Enhancing Air Quality Index Classification Based on Ensemble Machine Learning Techniques

Ahmed Fahim; Ahmed M. Osman; Zahraa Tarek; Ahmed M. Elshewey

doi:10.48084/etasr.13875

Authors

Ahmed Fahim Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia | Department of Computer Science, Faculty of Computers and Information, Suez University, Suez, Egypt
Ahmed M. Osman Department of Information Systems, Faculty of Computers and Information, Suez University, Suez, Egypt https://orcid.org/0009-0002-0527-533X
Zahraa Tarek Department of Computer Engineering and Information, College of Engineering, Wadi Ad Dwaser, Prince Sattam Bin Abdulaziz University, Al-Kharj 16273, Saudi Arabia | Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura, 35561, Egypt
Ahmed M. Elshewey Department of Computer Science, Faculty of Computers and Information, Suez University, P.O.Box:43221, Suez, Egypt | Applied Science Research Center. Applied Science Private University, Amman, Jordan https://orcid.org/0000-0002-3048-1920

Volume: 15 | Issue: 6 | Pages: 29325-29333 | December 2025 | https://doi.org/10.48084/etasr.13875

Received: 5 August 2025 | Revised: 8 September 2025 | Accepted: 15 September 2025 | Online: 8 December 2025

Corresponding author: Ahmed Fahim

Abstract

The accurate classification of Air Quality Index (AQI) is critical for environmental monitoring and public health protection. In this paper, we utilized a publicly available daily air quality dataset from U.S. counties, comprising six classification categories: Good, Moderate, Unhealthy for Sensitive Groups, Unhealthy, Very Unhealthy, and Hazardous. The dataset underwent preprocessing through missing value imputation and class balancing using the Synthetic Minority Over-sampling Technique (SMOTE). Several machine learning and deep learning models were trained and evaluated on the dataset, including Random Forest (RF), Extra Trees (ET), K-Nearest Neighbors (KNN), Naive Bayes (NB), Logistic Regression (LR), and a Multi-Layer Perceptron (MLP) neural network. The models were assessed using cross-validation accuracy, test set accuracy, macro-averaged recall, F1-Score, and ROC-AUC metrics. Ensemble methods (RRF and ET) and the MLP classifier achieved superior results compared to traditional models. The RF model achieved a test accuracy of 99.3%, while the MLP classifier achieved 99.0% . The stacking ensemble model achieved a test accuracy of 99.99 %, a macro-averaged recall of 87.12 %, and an ROC-AUC of 1.0000, highlighting the strong potential of ensemble learning techniques in enhancing the performance of AQI multi-class classification.

Keywords:

air pollution, Air Quality Index (AQI), environmental monitoring, machine learning, air quality classification, ensemble machine learning

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