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APPLICATION OF MACHINE LEARNING MODELS FOR AIR QUALITY INDEX PREDICTION

AUTHORS:
Sneha P. Kulkarni
Karthik N. Rao
Mentor
Prof. Meera V. Nair
Affiliation
Department of Biotechnology,
Horizon Institute of Scientific Studies, India
DOI: 10.55041/ijsmt.v1i2.004
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract
Air pollution is a mounting global environmental challenge with profound health and economic impacts. The Air Quality Index (AQI) serves as a standardized indicator of air pollution severity and is widely used by policymakers, researchers, and the public to understand environmental health risks. Traditional AQI forecasting techniques based on statistical regression and time-series models often struggle with nonlinear dependencies among multiple pollutant sources and meteorological variables. Machine Learning (ML) models offer significant promise for accurately capturing complex patterns in air quality data, enabling robust AQI predictions. This article explores the application of multiple machine learning and deep learning models—including Random Forest, Gradient Boosting (e.g., XGBoost), Support Vector Machines (SVM), Neural Networks, and temporal models such as LSTM—to AQI prediction. Using data from public air quality repositories, we evaluate model performance metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R². Our findings indicate that ensemble and deep learning models generally outperform shallow models, especially when data preprocessing, feature engineering, and hyperparameter tuning are properly implemented. We also discuss challenges, potential applications, and future research directions in deploying ML-driven AQI prediction tools for real-time and policy-relevant environmental intelligence
Keywords
Air Quality Index (AQI) Machine Learning Deep Learning Random Forest Boost LSTM Environmental Prediction Time Series Forecasting Feature Engineering
Article Information
PUBLICATION DETAILS
Review Type:
0
License:
CC BY-NC 4.0
Volume/Issue:
Volume Volume 01, Issue 02
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Nov 22 2025
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Kulkarni, S. P. & Rao, K. N. (2025). Application of Machine Learning Models for Air Quality Index Prediction. International Journal of Science, Strategic Management and Technology, Volume 01(02), 1-9. https://doi.org/10.55041/ijsmt.v1i2.004

Kulkarni, Sneha, and Karthik Rao. "Application of Machine Learning Models for Air Quality Index Prediction." International Journal of Science, Strategic Management and Technology, vol. Volume 01, no. 02, 2025, pp. 1-9. doi:https://doi.org/10.55041/ijsmt.v1i2.004.

Kulkarni, Sneha, and Karthik Rao. "Application of Machine Learning Models for Air Quality Index Prediction." International Journal of Science, Strategic Management and Technology Volume 01, no. 02 (2025): 1-9. https://doi.org/https://doi.org/10.55041/ijsmt.v1i2.004.

References

1.   Anugrah Ade Purnama, O. Forecasting Air Quality Dynamics: Employing Machine Learning Models for Enhanced Environmental Health Predictions. International Journal Of Mathematics And Computer Research, 2025.


2.   Bansal, S.K., Avula, S.R., Mehrotra, M.A., et al. Machine learning algorithms for predicting air quality index: A case study in urban and industrial zones. Periodicals of Engineering and Natural Sciences, 2025.


3.   Optimized machine learning model for air quality index prediction in major cities in India. Scientific Reports, 2024.


4.   Air Quality Index Prediction Using Machine Learning Techniques. IJRASET Journal, 2025.


5.   Air Quality Index Prediction using Machine Learning | IJACTE Journal.


6.   Jin, N., Zeng, Y., Yan, K., & Ji, Z. (2021). Multivariate Air Quality Forecasting With Nested Long Short Term Memory Neural Network. IEEE Transactions on Industrial Informatics, 17(12), 8514–8522. https://doi.org/10.1109/tii.2021.3065425


7.   Alwabli, A. (2024). Federated Learning for Privacy-Preserving Air Quality Forecasting using IoT Sensors. Engineering, Technology & Applied Science Research, 14(4), 16069–16076. https://doi.org/10.48084/etasr.7820


8.   Wu, H., Yang, T., Li, H., & Zhou, Z. (2023). Air quality prediction model based on mRMR–RF feature selection and ISSA–LSTM. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-39838-4


9.   Mei, S., Li, H., Fan, J., Zhu, X., & Dyer, C. R. (2014, August 1). Inferring air pollution by sniffing social media. https://doi.org/10.1109/asonam.2014.6921638


10.            Ayus, I., Natarajan, N., & Gupta, D. (2023). Comparison of machine learning and deep learning techniques for the prediction of air pollution: a case study from China. Asian Journal of Atmospheric Environment, 17(1). https://doi.org/10.1007/s44273-023-00005-w

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This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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