Integrating Q-Learning and Random Forest for Enhanced Water Potability Assessment

In this study, a comprehensive framework combining Pearson correlation analysis, Q-learning-based feature selection, dimensionality reduction techniques, and ensemble learning was developed to assess water potability. Initially, Pearson correlation coefficients were used to evaluate the linear relationship between individual water quality indicators and drinkability, laying the groundwork for feature relevance assessment. To further enhance model performance, a Q-learning algorithm was introduced to formulate feature se-lection as a Markov Decision Process, automatically identifying an optimal subset of predictors. Subsequently, PCA and t-SNE were employed to reduce dimensionality and visualize sample clustering patterns in low-dimensional space. Random Forest and Logistic Regression models were then constructed to predict potability, and their decision boundaries and predictive responses were interpreted through Partial Dependence Plots. The results show that Solids, Conductivity, and Trihalomethanes have strong impacts on water safety assessment, and the integration of reinforcement learning significantly improves feature screening efficiency and model robustness. This hybrid approach provides valuable insights and a scalable strategy for future water quality monitoring.