An Overview of Machine Learning Techniques Used in Real-Time Water Quality Index Measurement

Abstract

Water quality index (WQI) has been a universally recognized metric that quantifies water quality, calculated traditionally through analysis of physicochemical characteristics as well as using empirical methods. These conventional approaches can be time-intensive, costly, and may not effectively capture complex relationships among variables. The implementation of machine learning (ML) techniques for more efficient and accurate WQI measurement is the main focus of this paper. Models like Random Forest, Gradient Boosting and so on, required historical data in order to predict WQI dynamically, introducing automation and improving response times. Unique innovations, such as ecological data and hybrid systems, that put together ML and rule-based systems, would improve the accuracy and future development. Results gathered from various experimental data, have demonstrated that ML-based prototypes outperform traditional methods in predictive adaptability, laying foundations for real-time water monitoring solutions.