A Comparative Study of Machine Learning Algorithms for Nationwide Air Quality Index Prediction in Bangladesh

The contribution of this paper to the research is that it provides a nationwide and multiregion framework for AQI forecasting in Bangladesh, filling in a critical gap within the literature. In particular:

1. Geographic scale – While previous research done in Bangladesh only used a few cities or regions for AQI prediction, this study has utilized a dataset consisting of 103 cities/regions for approximately 25 years (2000-2025).
2. Chronological split for time-series validity – This framework has utilized a chronological and region-wise train-test split and region-wise lag feature engineering as opposed to randomized split and non-region-wise lag feature engineering, providing a better simulation of forecasting.
3. Comparative evaluation between models – This framework sets a benchmark for evaluating the models in a consistent environment using the same evaluation metrics (RMSE, MAE, MAPE, and R²) at a national level and at a regional level explicitly.
4. Flexible framework – It provides foundations for future implementation of XGBoost, LightGBM, GRU, Transformer, graph models, and even IoT-based real-time implementation.

Overall, the paper specifically says that the experimental results of the study are illustrative, not measurements obtained during the model training process. Therefore, the actual contribution of this work is the framework and methodology themselves.