A Systematic Literature Review on Flow Data-Based Techniques for Automated Leak Management in Water Distribution Systems

Abstract

Highlights: What are the main findings? IoT, smart metering, and AI-based models are increasingly used for real-time leak management, but their effectiveness relies on data quality and system integration. While significant research focuses on leak detection algorithms, fewer studies address the full scope of automated leak management systems, limiting progress beyond detection. What is the implication of the main finding? Developing an automated leak management system based on advanced data acquisition, robust leak management models, and scalable real-time monitoring platforms is crucial for enhancing leak detection accuracy and responsiveness. Further research is essential to improve model accuracy, scalability, and system integration, addressing key challenges for fully automated leak management deployment. Smart cities integrate advanced technologies, data-driven decision-making, and interconnected infrastructure to enhance urban living and resource efficiency. Among these, Smart Water Management (SWM) is crucial for optimizing water distribution and reducing Non-Revenue Water (NRW) losses, a persistent challenge for utilities worldwide. Water leaks contribute significantly to NRW, necessitating real-time leak detection and management systems to minimize detection time and human effort. Achieving this requires seamless integration of SWM technologies, including advanced metering infrastructure, the Internet of Things (IoT), and Artificial Intelligence (AI). While previous studies have explored various leak detection techniques, many lack a focused analysis of real-time data integration and automated alerts in SWM systems. This Systematic Literature Review (SLR) addresses this gap by examining advancements in automatic data collection, leak detection models, and real-time alert mechanisms. The findings highlight the growing potential of data-driven approaches to enhance leak detection accuracy and efficiency, particularly those leveraging flow and pressure data. Despite advancements, model accuracy, scalability, and real-world applicability remain. This review provides critical insights for future research, guiding the development of automated, AI-driven leak management systems to improve water distribution, minimize losses, and enhance sustainability in smart cities.