Symbolic Artificial Intelligence for Ground-Level Ozone Prediction Through Association Rule Mining

Accurate air quality prediction is essential for environmental monitoring and public health protection. Among atmospheric pollutants, ground-level ozone remains particularly difficult to predict because of the complex and nonlinear interactions governing its formation. Although recent advances have achieved promising predictive performance using machine learning and deep learning, most existing approaches rely on black-box models whose explanations are provided only through post hoc explainability techniques. This work presents an alternative symbolic artificial intelligence framework based on association rule mining for intrinsically explainable ozone prediction. Hourly atmospheric observations collected from ground-level monitoring stations in the Hauts-de-France region (France) are preprocessed through cleaning, discretization, and class balancing before rule extraction. Two complementary symbolic AI approaches, Formal Concept Analysis (FCA) and a Genetic Algorithm (GA), are employed to automatically discover human-readable association rules linking meteorological and atmospheric variables to ozone concentration classes. The extracted rules provide transparent and directly interpretable decision mechanisms that can be readily validated by air quality experts. The experimental results show that both rule-mining approaches produce substantially more precise rule sets than decision trees, with average rule precisions of 0.76 for FCA and 0.79 for GA. Furthermore, the resulting rule-based classifier achieves an accuracy of approximately 0.79, outperforming the evaluated machine learning baselines while preserving intrinsic interpretability. These results demonstrate that symbolic AI constitutes a promising alternative for trustworthy air quality prediction and knowledge discovery.