Machine Learning Driven Groundwater Quality Classification with Model Interpretability Using SHAP

The experimental evaluation of the suggested CatBoost-based groundwater quality classification system has proven discriminative power and positive generalization capacity. The model gets a score of 0.9730 accuracy, which implies that most samples of groundwater are classified correctly in either producing or not producing categories. This high accuracy is due to the model's very good ability to learn all the complicated nonlinear relationships among hydrochemical parameters and also to its performing well across the training and testing datasets. The F1-score of 0.9375 that was obtained additionally indicates a very good trade-off between precision and recall, which is a very crucial factor in groundwater quality assessment where the imbalance between classes is common and misclassification can come with serious environmental and agricultural risks. The analysis of the confusion matrix has reinforced these results by indicating that false negatives were very infrequently happening, thus making it less possible that unsatisfactory groundwater will be wrongly classified as good. This kind of dependability is very important for protecting the irrigation methods and for securing the health of the population. What is more, not only is the predictive performance powerful, but also the interpretability analysis via SHapley Additive exPlanations (SHAP) discloses that salinity-related parameters, sodium hazard indicators, groundwater level conditions, and dissolved constituents are the main drivers in deciding the groundwater suitability. The correlation of these significant features with established hydrogeochemical knowledge backs up the scientific reliability of the model. To sum up, the attained accuracy and F1-score along with the transparent interpretability confirm that the proposed system is very effective and also suitable for practical application in real-world groundwater quality management