Urban water distribution systems are essential for supplying clean and safe water to residential, commercial, and industrial sectors. However, aging infrastructure, pipe corrosion, pressure fluctuations, and environmental factors often lead to pipeline leaks that result in significant water loss, economic damage, and potential public health risks. Early detection of such leaks is therefore critical for efficient water management and infrastructure maintenance. This paper presents an intelligent system for early leak prediction and risk localization in urban water pipelines using advanced data analysis and sensor-based monitoring techniques. The proposed system collects real-time data from pressure sensors, flow meters, and acoustic sensors installed throughout the pipeline network. Machine learning algorithms analyze the collected data to identify abnormal patterns that indicate potential leakage. The system also employs predictive models to estimate the likelihood of future leaks and determine high-risk pipeline segments. By integrating data analytics, sensor networks, and intelligent decision-making methods, the proposed approach enables early warning and accurate localization of leaks. This helps water management authorities reduce water loss, improve maintenance planning, and enhance the reliability and sustainability of urban water distribution systems.
Madhumitha et al. (Thu,) studied this question.