Water Quality Prediction Using Explainable Machine Learning Models

Water pollution is a common global challenge, with significant impacts for ecosystem preservation, human health, and sustainable development. Addressing this issue requires a comprehensive understanding of the complex relationships between environmental factors and water quality outcomes. This study investigates the application of machine learning models to enhance water quality prediction and environmental management. The study utilizes robust machine learning models, including Adaboost, Random Forest, and Decision Tree classifiers, to uncover patterns within multidimensional water quality datasets. The Extra Trees classifier combined with the SMOTE-ENN resampling strategy achieved an accuracy of 87.25%, a recall of 95.26%, and an ROC of 94.52%. The Explainable AI (XAI) method is used to determine the impact of each parameter to the predictions of the model. This study identified parameters such as turbidity, solids, sulfate, hardness, and pH as some of the most influential factors in determining water potability. The identified factors provide valuable insights that can inform policy decisions and targeted interventions to reduce water pollution. The use of machine learning techniques provides a strong foundation for enhancing water quality evaluation and prediction, therefore facilitating sustainable water resource management.