MDPI
Refereed: Yes
Identification Number: 10.3390/engproc2024069206
Related URLs:
Sustainable Development Goals:
  • Sustainable Development Goals: Goal 6: Clean Water and Sanitation
Open Archives Initiative ID (OAI ID):

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