Machine Learning Algorithm for Water Quality Classification: A Systematic Literature Review

Abstract

Assessing Water quality classification has become an important research area as the demand for clean and safe water continues to grow worldwide. In recent years, Machine Learning (ML) has shown great potential in improving how water quality is monitored and analyzed. By using ML models, researchers can process large and complex environmental data more effectively to detect pollution, predict water conditions, and support better management decisions. While many studies have focused on using sensors and data analytics for monitoring, only a few have provided a full review of the different ML methods and their effectiveness in classifying water quality. Therefore, this paper aims to achieve two main goals: (1) to conduct a Systematic Literature Review (SLR) of existing ML techniques applied in water quality classification, and (2) to identify the main findings, challenges, and future opportunities in this field. Through a careful review and comparison of previous research, this paper hopes to give a clearer overview of how ML contributes to water quality analysis and guide future work in creating more accurate and intelligent systems for real-world environmental applications.