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

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

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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.

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.

Alert System, Forecasting, Ganga River, LSTM, Pollution

Alert System, Forecasting, Ganga River, LSTM, Pollution

PILLAI, N.; SHARMA, A.; GUPTA, A.; GUPTA, A.; SIKORA, P.; ŘÍHA, K.; DUTTA, M.

2026

26.11.2024

978-3-8007-6544-7

ICUMT 2024; 16th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops

2157-023X

International Congress on Ultra Modern Telecommunications and Workshops

Spojené státy americké

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https://ieeexplore.ieee.org/document/11048818/