International Journal of Science, Engineering and TechnologyAuthors: Sarthak Sunil Khandagale, Professor Bangar Abhishek Popat, Professor S. B. Bhosale, Dr. A.A.Khatri
Abstract: Rivers receive chemical contamination from industrial effluent, domestic sewage and agricultural runoff, including nutrients such as phosphorus, organic load, and other pollutants. Conventional assessment relies on periodic sampling and laboratory analysis, which needs specialised staff, chemicals and expertise, and therefore cannot give continuous, early warning of pollution events. Artificial intelligence (AI), combined with low-cost sensing and the Internet of Things (IoT), is increasingly proposed for continuous river monitoring and for the data-driven control of chemical treatment such as coagulant dosing. This paper presents a focused narrative review of studies that use machine learning (ML) and deep learning (DL) with IoT sensing for river water quality monitoring and chemical-treatment control. It examines three connected themes: sensing technologies, learning methods, and IoT architectures. The reviewed literature reports water-quality classification accuracy of up to 98 % with an Arduino-based IoT device, XGBoost-based prediction in an industrially affected river basin, ML-assisted phosphorus sensing in river samples, and a deep-learning coagulant-dosing model that reduced dosage by about 22 % in a plant study. However, most studies are small, single-site, short-term and laboratory-oriented, and few report field validation, sensor fouling and drift, or closed-loop treatment control in rivers. The paper contributes a comparative table of representative studies, a conceptual river-to-decision architecture (not experimentally validated), and a prioritised list of research gaps.
