AI Based Methods for Water Treatment, Conservation and Management

This research investigates the possibility of exploiting artificial intelligence (AI) to improve water treatment, conservation, and management, tackling key challenges in the water field. Interdisciplinary evaluation of AIbased methods like machine learning algorithms, data analytics, and optimization models is performed to enhance water quality, save resources, and maximize water distribution. Information was gathered from literature reviews, case studies, and sensor data from IoT-based sensors, giving a rich basis for the development of AI systems. Main findings are presented to show that AI models like neural networks, support vector machines and random forest have high prediction accuracy in water treatment and neural networks are the best according to prediction indicators. Better treatment efficiency and energy conservation were presented by optimization techniques such as genetic algorithm and particle swarm optimization. Also, Using AI predictive models for forecast of the water demand, it was demonstrated that the water demand prediction is highly accurate which allows for more efficient planning of the water distribution network. It also points out that AI solutions are cost savings, eco friendly, scalable, flexible solutions for rural as well as urban areas.