High Performance Machine Learning Models of Large Scale Air Pollution Data in Urban Area

1. Directive 2008/50/EC of the European Parliament and of the Council of 21 May 2008 on Ambient Air Quality and Cleaner Air for Europe. Official Journal of the European Union. Vol. L152. 2008, No 1.Search in Google Scholar

2. Air Quality Standards. European Commission. Environment, 2015 (online). http://ec.europa.eu/environment/air/quality/standards.htmSearch in Google Scholar

3. Air Quality in Europe – 2019 Report. European Environment Agency. EEA Report 10, 2019 (online). https://www.eea.europa.eu/publications/air-quality-in-europe-2019.Search in Google Scholar

4. Brunekreef, B., S. T. Holgate. Air Pollution and Health. – The Lancet, Vol. 360, 2002, No 9341, pp. 1233-1242.10.1016/S0140-6736(02)11274-8Search in Google Scholar

5. Guarnieri, M., J. R. Balmes. Outdoor Air Pollution and Asthma. – The Lancet, Vol. 383, 2014, No 9928, pp. 1581-1592.10.1016/S0140-6736(14)60617-6Search in Google Scholar

6. Hu, W., K. Mengersen, A. McMichael, S. Tong. Temperature, Air Pollution and Total Mortality During Summers in Sydney, 1994-2004. – International Journal of Biometeorology, Vol. 52, 2008, No 7, pp. 689-696.10.1007/s00484-008-0161-8Search in Google Scholar

7. Livieris, I. E., S. Stavroyiannis, E. Pintelas, P. Pintelas. A Novel Validation Framework to Enhance Deep Learning Models in Time-Series Forecasting. – Neural Computing and Applications, 2020. https://doi.org/10.1007/s00521-020-05169-y10.1007/s00521-020-05169-ySearch in Google Scholar

8. Durão, R. M., M. T. Mendes, M. J. Pereira. Forecasting O₃ Levels in Industrial Area Surroundings up to 24 h in Advance, Combining Classification Trees and MLP Models. – Atmospheric Pollution Research, Vol. 7, 2016, pp. 961-970.10.1016/j.apr.2016.05.008Search in Google Scholar

9. Biancofiore, F., M. Busilacchio, M. Verdecchia, B. Tomassetti, E. Aruffo, S. Bianco, S. Di Tommaso, C. Colangeli, G. Rosatelli, P. Di Carlo. Recursive Neural Network Model for Analysis and Forecast of PM10 and PM2.5. – Atmospheric Pollution Research, Vol. 8, 2017, No 4, pp. 652-659.10.1016/j.apr.2016.12.014Search in Google Scholar

10. Bougoudis, I., K. Demertzis, L. Iliadis. HISYCOL a Hybrid Computational Intelligence System for Combined Machine Learning: The Case of Air Pollution Modelling in Athens. – Neural Computing and Applications, Vol. 27, 2016, No 5, pp. 1191-1206.10.1007/s00521-015-1927-7Search in Google Scholar

11. Zhang, H., S. Zhang, P. Wang, Y. Qin, H. Wang. Forecasting of Particulate Matter Time Series Using Wavelet Analysis and Wavelet-ARMA/ARIMA Model in Taiyuan, China. – Journal of the Air & Waste Management Association, Vol. 67, 2017, No 7, pp. 776-788.10.1080/10962247.2017.1292968Search in Google Scholar

12. Gardner, M. W., S. R. Dorling. Statistical Surface Ozone Models: An Improved Methodology to Account for Non-Linear Behavior. – Atmospheric Environment, Vol. 34, 2000, pp. 21-34.10.1016/S1352-2310(99)00359-3Search in Google Scholar

13. Singh, K. P., S. Gupta, P. Rai. Identifying Pollution Sources and Predicting Urban Air Quality Using Ensemble Learning Methods, – Atmospheric Environment, Vol. 80, 2013, pp. 426-437.10.1016/j.atmosenv.2013.08.023Search in Google Scholar

