Title: Integrating Artificial Intelligence and IoT for Real-Time Water Quality Prediction and Early Pollution Warning
Authors: Ho Hien Vinh, Le Khanh Hai Dang
Volume: 9
Issue: 9
Pages: 271-280
Publication Date: 2025/09/28
Abstract:
This paper examines the development of an innovative Artificial Intelligence (AI) and Internet of Things (IoT) solution for monitoring real-time river pollution. As the world's water bodies become more polluted, the gravity of the situation deepens. The framework employs IoT devices that track specific water quality parameters (pH, temperature, turbidity, dissolved oxygen, total dissolved solids, and more) and relays the data to a processing unit. Within the processing unit, AI prediction models are formed that perform anomaly detection, pollution prediction, and classification for alerts. After training on historical data, the machine learning model can predict pollution instances much more rapidly than the thresholds. Preliminary findings suggest that the model's predictive capabilities surpass a 90% accuracy level and can thus be utilized to visualize reports. The data can be transformed into actionable insight for the web and mobile dashboards. In addition, the model can also be deployed to set off alarms for the concerned authorities. The adaptable model can also be applied to several other sites, including but not limited to, industrial zones and residential sites adjoining the river, thus aiding in the development of IoT driven environmental monitoring and achieving sustainable development goals for the emerging economies like Vietnam.