Our limited understanding and quantification of the variety and complexity of chemical, physical and biological reactions and interactions occurring within drinking water distribution systems currently prohibit the development of a deterministic model of water quality. The causes of known water quality anomalies can however be investigated through mining the large volumes of water quality, hydraulic and asset data currently being collected by utility companies. The data-driven methodology described here permits historical cause-effect linkages to be identified in a scalable, largely automatable fashion. Under Distribution System Integrated Modelling (DSIM), spatio-temporal searches within the set of pipes that typically lie upstream of a known water quality anomaly are used to identify possible causes. Understanding of the flow paths that connect causes and effects are derived from the results of hydraulic network simulations. DSIM was used to investigate contacts regarding discolouration and smell/taste issues from customers within a Water Supply Zone in England, UK, over a six-year period. 17.6% of discolouration issues and 17.4% of smell/taste issues were linked to maintenance jobs using the methodology, much smaller proportions than were identified using radial cause searches. The DSIM search results contained a greater proportion of one-to-one linkages and so are less ambiguous than the results of the radial spatio-temporal searches. DSIM was found to be a useful and informative tool for data mining multiple water quality related datasets. © 2012 Elsevier Ltd.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)