Ai-Based River Chemical Pollution Monitoring And Cleaning: A Review Of Sensor Networks, Water Quality Prediction And Automated Treatment
Year: 2026
Type: article
Abstract: 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.
Language: English
Subfield: Water Science and Technology
Field: Environmental Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225746992
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