Intelligent Water Quality Assessment: Predictive Modeling for Potable Water Using Advanced Machine Learning Techniques

Abstract

Access to safe drinking water remains a global priority, necessitating robust and accurate methods for potability assessment. This research implements advanced machine learning approaches to develop a predictive framework for water quality classification. The study utilizes a comprehensive dataset containing critical physicochemical parameters including pH levels, sulfate concentration, and trihalomethane content. The methodology encompasses extensive data preparation protocols such as missing value imputation, outlier identification, and feature normalization. Multiple classification algorithms were evaluated to create an optimal prediction model, with performance assessed through rigorous statistical analysis. The developed system was integrated into an interactive web interface using Flask architecture, enabling real-time water quality evaluation based on user-provided parameters. Results demonstrate the system's effectiveness in distinguishing between potable and non-potable water sources, offering significant implications for public health monitoring and environmental management practices.