E-ISSN 3026-930X
 
2025, Vol: 3, Issue: 1
3 / 1Current Issue ArchiveAims and ScopeAbstracting & Indexing
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Original Research 


Optimizing Air Quality Index Prediction: A Machine Learning Comparison of Standalone Models and Stacking Ensembles for Accuracy and Interpretability

Tahir Aja Zarma, Ibrahim Mukhtar, Ahmadu Adamu Galadima, Jafar Sanusi, Usman Mohammed.


Abstract
Air pollution remains a critical global challenge, necessitating accurate and interpretable predictive models for effective environmental monitoring. This study develops and evaluates machine learning models to predict the U.S. EPA Air Quality Index (AQI) using meteorological and pollutant data. We compare standalone algorithms—including XGBoost, TabNet, MLP, SVM, and Random Forest—and propose a stacking ensemble that integrates XGBoost, MLP, RF, and SVM via logistic regression meta-learning. Our results demonstrate that XGBoost achieves the highest individual performance (98.78% accuracy), while the stacking ensemble further improves predictive robustness (99.10% accuracy), particularly at AQI class boundaries. Feature importance analysis identifies PM2.5, PM10, and CO as the most influential predictors, with spatial visualization revealing urban-industrial hotspots. The framework balances accuracy, computational efficiency, and interpretability, recommending XGBoost for resource-constrained deployments and the ensemble for high-stakes applications. This work contributes a scalable solution for real-time air quality alerts and policy support, with implications for public health and environmental management.

Key words: Air Quality Index (AQI), machine learning, stacking ensemble, XGBoost, interpretability, environmental monitoring.


 
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How to Cite this Article
Pubmed Style

Zarma TA, Mukhtar I, Galadima AA, Sanusi J, Mohammed U. Optimizing Air Quality Index Prediction: A Machine Learning Comparison of Standalone Models and Stacking Ensembles for Accuracy and Interpretability. NJEAS. 2025; 3(1): 498-509. doi:10.5455/NJEAS.252828

Web Style

Zarma TA, Mukhtar I, Galadima AA, Sanusi J, Mohammed U. Optimizing Air Quality Index Prediction: A Machine Learning Comparison of Standalone Models and Stacking Ensembles for Accuracy and Interpretability. https://www.nilejeas.com/?mno=252828 [Access: June 22, 2026]. doi:10.5455/NJEAS.252828

AMA (American Medical Association) Style

Zarma TA, Mukhtar I, Galadima AA, Sanusi J, Mohammed U. Optimizing Air Quality Index Prediction: A Machine Learning Comparison of Standalone Models and Stacking Ensembles for Accuracy and Interpretability. NJEAS. 2025; 3(1): 498-509. doi:10.5455/NJEAS.252828


Vancouver/ICMJE Style

Zarma TA, Mukhtar I, Galadima AA, Sanusi J, Mohammed U. Optimizing Air Quality Index Prediction: A Machine Learning Comparison of Standalone Models and Stacking Ensembles for Accuracy and Interpretability. NJEAS. (2025), [cited June 22, 2026]; 3(1): 498-509. doi:10.5455/NJEAS.252828


Harvard Style

Zarma, T. A., Mukhtar, . I., Galadima, . A. A., Sanusi, . J. & Mohammed, . U. (2025) Optimizing Air Quality Index Prediction: A Machine Learning Comparison of Standalone Models and Stacking Ensembles for Accuracy and Interpretability. NJEAS, 3 (1), 498-509. doi:10.5455/NJEAS.252828


Turabian Style

Zarma, Tahir Aja, Ibrahim Mukhtar, Ahmadu Adamu Galadima, Jafar Sanusi, and Usman Mohammed. 2025. Optimizing Air Quality Index Prediction: A Machine Learning Comparison of Standalone Models and Stacking Ensembles for Accuracy and Interpretability. Nile Journal of Engineering and Applied Science, 3 (1), 498-509. doi:10.5455/NJEAS.252828


Chicago Style

Zarma, Tahir Aja, Ibrahim Mukhtar, Ahmadu Adamu Galadima, Jafar Sanusi, and Usman Mohammed. "Optimizing Air Quality Index Prediction: A Machine Learning Comparison of Standalone Models and Stacking Ensembles for Accuracy and Interpretability." Nile Journal of Engineering and Applied Science 3 (2025), 498-509. doi:10.5455/NJEAS.252828


MLA (The Modern Language Association) Style

Zarma, Tahir Aja, Ibrahim Mukhtar, Ahmadu Adamu Galadima, Jafar Sanusi, and Usman Mohammed. "Optimizing Air Quality Index Prediction: A Machine Learning Comparison of Standalone Models and Stacking Ensembles for Accuracy and Interpretability." Nile Journal of Engineering and Applied Science 3.1 (2025), 498-509. Print. doi:10.5455/NJEAS.252828


APA (American Psychological Association) Style

Zarma, T. A., Mukhtar, . I., Galadima, . A. A., Sanusi, . J. & Mohammed, . U. (2025) Optimizing Air Quality Index Prediction: A Machine Learning Comparison of Standalone Models and Stacking Ensembles for Accuracy and Interpretability. Nile Journal of Engineering and Applied Science, 3 (1), 498-509. doi:10.5455/NJEAS.252828


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1. Prof. Abdulhameed Danjuma Mambo
   Editor-in-Chief
abdulhameed.mambo@nileuniversity.edu.ng
2. Dr. Adekunle Adeleke
    Associate Editor-in-Chief
    adekunle.adeleke@nileuniversity.edu.ng
3. Dr. Ifeyinwa Obianyo
Managing Editor
ifeyinwa.obianyo@nileuniversity.edu.ng