Enhancing Water Potability Identification through Random Forest Regression and Genetic Algorithm Optimization Water Potability Identification through Random Forest Regression and Genetic Algorithm Optimization

Water quality is important for both environmental sustainability and public health. This research introduces an innovative method for forecasting water quality using Random Forest Regression, optimized through Genetic Algorithm (GA) techniques. The goal is to enhance prediction accuracy and offer meaningful insights for better water resource management. The study employed the “Water Quality Data” dataset, encompassing 11 essential water quality parameters from different locations. After thorough data preprocessing, the Random Forest model, refined with GA optimization, achieved a Mean Squared Error (MSE) of 0.3476 and an accuracy rate of 91.77%, surpassing conventional methods. This approach highlights the effectiveness of merging machine learning algorithms with evolutionary optimization techniques to achieve superior predictive outcomes. Although the dataset was of moderate size, the results show considerable improvements in model accuracy. This work advances the field of water quality prediction by leveraging sophisticated algorithms and emphasizes the significance of hyperparameter tuning. Future research should focus on using larger datasets and examining the specific regions from which the data is collected.