Modelling the impact of water pressure on water main breaks using a spatially explicit Bayesian model

Providing safe drinking water to the population is one of the main roles of municipalities. However, due to aging urban infrastructure, the number of pipe break is an increasingly around the world which is costly at the economic, social and environmental levels. Municipalities need to have tools that allow predicting these breaks so they can develop a preventive strategy to repair or replace vulnerable pipes before they break. This project proposes to use modern statistical tools to create a model that will use water pressure and pipes characteristics to predict pipe breaks. To support the model development, five Canadian cities will provide extensive pipe break datasets to train and test the model. This model will be integrated in InteliPipes, a break prediction tool developed by CANN Forecast. TO BE CONT’D

Faculty Supervisor:

Alexandra Schmidt;Sophie Duchesne

Student:

Renato Henriques da Silva

Partner:

CANN Forecast

Discipline:

Epidemiology / Public health and policy

Sector:

Real estate and rental and leasing

University:

Program: