* Corresponding Author
Predictive Modeling of Urban Air Quality Using Machine Learning
https://doi.org/10.22146/ijccs.117546 Akhmad Dahlan(1*), Hanif Al Fatta(2), Lilis Dwi Farida(3), Hastari Utama(4), Yoga Pristyanto(5)
(1) Universitas Amikom Yogyakarta
(2) Universitas Amikom Yogyakarta
(3) Universitas Amikom Yogyakarta
(4) Universitas Amikom Yogyakarta
(5) Universitas Amikom Yogyakarta
(*) Corresponding Author
Abstract
Air pollution is a critical environmental concern adversely affecting public health in urban areas worldwide. Accurate prediction of air quality enables timely intervention by environmental agencies and policymakers. This study presents a comprehensive comparative analysis of ensemble machine learning methods for predicting urban air quality using the publicly available UCI Air Quality Dataset, containing 9,358 hourly instances of chemical sensor measurements collected in a heavily polluted Italian city from March 2024 to February 2025. Five algorithms were evaluated: Random Forest (RF), Gradient Boosting Machine (GBM), XGBoost, LightGBM, and CatBoost, against Ridge Regression and SVR baselines. A systematic pipeline encompassing preprocessing, temporal feature engineering, Bayesian hyperparameter optimization via Optuna, and time-series cross-validation was implemented. Results demonstrate XGBoost achieved the best performance with RMSE = 2.14, MAE = 1.58, and R² = 0.9312. SHAP-based feature importance analysis revealed CO(GT) lag features, C6H6(GT), and NOx(GT) as the most influential predictors.
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World Health Organization, "Ambient (outdoor) air pollution," WHO Fact Sheets, 2022. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health. [Accessed: 01-Oct-2025] [2] C. A. Pope, N. Coleman, Z. A. Pond, and R. T. Burnett, "Fine particulate air pollution and human mortality: 25+ years of cohort studies," Environmental Research, vol. 183, p. 108924, 2020. [Online]. Available: https://doi.org/10.1016/j.envres.2019.108924 [3] B. Zhang, Y. Rong, R. Yong, D. Qin, M. Li, G. Zou, and J. Pan, "Deep learning for air pollutant concentration prediction: A review," Atmospheric Environment, vol. 290, p. 119347, 2022. [Online]. Available: https://doi.org/10.1016/j.atmosenv.2022.119347 [4] Y. Bai, Y. Li, X. Wang, J. Xie, and C. Li, "Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition," Atmospheric Pollution Research, vol. 7, no. 3, pp. 557–566, 2016. [Online]. Available: https://doi.org/10.1016/j.apr.2016.01.004 [5] T. Chen and C. Guestrin, "XGBoost: A scalable tree boosting system," in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 2016, pp. 785–794. [Online]. Available: https://doi.org/10.1145/2939672.2939785 [6] G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T. Y. Liu, "LightGBM: A highly efficient gradient boosting decision tree," in Advances in Neural Information Processing Systems, vol. 30, 2017, pp. 3146–3154. [7] S. De Vito, E. Massera, M. Piga, L. Martinotto, and G. Di Francia, "On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario," Sensors and Actuators B: Chemical, vol. 129, no. 2, pp. 750–757, 2008. [Online]. Available: https://doi.org/10.1016/j.snb.2007.09.060 [8] D. Dua and C. Graff, "UCI Machine Learning Repository," University of California, Irvine, 2019. [Online]. Available: http://archive.ics.uci.edu/ml. [Accessed: 01-Oct-2025] [9] P. J. García-Laencina, J. L. Sancho-Gómez, and A. R. Figueiras-Vidal, "Pattern classification with missing data: A review," Neural Computing and Applications, vol. 19, no. 2, pp. 263–282, 2010. [Online]. Available: https://doi.org/10.1007/s00521-009-0295-6 [10] F. T. Liu, K. M. Ting, and Z. H. Zhou, "Isolation forest," in Proc. 8th IEEE Int. Conf. Data Mining, 2008, pp. 413–422. [Online]. Available: https://doi.org/10.1109/ICDM.2008.17 [11] L. Breiman, "Random forests," Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. [Online]. Available: https://doi.org/10.1023/A:1010933404324 [12] J. H. Friedman, "Greedy function approximation: A gradient boosting machine," Annals of Statistics, vol. 29, no. 5, pp. 1189–1232, 2001. [Online]. Available: https://doi.org/10.1214/aos/1013203451 [13] L. Prokhorenkova, G. Gusev, A. Vorobev, A. V. Dorogush, and A. Gulin, "CatBoost: Unbiased boosting with categorical features," in Advances in Neural Information Processing Systems, vol. 31, 2018, pp. 6638–6648. [14] T. Akiba, S. Sano, T. Yanase, T. Ohta, and M. Koyama, "Optuna: A next-generation hyperparameter optimization framework," in Proc. 25th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 2019, pp. 2623–2631. [Online]. Available: https://doi.org/10.1145/3292500.3330701 [15] F. X. Diebold and R. S. Mariano, "Comparing predictive accuracy," Journal of Business & Economic Statistics, vol. 13, no. 3, pp. 253–263, 1995. [Online]. Available: https://doi.org/10.1080/07350015.1995.10524599 [16] S. M. Lundberg and S. I. Lee, "A unified approach to interpreting model predictions," in Advances in Neural Information Processing Systems, vol. 30, 2017, pp. 4765–4774. [17] H. Peng, J. Li, Y. Song, and Q. Yang, "Attention-based temporal convolutional network for air quality prediction," Environmental Research, vol. 208, p. 112507, 2022. [Online]. Available: https://doi.org/10.1016/j.envres.2021.112507 [18] P. Massoli, M. Marinaro, G. Di Francia, and F. Formisano, "Calibrating chemical sensors in urban environments: SVMs vs ANNs for CO and NOx estimation," Sensors and Actuators B: Chemical, vol. 147, no. 2, pp. 478–489, 2010.
DOI: https://doi.org/10.22146/ijccs.117546
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