Air Quality Prediction Model for Monitoring AQI

Air pollution causes major concerns to human health and the environment. Accurately estimating the Air Quality Index (AQI) is vital for proactive policymaking, health warnings, and urban planning. This research proposes a complete framework that incorporates deep learning models—specifically Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) networks—with ensemble learning methods, particularly Extreme Gradient Boosting (XGBoost), to predict AQI. Our technique harnesses the capabilities of deep neural networks to capture complex nonlinear correlations from high-dimensional environmental data, while ensemble approaches enhance predictions by effectively handling feature interactions and missing data. Extensive data collection from urban monitoring stations, rigorous preprocessing, and detailed model design are carried out. Experimental findings suggest that the hybrid model outperforms standalone deep learning or ensemble methods, resulting in higher R² scores and lower error metrics. The study also discusses real-time applications, integration with additional data sources, and future research directions in AQI forecasting, aiming to support dynamic and accurate environmental monitoring systems.