Advancing Water Quality Prediction: Integrating Ensemble Learning and Explainable AI for Actionable Insights

Water quality is fundamental for public health and also for environmental sustainability, thus imperative for maintaining a corporate monitoring system to deter contamination risks. In order to lessen environmental degradation and ensure safe drinking water for humans, it is necessary to assess the water quality at the right time. This study analyzes the capability of machine learning models in assessing water quality, concerning some ensemble methods like random forest and gradient boosting, which have already proven their edge over traditional classifiers, for instance, naive Bayes and SVM. The approach exploits different explainable AI (XAI) techniques in order to provide transparency around model decision-making and explain the importance of each feature. Through a thorough interrogation of feature importance, pH comes out on top, being the most significant factor in assessing water quality in general, thus highlighting its role in the overall assessment of water bodies. This study passes a note about the superior predictive strength of ensemble methods in achieving up to 100% accuracy and emphasizes the practical implications of treating pH as a highly determinative parameter in water quality management. By integrating XAI tools, this work champions broad support for the importance of the transparent, interpretable model for environmental monitoring, enhancing the basis for actionable insights to achieve more effective, prioritized interventions in water quality management.