Machine Learning and AI-Driven Water Quality Monitoring and Treatment

Abstract

Water quality monitoring and improvement has emerged as an important aspect of environmental management; this paper
investigates the most recent applications of ML and AI in this field. Recent advances in artificial intelligence (AI) and machine
learning (ML) algorithms have greatly improved the accuracy and efficiency of water quality monitoring systems; this
article takes a close look at these new approaches. This research looks at how these innovations may be used to improve water
treatment procedures. It focuses on how these approaches can detect and eliminate impurities more effectively than previous
methods. In this article, we take a look at a number of case studies that demonstrate how artificial intelligence (AI)-powered
devices have improved water quality assessment and treatment efficiency. In addition, this research delves into the many issues
plaguing this area and speculates on possible future advancements in AI and ML. The significance of multidisciplinary cooperation,
data security, and scalability are all touched on in these problems. In this study, we show how artificial intelligence and
machine learning can improve water quality management by analyzing their effects and showing how they can change the way
we do things for the better.