Hybrid Horizons: Advancing Water Potability Prediction Through Hybrid Machine Learning

Ensuring access to safe drinking water is a critical global concern with significant implications for public health. This paper investigates the application of the hybrid machine learning model in assessing water potability, offering a comprehensive review of current methodologies and prospects. With water quality assessment a critical component of public health management, integrating machine learning techniques shows promising avenues for improving accuracy, efficiency, and predictive capabilities. This paper synthesizes existing literature on machine learning models in water quality analysis, highlighting various approaches, such as supervised and hybrid machine learning models utilized for water potability assessment. Furthermore, it examines using diverse data sources, including the pH level of the water, water hardness, total dissolved solids in the water, Chloramines concentration, sulfate concentration, electrical conductivity, organic carbon content, Trihalomethanes concentration, and turbidity level to enhance model performance and robustness. Our experiment results on the Water Quality and Potability dataset show that the proposed hybrid machine learning model achieved 68% classification accuracy compared to traditional supervised machine learning techniques. By critically evaluating the strengths and limitations of supervised and hybrid machine learning models, our research contributes to the ongoing discourse on leveraging technology to safeguard water quality and public health, ultimately fostering sustainable water management practices.