Air Quality Prediction In Metropolitan Areas Using Machine Learning

This paper presented an ensemble machine learning-based approach for air quality prediction using Gradient Boosting, AdaBoost, XGBoost, Voting, and Stacking classifiers. The proposed methodology effectively captures nonlinear relationships between air pollutants and meteorological parameters. The experimental analysis demonstrates that ensemble learning techniques significantly improve predictive accuracy compared to traditional single classifiers.   The results confirm that combining multiple classifiers enhances robustness and stability in air quality prediction. The proposed framework can be effectively utilized for environmental monitoring and early pollution warning systems in metropolitan areas. By providing accurate and timely air quality predictions, the system contributes to improved public health protection and environmental management.

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