Proceedings of International Conference on Applied Innovation in IT  ·  2026/07/22  ·  Vol. 14  ·  Issue 4  ·  pp. 1163–1170
Prediction of PM2.5 and PM10 Concentrations Using Machine and Deep Learning Techniques: Evidence from Tashkent, Uzbekistan
Sokhobiddin Akhatkulov, Islom Yalgoshev, Izhar Uddin, Abubakir Abdullayev, Shohkrukh Sariyev and Rakhim Dusanov
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Abstract
This study investigates several machine and deep learning models to predict the concentrations of PM2.5 and PM10 particles in the air of Tashkent city. These particulate matters are the most influential factors in Tashkent’s high air quality index. The dataset utilized in this investigation comprises PM2.5 and PM10 concentration levels alongside meteorological indicators, including temperature, humidity, wind speed, and air pressure, gathered from 16 monitoring stations around Tashkent city and accessible at https://opendata.tashkent.uz/. The algorithms, Linear Regression (LR), Random Forest (RF), Support Vector Regression (SVR) and Gated Recurrent Unit (GRU) neural network were used to build predictive models on the concentrations of particles PM2.5 and PM10. The model performances were evaluated using metrics RMSE, MAE, MAPE, R2 and compared. The results show that the GRU and RF models performed significantly better than LR and SVR with R² values of 0.98 and 0.97, respectively, compared to the value of 0.91 and 0.93 respectively, achieved by LR and SVR. The GRU model was evaluated as the most effective approach due to its ability to deeply explore dynamic relationships across time series.
Keywords
PM25 PM10 Air Quality Machine and Deep Learning Models
References
  1. World Health Organization, “Ambient (outdoor) air quality and health,” WHO Fact Sheet, 2024, [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/ambient-%28outdoor%29-air-quality-and-health.
  2. European Environment Agency, “Harm to human health from air pollution in Europe,” EEA Report, 2024, [Online]. Available: https://www.eea.europa.eu/en/analysis/publications/harm-to-human-health-from-air-pollution-2024.
  3. R. P. Kumar, A. Prakash, R. Singh, et al., “Machine learning-based prediction of hazards fine PM2.5 concentrations: A case study of Delhi, India,” Discovery Geoscience, vol. 2, Art. no. 34, 2024, [Online]. Available: https://doi.org/10.1007/s44288-024-00043-z.
  4. S. Mampitiya, et al., “Exploring PM2.5 and PM10 ML Forecasting Models: A Comparative Study in the UAE,” Scientific Reports, vol. 15, Art. no. 9536, Mar. 2025, [Online]. Available: https://doi.org/10.1038/s41598-025-94013-1.
  5. M. Shukurova, M. Talipov, K. Ruziev, and K. Jurayeva, “Advanced geospatial monitoring of oil and gas infrastructure via satellite data,” Mathematical Models in Engineering, vol. 12, no. 2, pp. 190-201, Jun. 2026, [Online]. Available: https://doi.org/10.21595/mme.2026.25328.
  6. R. Aliev, M. Aliev, and G. Talipova, “Mathematical model and algorithm for determining the shunt zone by train,” AIP Conference Proceedings, vol. 3447, no. 1, Art. no. 020004, May 2026, [Online]. Available: https://doi.org/10.1063/12.0044000.
  7. A. Mampitiya, N. Rathnayake, Y. S. Leon, P. Hoshino, and U. Rathnayake, “Machine Learning Approaches for Predicting the Air Quality Index,” Environmental Pollution, 2023, [Online]. Available: https://doi.org/10.1016/j.envpol.2023.122293.
  8. Q. Di, Y. Wang, et al., “An ensemble-based model of PM2.5 concentration across the contiguous United States with high spatiotemporal resolution,” Environment International, vol. 141, Art. no. 105726, 2020, [Online]. Available: https://doi.org/10.1016/j.envint.2020.105726.
  9. A. Masood and K. Ahmad, “Data-Driven Predictive Modeling of PM2.5 Concentrations Using Machine Learning and Deep Learning Techniques: A Case Study of Delhi, India,” Environmental Monitoring and Assessment, vol. 195, Art. no. 60, 2022, [Online]. Available: https://doi.org/10.1007/s10661-022-10603-w.
  10. L. Qing, “PM2.5 Concentration Prediction Using GRA-GRU Network,” Sustainability, vol. 15, no. 3, Art. no. 1973, 2023, [Online]. Available: https://doi.org/10.3390/su15031973.
  11. L. Dai, C. Zhang, and M. Lei, “Dynamic forecasting model of short-term PM2.5 concentration based on machine learning,” Journal of Computer Applications, vol. 37, pp. 3057-3063, 2017.
  12. X. Wu, J. Zhu, and Q. Wen, “Short-Term Prediction of PM2.5 Concentration by Hybrid Neural Network Based on Sequence Decomposition,” PLOS ONE, vol. 19, no. 5, Art. no. e0299603, May 2024, [Online]. Available: https://doi.org/10.1371/journal.pone.0299603.
  13. S. Zhou, W. Wang, L. Zhu, Q. Qiao, and Y. Kang, “Deep-Learning Architecture for PM2.5 Concentration Prediction: A Review,” Environmental Science and Ecotechnology, vol. 21, Art. no. 100400, 2024, [Online]. Available: https://doi.org/10.1016/j.ese.2024.100400.
