Ai-Based River Chemical Pollution Monitoring And Cleaning: A Review Of Sensor Networks, Water Quality Prediction And Automated Treatment

Industrial effluents, agricultural runoff, sewage and hazardous chemicals contaminate rivers, threatening human health, aquatic ecosystems and the communities that depend on river water. Conventional water-quality monitoring relies on periodic manual sampling and laboratory analysis, which is slow, expensive and cannot provide continuous real-time information. This paper reviews an AI-based approach to river chemical pollution monitoring and cleaning. It covers wireless sensor networks that measure pH, temperature, turbidity, dissolved oxygen, conductivity and specific chemical concentrations; machine learning and deep learning models that classify pollution levels, compute water quality indices, predict future contamination and detect anomalous discharge events; and robotic and automated treatment technologies, including autonomous surface vehicles and chemical treatment processes, that respond to detected pollution. Water quality indices and their limitations are discussed. A five-layer architecture comprising in-river sensing, communication, AI analytics, a treatment decision and actuation layer, and an authority dashboard is proposed, together with research gaps, objectives, a comparison of prediction models and challenges such as sensor fouling, calibration, power supply, ecological side effects of treatment and enforcement. The review concludes that AI-driven continuous monitoring can detect and respond to river pollution far earlier than manual methods.