Prediction of pipe failures in water supply networks using logistic regression and support vector classification

Abstract

Companies in charge of water supply networks are making a huge effort to optimally plan the annual replacements of pipes. This would save costs, enable a higher quality of service and a sustainable management of infrastructure.

This study presents a methodology to predict pipe failures in water supply networks. Logistic regression and support vector classification are chosen as predictive systems. Both provide a failure probability associated with each sample which is increasingly required by companies that manage these infrastructures. Furthermore, several pre-processing techniques that seek to improve the accuracy of predictions are addressed.

The proposed methodology is illustrated with the real case of a Spanish city. This is an extensive water supply network whose recorded data contains 4,393 pipe failures. The results obtained state that the number of unexpected failures might be significantly reduced. Around 30% of failures could have been prevented by replacing only 3% of the network's pipes per year, which is a realistic and feasible option.

As a future line of research, the objective must be to develop a global tool that incorporates the failure probability and its consequence, generating the optimal pipe replacement plan.