The University of Southampton
University of Southampton Institutional Repository

Advanced turbidity prediction for operational water supply planning

Advanced turbidity prediction for operational water supply planning
Advanced turbidity prediction for operational water supply planning
Turbidity is an optical quality of water caused by suspended solids that give the appearance of ‘cloudiness’. While turbidity itself does not directly present a hazard to human health, it can be an indication of poor water quality and mask the presence of parasites such as Cryptosporidium. It is, therefore, a recommendation of the World Health Organisation (WHO) that turbidity should not exceed a level of 1 Nephelometric Turbidity Unit (NTU) before chlorination. For a drinking water supplier, turbidity peaks can be highly disruptive requiring the temporary shutdown of a water treatment works. Such events must be carefully managed to ensure continued supply; to recover the supply deficit, water stores must be depleted or alternative works utilised. Machine learning techniques have been shown to be effective for the modelling of complex environmental systems, often used to help shape environmental policy. We contribute to the literature by adopting such techniques for operational purposes, developing a decision support tool that predicts >1 NTU turbidity events up to seven days in advance allowing water supply managers to make proactive interventions. We apply a Generalised Linear Model (GLM) and a Random Forest (RF) model for the prediction of >1 NTU events. AUROC scores of over 0.80 at five of six sites suggest that machine learning techniques are suitable for predicting turbidity peaking events. Furthermore, we find that the RF model can provide a modest performance boost due to its stronger capacity to capture nonlinear interactions in the data.
Analytics, Water Quality, Turbidity Prediction
0167-9236
72-84
Stevenson, Matthew, Paul
c11bc02f-acf9-4e13-a703-8ed273bcd4e8
Bravo, Cristian
b22c4145-644e-40ee-85d8-431c59c3c71b
Stevenson, Matthew, Paul
c11bc02f-acf9-4e13-a703-8ed273bcd4e8
Bravo, Cristian
b22c4145-644e-40ee-85d8-431c59c3c71b

Stevenson, Matthew, Paul and Bravo, Cristian (2019) Advanced turbidity prediction for operational water supply planning. Decision Support Systems, 119, 72-84. (doi:10.1016/j.dss.2019.02.009).

Record type: Article

Abstract

Turbidity is an optical quality of water caused by suspended solids that give the appearance of ‘cloudiness’. While turbidity itself does not directly present a hazard to human health, it can be an indication of poor water quality and mask the presence of parasites such as Cryptosporidium. It is, therefore, a recommendation of the World Health Organisation (WHO) that turbidity should not exceed a level of 1 Nephelometric Turbidity Unit (NTU) before chlorination. For a drinking water supplier, turbidity peaks can be highly disruptive requiring the temporary shutdown of a water treatment works. Such events must be carefully managed to ensure continued supply; to recover the supply deficit, water stores must be depleted or alternative works utilised. Machine learning techniques have been shown to be effective for the modelling of complex environmental systems, often used to help shape environmental policy. We contribute to the literature by adopting such techniques for operational purposes, developing a decision support tool that predicts >1 NTU turbidity events up to seven days in advance allowing water supply managers to make proactive interventions. We apply a Generalised Linear Model (GLM) and a Random Forest (RF) model for the prediction of >1 NTU events. AUROC scores of over 0.80 at five of six sites suggest that machine learning techniques are suitable for predicting turbidity peaking events. Furthermore, we find that the RF model can provide a modest performance boost due to its stronger capacity to capture nonlinear interactions in the data.

Text
Advanced turbidity prediction for operational water supply planning - Accepted Manuscript
Restricted to Repository staff only until 3 March 2021.
Request a copy

More information

Accepted/In Press date: 26 February 2019
e-pub ahead of print date: 5 March 2019
Published date: April 2019
Keywords: Analytics, Water Quality, Turbidity Prediction

Identifiers

Local EPrints ID: 428675
URI: https://eprints.soton.ac.uk/id/eprint/428675
ISSN: 0167-9236
PURE UUID: 3a316d6c-1fd4-44d4-9841-c198f4208650
ORCID for Cristian Bravo: ORCID iD orcid.org/0000-0003-1579-1565

Catalogue record

Date deposited: 06 Mar 2019 17:30
Last modified: 10 Dec 2019 01:41

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×