  14. A. Shakya, M. S. Gohain, and R. Singh, “A Systematic Study on PM2.5 and PM10 Concentration Prediction in Air Pollution Using Machine Learning and Deep Learning Model,” Environmental Pollution, 2025, [Online]. Available: https://doi.org/10.1016/j.envpol.2025.122293.
  15. H. Alrashidi, F. N. Sibai, A. Abonamah, M. Alrashidi, and A. Alsaber, “PM2.5: Air Quality Index Prediction Using Machine Learning: Evidence from Kuwait’s Air Quality Monitoring Stations,” Sustainability, vol. 17, no. 20, Art. no. 9136, 2025, [Online]. Available: https://doi.org/10.3390/su17209136.
  16. C. Chen, et al., “A deep learning approach to identify smoke plumes in satellite imagery in near-real time for health risk communication,” Environmental Health, vol. 20, Art. no. 11, 2021, [Online]. Available: https://doi.org/10.1186/s12940-021-00722-z.
  17. Z. Gao, K. Do, Z. Li, X. Jiang, K. J. Maji, C. E. Ivey, and A. G. Russell, “Predicting PM2.5 levels and exceedance days using machine learning methods,” Atmospheric Environment, vol. 321, Art. no. 120293, 2024, [Online]. Available: https://doi.org/10.1016/j.atmosenv.2024.120293.
  18. K. Kumar and D. B. Pande, “Air Pollution Prediction with Machine Learning: A Case Study of Indian Cities,” International Journal of Environmental Science and Technology, vol. 20, pp. 5333-5348, 2022, [Online]. Available: https://doi.org/10.1007/s13762-022-04241-5.
  19. V. N. Vapnik, The Nature of Statistical Learning Theory. New York, NY, USA: Springer, 1995.
  20. S. Akhatkulov, I. Yalgoshev, and J. Haydarov, “Different Warmup and Annealing Strategies for ANN Models to Predict Air Quality Index,” in Proc. 2025 International Russian Automation Conference (RusAutoCon), Sochi, Russian Federation, pp. 491-496, 2025, [Online]. Available: https://doi.org/10.1109/RusAutoCon65989.2025.11177428.
  21. S. Liu, B. Li, and G. Hu, “Prediction of PM2.5 concentration based on random forest algorithm and meteorological data,” Atmosphere, vol. 13, no. 4, Art. no. 521, 2022, [Online]. Available: https://doi.org/10.3390/atmos13040521.
  22. Y. Wu and Q. Wang, “LightGBM Based Optiver Realized Volatility Prediction,” in Proc. 2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE), SC, USA, pp. 227-230, 2021, [Online]. Available: https://doi.org/10.1109/CSAIEE54046.2021.9543438.
  23. S. Akhatkulov, I. Yalgoshev, and Z. Urinboyev, “Vehicle CO2 Emission Prediction Using Deep Learning and Ensemble Machine Learning Methods,” in Proc. 2025 International Russian Automation Conference (RusAutoCon), Sochi, Russian Federation, pp. 819-824, 2025, [Online]. Available: https://doi.org/10.1109/RusAutoCon65989.2025.11177377.
  24. P. Mahajan, S. Uddin, F. Hajati, and M. A. Moni, “Ensemble Learning for Disease Prediction: A Review,” Healthcare, vol. 11, no. 12, Art. no. 1808, Jun. 2023, [Online]. Available: https://doi.org/10.3390/healthcare11121808.
  25. L. Breiman, “Random Forests,” Machine Learning, vol. 45, no. 1, pp. 5-32, 2001, [Online]. Available: https://doi.org/10.1023/A:1010933404324.
  26. M. H. Abdulameer and M. Z. Abdullah, “Datasets Classification Using Deep Learning and Machine Learning Classification Algorithms,” AIP Conference Proceedings, vol. 2591, no. 1, Art. no. 030032, 2023, [Online]. Available: https://doi.org/10.1063/5.0120454.
  27. J. Zhang, B. Li, and X. Chen, “PM2.5 concentration prediction with GRU and meteorological features in Beijing,” Atmospheric Pollution Research, vol. 14, no. 6, Art. no. 101765, 2023, [Online]. Available: https://doi.org/10.1016/j.apr.2023.101765.
  28. K. Cho, B. van Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning Phrase Representations Using RNN Encoder-Decoder for Statistical Machine Translation,” arXiv preprint arXiv:1406.1078, 2014, [Online]. Available: https://doi.org/10.48550/arXiv.1406.1078.
  29. H. Bui, et al., “PM2.5 prediction using Random Forest algorithm: A case study of Hanoi, Vietnam,” Science of the Total Environment, vol. 818, Art. no. 151791, 2022, [Online]. Available: https://doi.org/10.1016/j.scitotenv.2021.151791.
  30. T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), New York, NY, USA, pp. 785-794, 2016.
  31. L. Gosink, et al., “Characterizing and Visualizing Predictive Uncertainty in Numerical Ensembles Through Bayesian Model Averaging,” IEEE Transactions on Visualization and Computer Graphics, vol. 19, no. 12, pp. 2703-2712, Dec. 2013, [Online]. Available: https://doi.org/10.1109/TVCG.2013.138.
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