A Hybrid ANN and XGBoost Approach to Urban Air Quality Classification

Abstract

Public health is severely threatened by urban air pollution, particularly in densely populated and rapidly industrialising cities.  For risk management, environmental monitoring, and targeted policymaking, urban zones must be accurately classified according to pollution levels.  This paper proposes a classification framework that integrates the ensemble-based decision-making power of Gradient Boosting With the nonlinear feature extraction capabilities of neural networks, a publicly available dataset containing over 52,000 daily air quality records from six major cities was used. The model was designed to distinguish between industrial and residential urban areas based on six major pollutants: PM2.5, PM10, CO, NO₂, SO₂, and O₃. The proposed two-stage architecture first transforms input features through an ANN to capture complex pollutant interactions, then feeds the learned representations into an XGBoost classifier for final prediction. The performance of this hybrid model was compared to that of several well-known classifiers, including standalone ANN, standalone XGBoost, Support Vector Machine, and Logistic Regression.  With an accuracy of 99.98%, the suggested ANN–XGBoost model outperformed all baseline techniques.  At 99.92%, 99.96%, and 99.98%, respectively, precision, recall, and F1-score were likewise exceptionally high, demonstrating exceptional classification performance and generalization ability.