Issue 4, 2019

Identification of the causes of drinking water discolouration from machine learning analysis of historical datasets

Abstract

Understanding the processes and interactions occurring within complex, ageing drinking water distribution systems is vital to ensuring the supply of safe drinking water. While many water quality samples are taken for regulatory compliance, the resulting data are often simply archived rather than being interrogated for deeper understanding due to their sparse nature across time and space and the difficulties of integrating with other data sources. This paper opens a new direction of research into distribution system water quality by mining large, historical drinking water quality datasets using machine learning techniques, in this case self-organizing maps (SOMs). Application of the methodology to national-scale datasets from three different UK water companies demonstrates the ability to identify the dominant mechanisms of iron release. Factors leading to discolouration such as low disinfectant residual, nitrification, and corrosion of unlined cast iron mains were identified at scales ranging from city to country, thereby enabling targeted interventions to ensure drinking water quality.

Graphical abstract: Identification of the causes of drinking water discolouration from machine learning analysis of historical datasets

Article information

Article type
Paper
Submitted
19 Oct 2018
Accepted
11 Jan 2019
First published
27 Feb 2019
This article is Open Access
Creative Commons BY license

Environ. Sci.: Water Res. Technol., 2019,5, 747-755

Identification of the causes of drinking water discolouration from machine learning analysis of historical datasets

V. L. Speight, S. R. Mounce and J. B. Boxall, Environ. Sci.: Water Res. Technol., 2019, 5, 747 DOI: 10.1039/C8EW00733K

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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