Prediction of water–main failures and management of the associated risks using integrated predictive analytics approach

Abstract

Water distribution networks are vital to delivering potable water. They commonly include interconnected water mains (WMs), pumps and other hydraulic controls. Numerous WM failures have occurred due to ageing and harsh climate. This paper aims to predict the probability of future failures and integrate the predictions into risk-management strategies. The novelty lies in emphasising on the relevance to networks in cold regions like Canada. This study applied clustering and principal component analysis to the WM data from the Canadian City of Kitchener network. Clustering is shown to improve the failure prediction outcomes from the random forest algorithm and risk analysis output. Compared to without implementing clustering, the improvement reached 67–80% for WMs with high-rating risk. This paper successfully produced risk maps for Kitchener’s network, showing that only a small percentage (0.07–1.02%) of the existing WMs needs immediate action (prioritised rehabilitation or replacement). In addition to WM length and diameter, freeze index is shown to be an influence factor for failure predictions. The integrated, proactive approach discussed in this article can be applied to other cold-region WDNs. The results help reduce water losses and develop cost-effective, practical risk-management strategies.

Keywords:

• failure analysis
• risk management
• water mains
• water distribution
• networks
• data analytics
• machine learning

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Author contributions

The work has been done by AD under the supervision of FN and SL. The first draft of the manuscript was written by AD. Reviews, edits, and further analysis were performed by FN and SL. All authors commented on the manuscript. All authors read and approved this final manuscript.

Disclosure statement

The authors declare that they have no competing interests.

Data availability statement

Data generated or analysed during this study are available in the following repositories: [Dataset 1: Kitchener Water Mains, City of Waterloo Open Data, https://open-kitchenergis.opendata.arcgis.com/datasets/water-mains/explore]; [Dataset 2: Water Main Breaks, City of Waterloo Open Data, https://open-kitchenergis.opendata.arcgis.com/datasets/water-main-breaks/explore?location=43.459293%2C-80.434081%2C12.25]; and[Dataset 3: Kitchener Roads, City of Waterloo Open Data, https://open-kitchenergis.opendata.arcgis.com/datasets/City-of-Waterloo::roads/explore].

Additional information

Funding

This study received financial support from the Natural Sciences and Engineering Research Council of Canada (NSERC) through Discovery Grants.