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Interpretable machine learning framework for predicting Urban air quality
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AI
DOI：10.1371/journal.pone.0336241.png)
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Abstract
En 中文
Urban air pollution remains a critical challenge for public health and environmental sustainability. This study investigates the predictive capabilities of five machine learning (ML) models: Linear Regression (LR), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR) for forecasting the Air Quality Index (AQI) using the widely adopted Air Quality dataset from the UCI ML Repository. Although collected in 2004-2005, the dataset continues to serve as a benchmark in recent literature and provides a reproducible testbed for methodological evaluation. After structured pre-processing, feature engineering, and chronological train-validation-test splitting, models were rigorously tuned and assessed using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2), with 95% bootstrap confidence intervals and corrected resampled t-tests confirming statistical significance. Ensemble models achieved the best performance, with Random Forest obtaining the lowest RMSE (12.48) and MAE (9.35), and XGBoost achieving the highest R2 (0.89). Feature importance analysis identified NOx, PM2.5, and CO as the most influential predictors. We incorporated Shapley Additive exPlanations (SHAP) analyses and case-level visualizations to support interpretability, providing transparent insights for practical decision-making. While the study is limited by the absence of external validation and genetic variables (e.g., APOE), it establishes a reproducible, interpretable, and computationally efficient ML framework for AQI forecasting. The findings highlight the continuing relevance of benchmark datasets for reproducible evaluation and demonstrate the potential of interpretable ML-based approaches for smart city air quality management and public health policy.
Keywords:
Machine Learning
Air Quality Index
Random Forest
XGBoost
SHAP Analysis
Journal
IF:
2.6
Papers:
2.6W
Citations:
81.6W
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