Spatiotemporal clustering and multivariate forecasting of air quality index across Indian cities using machine learning and deep learning models

Air pollution is still a significant environmental and public health issue in India, with heterogeneous urban systems showing unique Air Quality Index (AQI) patterns. This paper suggests an integrated approach of combining spatiotemporal clustering and multivariate forecasting to improve AQI prediction in ten large Indian cities. Applying K-Means clustering on AQI, temperature, and wind speed metrics, cities were classified into three replicable regional groups: northern industrial, coastal/eastern, and southern plateau regions. In every cluster, machine learning (Random Forest, XGBoost) and deep learning (LSTM, GRU, and a hybrid LSTM+GRU ensemble) models were learned in a rolling-origin cross-validation framework to provide temporal consistency.The ensemble model performed the best consistently, with RMSE varying from 0.090 to 0.101 and R² varying from 0.71 to 0.76, overperforming both standard baselines and naïve seasonal predictors. Seasonal analysis indicated winter months in northern cities to be the most challenging to predict, and monsoon periods were found to have the highest predictability. An ablation study also measured the contribution of hybridization, clustering, and meteorological inputs quantitatively.The approach is scalable, interpretable, and deployable, enabling data-driven air quality management as well as actionable insights for environmental policy and urban planning.