The designing of a transparent hybrid machine learning framework for water leak detection: a systematic review

Abstract

Introduction: Global water scarcity is increasingly exacerbated by substantial water losses, with approximately 30% of treated water lost annually due to leaks in aging Water Distribution Networks (WDNs). Addressing this challenge requires advanced and reliable leak detection mechanisms. This study investigates the design of a transparent hybrid machine learning framework aimed at improving the accuracy and effectiveness of water leak detection systems. Methods: A systematic literature review was conducted following PRISMA guidelines. A total of 27 relevant studies were analyzed, focusing on hybrid deep learning approaches that incorporate data fusion, mixed models, and ensemble techniques for leak detection in WDNs. Results: The findings indicate that hybrid and ensemble learning techniques are becoming more important in the identification of water leaks. Several studies reported exceptional high performance, with some models achieving up to 99% balanced accuracy by leveraging multiple data modalities. These approaches demonstrate strong resilience and adaptability across varying operational conditions. Discussion: Despite their high performance, the complexity and "black-box" nature of hybrid models limit their practical deployment. The study highlights the importance of integrating Explainable Artificial Intelligence (XAI) techniques to enhance transparency, interpretability, and user trust. The review concludes that future intelligent leak management systems should combine high-performing hybrid models with XAI to develop efficient, interpretable, and trustworthy decision-support systems that support sustainable water resource management.