Smart Water Management System Using Edge Computing And Machine Learning For Real-Time Pollution Detection In Urban Waters

Abstract

This study proposes a Smart Water Management System grounded in edge computing and machine learning to advance real-time pollution detection in urban aquatic environments. Rapid urbanization has intensified pollutant inflows—ranging from nutrients and heavy metals to organic waste necessitating an adaptive, high-resolution monitoring architecture capable of immediate response. The system integrates distributed edge-based sensor nodes with lightweight analytical models, enabling on-site data processing, reduced latency, and enhanced situational awareness. A hybrid machine learning framework is employed, combining anomaly detection, regression-based water-quality forecasting, and classification of pollutant signatures to strengthen diagnostic accuracy. Field deployment across multiple urban water sites demonstrates that the proposed system reduces data-transmission demands by over 60%, increases detection sensitivity—particularly for turbidity, dissolved oxygen fluctuations, and contaminant spikes—and supports near real-time environmental decision-making. The study further highlights scalability, resilience to intermittent connectivity, and compatibility with existing municipal water-management infrastructures. Overall, the findings underscore that the integration of edge intelligence with adaptive learning algorithms significantly improves pollution monitoring performance, offering a transformative pathway toward smarter, more sustainable urban water governance. This framework provides a replicable model for policymakers, environmental engineers, and urban planners seeking advanced, responsive water-quality protection strategies.