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Dr. Pratima V. Damre is an Assistant Professor in the Department of Chemistry at Government College of Engineering, Maharashtra, India. Her academic interests include environmental chemistry, water quality assessment, Artificial Intelligence, IoT-based monitoring systems, and sustainable environmental technologies. She is actively engaged in interdisciplinary research focusing on intelligent water quality monitoring and environmental protection.
H2O is a fundamental natural resource vital for human well-being, agricultural practices, industrial activities, and the sustainability of ecosystems. Accelerated urban development, industrial growth, agricultural effluents, and climate change have markedly heightened water contamination, necessitating sophisticated water quality surveillance and forecasting systems. Traditional water quality evaluation techniques depend on intermittent laboratory analyses, which are frequently laborious, time-consuming, and unable to facilitate ongoing surveillance. The amalgamation of Internet of Things (IoT), Machine Learning (ML), Artificial Intelligence (AI), Cloud Computing, Edge Computing, and Big Data Analytics has transformed water resource management through real-time monitoring, astute forecasting, anomaly identification, and decision-making assistance. IoT sensors perpetually assess essential water quality metrics like pH, turbidity, dissolved oxygen, electrical conductivity, temperature, Total Dissolved Solids (TDS), and oxidation-reduction potential. Machine Learning algorithms evaluate past and current sensor data to forecast water quality, identify pollution, and facilitate sustainable water resource management. This chapter delineates the principles, framework, machine learning methodologies, applications, obstacles, and prospective research avenues pertaining to smart water quality forecasting utilising IoT and machine learning.