Predictive Rehabilitation of Clean Water Customer Connections Leveraging Machine Learning Algorithms and Failure Time Series Data

Failures in clean water service lines can disrupt supply, increase operational costs, and reduce customer satisfaction. This study develops a machine learning framework to predict such failures, providing a proactive tool for utility asset management. A case study was conducted on a water distribution network in Tehran, serving approximately 205,000 customers, with 11 years of service line data and over 88,000 recorded failures. Service line attributes, including length, diameter, material, age, demand, and pressure, were combined with historical failure data to train Random Forest, Extreme Gradient Boosting, and Long Short-Term Memory models. Model performance was assessed using F1-score, AUC-ROC, and AUC-PRC. A novel metric was introduced to quantify failure reduction when prioritising replacements. The results demonstrate that machine learning can effectively capture complex relationships between service line features and failures, offering significant benefits for tactical maintenance planning. This research underscores the potential of predictive approaches to improve reliability and reduce costs.