Using Machine Learning Techniques to Optimize Infrastructure Investment for the Water Distribution Network

To mitigate the disruptions caused by pipe failures, water utility managers must be able to anticipate network degradation in the short to medium term. Unfortunately, predicting this deterioration can be a highly intricate and uncertain endeavor. The main culprit for this inherent complexity is the fact that a water main wear rate depends on its physical and structural characteristics but also on environmental and operational factors. In nearly all cases, the number of possible parameter combinations makes highly vulnerable pipes extremely difficult, if not impossible, to find with a manual approach. Furthermore, many studies have shown that modeling a group of pipes, or cohorts, which share similar characteristics improves the prediction of a distribution network’s deterioration. A more computational and data-driven solution seems to represent the best way to extract this valuable information efficiently. Artificial intelligence and unsupervised learning algorithms possess the advantage to identify the pipe cohorts that are most at risk of failure and the conditions under which network failures occur from historical data. Once these vulnerable groups are identified, it allows utility managers (1) to have a better understanding of the network’s degradation over time, (2) to tailor inspection plans and replacement programs, and (3) to optimize water main investments in order to provide an improved level of service. The Region of Peel (Canada) has made investments in the past to collect good quality data for water main breaks and associated factors. And as such, the Region of Peel is faced with the increasing challenge of water main breaks and the resulting disruption to Peel residents and businesses and, in an attempt to meet council-approved service levels, the Region intends to use innovative methods to plan and optimize strategic investment in the water distribution network. Staff plans to use this predictive modeling information to plan water main inspection and replacement programs and optimize investments in the water main replacement program.