Using Machine Learning to Optimize Water Treatment and Desalination for Sustainability

Abstract

Water scarcity is a critical global challenge, necessitating the development of intelligent water treatment and desalination techniques. This paper investigates the role of machine learning in optimizing desalination processes, improving water purification, and reducing energy consumption in water treatment plants. AI models such as neural networks, anomaly detection, and reinforcement learning are applied to monitor water quality, predict contaminant levels, and optimize filtration systems. The study highlights real-world implementations of AI-driven water management, showcasing how ML enhances sustainability in clean water accessibility and conservation efforts.