Machine learning for predicting leakage occurrence based on water pressure observation

Abstract

Currently, more than 20,000 water leakage and breakage incidents occur annually in Japan’s water supply system. Leaks in water pipes can be roughly classified into two types: aboveground leaks that flow out above the ground and underground leaks that do not flow out above the ground but flow underground. While aboveground leaks are easy to detect because they are visible, underground leaks cannot be directly confirmed visually. Therefore, development for early detection is required. In this study, the authors tried to detect leakage location based on water pressure observation, assuming the monitoring of pipelines using smart meters, which are currently in widespread use. Six models with different explanatory variables and machine learning methods were constructed, and their prediction accuracy was compared. The leakage prediction model based on LightGBM, which uses the rate of water pressure change, amount of water pressure change, and pipe type information as explanatory variables, showed the best results.