AI-Driven Decision Support System for Real-Time Ganga River Water Quality Monitoring and Forecasting Using IoT Sensors

Abstract:
The Ganga River, a vital lifeline for millions in India, is facing severe pollution challenges, with significant implications for both environmental sustainability and public health. In response, this study presents an AI-enabled Decision Support System (DSS) that employs Long Short-Term Memory (LSTM) networks to predict water quality in real time. The model forecasts essential water quality parameters—pH, Dissolved Oxygen (DO), and Biochemical Oxygen Demand (BOD)—based on historical and real-time data from monitoring stations along the river. Achieving a remarkable accuracy of 96.86%, the model is capable of predicting water quality trends up to five days in advance, accounting for seasonal variations, pollution levels, and industrial discharges. An integrated alert mechanism warns stakeholders of potential deteriorations, enabling prompt intervention and pollution control measures. This system not only supports environmental compliance efforts for the Ganga River but also offers a framework adaptable to other rivers facing similar challenges. The proposed approach contributes significantly to preserving water quality and safeguarding public health.