Reliable generative interpretable framework for efficient predictive analysis of air quality index

Abstract

Air quality management is one of the most important sustainability goals in the era of Industry 5.0. The magnitude of air pollution and impact of drastic pollutants increase day by day despite the significant efforts of the environmental enthusiasts and researchers. The role of Artificial Intelligence (AI) in determining the Air Quality Index (AQI) is significant with reasonable accuracy of classification achieved. The proposed model is a multi-class problem, that classifies the AQI into six different classes. Various ML models such as Decision Tree (DT), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Boosting(GB), Logistic Regression (LR). The RF provided reliable performance metrics for AQI category prediction, achieving an accuracy and Precision of 0.99. This model is selected for the implementation of Explainable AI (XAI) models such as Local Interpretable Model Agonistic Explainer (LIME) for explanation using the local surrogacy plots and SHapley Additive exPlanations (SHAP) explainer for the global surrogacy plots. The Generative Adversarial Network (GAN) can generate synthetic data, which addresses critical issues such as missing data, class imbalance, noise, and redundant data. The performance the GAN shows optimized performance in classification of the AQI data with accuracy closer to 100 %. This is mainly due to the synthetic data generated by the GAN which enhances the performance of the classification. The proposed work integrates the efforts of the GAN-AI-XAI that enhances the performance, reliability, trustworthiness and robustness of the AQI classification model.