Forecasting bacteriological presence in treated drinking water using machine learning

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

Metadata

Item Type: Article
Authors/Creators:
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:
  • Published: 30 June 2023
  • Published (online): 30 June 2023
  • Accepted: 16 June 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 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):

Download

Export

Statistics