Air quality index modeling using ensemble and data-intelligence modeling

Urban population growths, expansion of the industrial economy, burning stubble, and other major sources have significantly contributed to the increasing levels of air pollution in urban areas. Therefore, accurate prediction of the Air Quality Index (AQI) is essential for effective monitoring and planning within cities. In this research, an ensemble machine learning (ML) called Gradient Boosting (GB) model with 5- and 10-folds’ cross-validation was developed to achieve more precise and operative prediction outcomes based on important air quality related input variables. The results demonstrated that the suggested hybrid model attained the best accuracy in comparison with other benchmark ML models for predicting AQI values of Delhi city. Statistically, GB model reported second best model based on statistical metrics (R2 = 0.96, R = 0.98, RMSE = 23.03, MSE = 530.71, MAE = 15.16 and MARE = 11.96). Whereas Light Gradient Boosting Machine (LightGBM) model third best accuracy reported (R2 = 0.96, R = 0.98, RMSE = 23.21, MSE = 538.74, MAE = 14.68, and MARE = 11.33). The AQI prediction values are presented as spatial thematic maps for better understanding, highlighting city areas with high AQI levels to support air quality planning. The daily AQI prediction maps were prepared using various ML models. Further, the current research forecasted five days of AQI values and compared with the observed values. The results of five-day forecasting of GB model again indicated the best match with observed data of AQI values. Finally, forecasting accuracy results of GB (Boosting) models was attained 90% match with the observed values of AQI as compared with hybrid model. In conclusion, the reported results provided better understanding of AQI modeling for values and classes of spatial maps. © The Author(s), under exclusive licence to Springer Nature B.V. 2026.