Comparative Analysis of Machine Learning Models in Smart Water Quality Monitoring

Managing water quality is very important for protecting public health and keeping aquatic ecosystems healthy. Monitoring water quality the old-fashioned way is often hard work and doesn't allow for real-time monitoring. This study introduces an intelligent water quality monitoring system utilizing several machine learning (ML) techniques to facilitate precise, real-time evaluation of water parameters. IoT-enabled sensors gather important information including pH, turbidity, dissolved oxygen, and temperature. We test a number of machine learning (ML) techniques to see how well they work for predicting accuracy and finding anomalies. These algorithms include Random Forest, Support Vector Machine, and Artificial Neural Networks. Experimental findings indicate that ML-driven methodologies substantially improve monitoring accuracy and enable prompt interventions. The study emphasizes the relative advantages of each method and their suitability for scalable, automated water quality control systems.