Loading...
Tamilarasi R is an Assistant Professor in the Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning) at Velalar College of Engineering and Technology, Erode, Tamil Nadu, India. Her research interests include Artificial Intelligence, Machine Learning, Internet of Things, intelligent data analytics, and smart environmental systems. She actively contributes to teaching and research in AI-driven solutions for real-world challenges.
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.