Prediction of pipe failures in water supply networks for longer time periods through multi-label classification

The unexpected failure of pipes is a problem that is hitting the water networks of many cities around the world. Nowadays, many proposals based on the use of machine learning techniques are emerging to combat this problem. However, most studies focus their efforts on predicting failures in short time periods, usually a year, while longer time period predictions would be more valuable to address strategic decisions.In this study, the use of multi-label classification techniques is proposed to simultaneously predict pipe failures in water supply systems for multiple years. For this purpose, three models (discriminant analysis, logistic regression and random forest) and different prediction time periods (one, two and three years) have been analysed. As multi-label data require specific quality metrics and sampling techniques, part of this work is dedicated to their exploration and discussion.The models are evaluated on a real-world seven-year database, achieving successful results. An insightful analysis of the use of the methodology shows how the percentage of avoided pipe failures increases over time. In fact, it is demonstrated that 30.2%, 51.4% and 54.0% of the pipe failures of three consecutive years are avoided according to data from a real network.