Prediction of DMAs Pipe Failures Rehabilitation Priorities

Water Distribution System (WDS) pipe failures are one of the most critical issues in WDS management. In order to identify them, a machine learning approach was applied to eight years of geolocated data on pipe failures to establish priorities for WDS rehabilitation. District-level characteristics, such as network length, pressures, materials, population density, and temperature, were combined with a specific failure rate to account for differences in network size. A cost-sensitive classification approach minimized false negatives, ensuring that high-risk areas were correctly flagged. Among all models analyzed the best performance was achieved by Naive Bayes, which reliably predicted priority districts for proactive maintenance, supporting pipeline renewal strategies