Explainable hybrid deep learning framework for air quality index prediction in Indian cities

Abstract Air pollution constitutes one of the most pressing environmental and public health crises of the twenty-first century, disproportionately affecting rapidly urbanising nations such as India. Accurate prediction of the Air Quality Index (AQI) is essential for enabling timely hospital preparedness, school-closure decisions, and evidence-based policy interventions. AQI prediction remains inherently challenging owing to widespread missing data due to sensor failures (up to 54% missingness in certain pollutant columns), pronounced non-stationarity, nonlinear inter-pollutant interactions, and the limited interpretability of existing predictive models. This paper presents a four-stage Explainable Hybrid Deep Learning Framework validated on 435,742 CPCB India records (1990–2015) to address these challenges. The XAI-Enhanced Ensemble achieves RMSE = 0.3817, MAE = 0.2773, and R2= 0.9994 on the held-out test set a 54.7% RMSE and 58.7% MAE reduction over the standalone LSTM baseline. SHAP-guided feature selection reduces input dimensionality by 62% (from 26 to 10 features) while yielding an additional 38% RMSE improvement over the standard ensemble, demonstrating that interpretability and accuracy are complementary rather than competing objectives.

# Computational linguistics. Natural language processing # Electronic computers. Computer science # AQI prediction # Explainable AI (XAI) # SHAP # LSTM # XGBoost # Ensemble learning

Priyal Singhal, Praneet Saurabh, Mayank Sharma, et al. (2026). "Explainable hybrid deep learning framework for air quality index prediction in Indian cities". Discover Artificial Intelligence. https://doi.org/10.1007/s44163-026-02131-0