Advanced turbidity prediction for operational water supply planning

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.

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Identifiers

ORCID for Matthew, Paul Stevenson: orcid.org/0000-0001-6232-0745

ORCID for Cristian Bravo: orcid.org/0000-0003-1579-1565

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