14. Bai, Y., Y. Li, X. Wang, J. Xie, C. Li. Air Pollutants Concentrations Forecasting Using Back Propagation Neural Network Based on Wavelet Decomposition with Meteorological Conditions. – Atmospheric Pollution Research, Vol. 7, 2016, No 3, pp. 557-566.10.1016/j.apr.2016.01.004Search in Google Scholar

15. Dotse, S.-Q., M. I. Petra, L. Dagar, L. C. De Silva. Application of Computational Intelligence Techniques to Forecast Daily PM10 Exceedances in Brunei Darussalam. – Atmospheric Pollution Research, Vol. 9, 2018, No 2, pp. 358-368.10.1016/j.apr.2017.11.004Search in Google Scholar

16. Roy, S. S., C. Pratyush, C. Barna. Predicting Ozone Layer Concentration Using Multivariate Adaptive Regression Splines, Random Forest and Classification and Regression Tree. – In: Advances in Intelligent Systems and Computing. Vol. 634. 2018, pp. 140-152.10.1007/978-3-319-62524-9_11Search in Google Scholar

17. Liu, B., C. Shi, J. Li, Y. Li, J. Lang, R. Gu. Comparison of Different Machine Learning Methods to Forecast Air Quality Index. – In: Lecture Notes in Electrical Engineering. Vol. 542. 2019, pp. 235-245.10.1007/978-981-13-3648-5_27Search in Google Scholar

18. Masih, A. Comparative Analysis of Tree, Meta-Learning and Function Classifiers to Predict the Atmospheric Concentration of NO₂. – Journal of Environmental Accounting and Management, Vol. 8, 2020, No 1, pp. 31-39.10.5890/JEAM.2020.03.003Search in Google Scholar

19. Masmoudi, S., H. Elghazel, D. Taieb, O. Yazar, A. Kallel. A Machine-Learning Framework for Predicting Multiple Air Pollutants’ Concentrations Via Multi-Target Regression and Feature Selection. – In: Science of the Total Environment. Vol. 715. 2020, 136991.10.1016/j.scitotenv.2020.13699132041079Search in Google Scholar

20. Martínez-España, R., A. Bueno-Crespo, I. Timón, J. Soto, A. Muñoz, J. M. Cecilia. Air-Pollution Prediction in Smart Cities through Machine Learning Methods: A Case of Study in Murcia, Spain. – Journal of Universal Computer Science, Vol. 24, 2018, No 3, pp. 261-276.Search in Google Scholar

21. Veleva, E., I. Zheleva. GARCH Models for Particulate Matter PM10 Air Pollutant in the City of Ruse, Bulgaria. – In: AIP Conference Proceedings, Vol. 2025, 2018, 040016.10.1063/1.5064900Search in Google Scholar

22. Joharestani, M. Z., C. Cao, X. Ni, B. Bashir, S. Talebiesfandarani. PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data. – Atmosphere, Vol. 10, 2019, No 7, 373.10.3390/atmos10070373Search in Google Scholar

23. Breiman, L. Random Forests. – Machine Learning, Vol. 45, 2001, No 1, pp. 5-32.10.1023/A:1010933404324Search in Google Scholar

24. Box, G. E. P., G. M. Jenkins. Time Series Analysis, Forecasting and Control. Revised Edition. San Francisco. San Francisco, Holden-Day, 1976.Search in Google Scholar

25. Gocheva-Ilieva, S. G., D. S. Voynikova, M. P. Stoimenova, A. V. Ivanov, I. P. Iliev. Regression Trees Modeling of Time Series for Air Pollution Analysis and Forecasting. – Neural Computing and Applications, Vol. 31, 2019, No 12, pp. 9023-9039.10.1007/s00521-019-04432-1Search in Google Scholar

26. Weaver, K. F., V. Morales, S. L. Dunn, K. Godde, P. F. Weaver. Pearson’s and Spearman’s Correlation. – In: An Introduction to Statistical Analysis in Research, John Wiley & Sons, Inc., New Jersey, 2017, Ch. 10, pp. 435-471.10.1002/9781119454205Search in Google Scholar