Deciphering seasonal dynamics and time series forecasting of urban air quality: a case study in Urban Cities of Andhra Pradesh

Proper air quality forecasting is essential in developing countries such as India, where climate variability, industrialization, and increasing urbanization play a major role in degrading air quality and posing health risks. This research introduces an integrated machine learning (ML) architecture for the prediction of the Air Quality Index (AQI) in two Andhra Pradesh urban cities, Visakhapatnam and Vijayawada, and one city in Telangana, Hyderabad based on five years of pollutant and meteorological data. This approach combines a deep feedforward neural network (FNN) with residual blocks and several traditional regression techniques, viz., Random Forest, Lasso, and Gradient Boosting, to both predict AQI directly and impute it through pollutant-wise modeling in accordance with CPCB standards. Imposing a large amount of feature engineering like temporal lags, rolling statistics, and pollutant interactions was used to identify spatiotemporal dynamics. The unified advanced FNN model attained [Formula: see text] values of 0.965, 0.97, and 0.96, and Root Mean Square Errors (RMSE) of 10.0, 11.25, and 13.17 for Vijayawada, Hyderabad, and Visakhapatnam respectively. Furthermore, predictions for pollutant-specific values in 2025 showed close conformity with real AQI values ([Formula: see text], RMSE = 2.68, 5.84, 5.30 for Random Forest for Vijayawada, Hyderabad, Visakhapatnam respectively) when predicted from estimated pollutant concentrations. This research illustrates a scalable method for AQI forecasting that is capable of informing real-time policy and public health intervention in data-scarce settings.