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<p>Accurate Air Quality Index (AQI) classification is essential for environmental surveillance and public health decision-making. Using a publicly available daily U.S. county-level dataset with six AQI categories (Good, Moderate, Unhealthy for Sensitive Groups, Unhealthy, Very Unhealthy, Hazardous), we conducted a comprehensive benchmarking study. Data preprocessing included missing-value imputation and class balancing via Synthetic Minority Over-sampling Technique (SMOTE). We trained and evaluated classical and deep models (Random Forest (RF), Extra Trees (ET), K-Nearest Neighbors (KNN), Naive Bayes (NB), Logistic Regression (LR), and a Multi-Layer Perceptron (MLP)) and assessed performance using cross-validation accuracy, test accuracy, macro-averaged recall, F1-score, and ROC-AUC. Ensemble methods (RF, ET) and the MLP consistently outperformed traditional baselines. RF achieved 99.3% test accuracy with perfect recall, F1-score, and ROC-AUC; MLP achieved 99.0% test accuracy. A stacking ensemble, optimized with a hybrid Particle Swarm–Grey Wolf Optimizer (PSO–GWO), delivered 99.99% test accuracy, 99.99% macro-averaged recall, and 1.0000 ROC-AUC. These findings demonstrate that combining ensemble learning with metaheuristic optimization can substantially enhance multi-class AQI classification performance and offer a practical path toward reliable, real-time air-quality assessment.</p>

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Air quality index AQI classification based on hybrid particle swarm and grey wolf optimization with ensemble machine learning model

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月5日

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  • Emad Elabd,
  • Hany Mohamed Hamouda,
  • M. A. Mohamed Ali,
  • A. S. Hamid,
  • Yasser Fouad

摘要

Accurate Air Quality Index (AQI) classification is essential for environmental surveillance and public health decision-making. Using a publicly available daily U.S. county-level dataset with six AQI categories (Good, Moderate, Unhealthy for Sensitive Groups, Unhealthy, Very Unhealthy, Hazardous), we conducted a comprehensive benchmarking study. Data preprocessing included missing-value imputation and class balancing via Synthetic Minority Over-sampling Technique (SMOTE). We trained and evaluated classical and deep models (Random Forest (RF), Extra Trees (ET), K-Nearest Neighbors (KNN), Naive Bayes (NB), Logistic Regression (LR), and a Multi-Layer Perceptron (MLP)) and assessed performance using cross-validation accuracy, test accuracy, macro-averaged recall, F1-score, and ROC-AUC. Ensemble methods (RF, ET) and the MLP consistently outperformed traditional baselines. RF achieved 99.3% test accuracy with perfect recall, F1-score, and ROC-AUC; MLP achieved 99.0% test accuracy. A stacking ensemble, optimized with a hybrid Particle Swarm–Grey Wolf Optimizer (PSO–GWO), delivered 99.99% test accuracy, 99.99% macro-averaged recall, and 1.0000 ROC-AUC. These findings demonstrate that combining ensemble learning with metaheuristic optimization can substantially enhance multi-class AQI classification performance and offer a practical path toward reliable, real-time air-quality assessment.