Work

A Data-Driven Analysis for Understanding and Risk Estimation of Discolouration in Drinking Water Distribution Systems

Year: 2024

Type: conference-paper

Abstract: This paper presents machine learning analysis to understand the factors impacting iron concentrations and discolouration customer contacts in drinking water distribution systems. Fourteen years of network sampling and additional data from a large UK utility were collated, analysed, and interpreted using self-organising maps (SOMs), which include complex network theory (CNT) centrality metrics for the first time, investigating how possible explanatory variables interact. The outputs are used to inform ensemble decision trees for risk estimation of iron exceedance and customer contacts for each of the utility’s DMAs, helping inform proactive maintenance.

DOI: 10.3390/engproc2024069206

Authors: Grigorios Kyritsakas, Stewart Husband, Killian Gleeson, Katrina Flavell, Joby Boxall

Institutions: University of Sheffield, Delft University of Technology, Kelda Group (United Kingdom)

Language: English

Topic: Water Systems and Optimization

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Clean water and sanitation

Open Access status: gold

OpenAlex ID: W4404285363

FWCI:

Cites: 7

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Related to: 10