A Review of the Application of Machine Learning for Pipeline Integrity Predictive Analysis in Water Distribution Networks

Water Distribution Networks (WDNs), as critical urban infrastructures, face heightened vulnerability to damage and failure due to aging systems and external factors such as environmental changes, operational demands, and urban development pressures. Accurate predictive integrity assessment for pipeline systems is crucial for implementing proactive maintenance strategies that prevent catastrophic failures and ensure service reliability. In recent decades, the application of Machine Learning (ML) has emerged as a promising technique for processing and extracting complex interactions between influencing factors and failure trends within WDN systems. This article systematically reviews application scenarios, critical factors influencing WDN integrity, and the modeling and analysis of ML-based predictive models for WDNs. The review analyzes pertinent literature from the past two decades, up to 2024, using the PRISMA procedure and the snowballing method. The findings highlight the superior capabilities of specific ML models, such as tree-based algorithms, artificial neural networks, support vector machines, and other recent deep learning methods in predicting network failures and enhancing system health diagnostics. In addition, key challenges identified include: (i) insufficient standardization in variable selection, model selection and evaluation; (ii) limited data availability due to inconsistent historical failure records; (iii) a lack of systematic feature engineering pipelines for data preprocessing; and (iv) constraints in real-world generalization across finer temporal scales and different geographical regions. Furthermore, the main future research recommendations include developing a standardized framework for variable selection and model architectures, improving multi-source data fusion and collection techniques, enhancing feature engineering methodologies, and conducting systematic evaluations across diverse operational environments.