Deep Learning-Based Real-Time Water Quality Forecasting And Disaster Reporting System For The Ganga River Basin

The Ganga River is an essential source of water and livelihood for millions of people across India, yet it continues to face serious pollution problems that threaten both environmental sustainability and public health. To help address this issue, this study presents an AI-enabled decision support system designed to monitor and predict water quality conditions along the Ganga River in real time. The system uses an Exogenous Long Short-Term Memory (LSTM) model to predict key water quality parameters such as pH, dissolved oxygen, and biochemical oxygen demand, based on historical records and real-time data collected from monitoring stations along the river. The model demonstrates a prediction accuracy of 96.86 percent and is able to forecast water quality trends up to five days in advance, while effectively capturing seasonal variations, pollution levels, and the influence of external factors such as industrial discharge. Alongside the prediction model, the system includes a user-driven disaster reporting dashboard where pollution incidents, floods, and industrial spills can be reported for specific locations. These reports are analyzed and visualized as location-wise disaster hotspots, helping identify areas that require immediate attention. By combining predictive analysis with real-time reporting and alerts, the proposed system supports timely decision-making, strengthens pollution control efforts, and improves preparedness for environmental risks. The framework can also be adapted for other rivers facing similar challenges, contributing to more sustainable water resource management and better protection of public health.