WP6 – Stage 2 – AQI modeling and forecasting
Planned activities during stage 2
Activity 6.3
Creation and testing of new hybrid methods for creating statistical models with enhanced predictive characteristics, applicable for short-term future forecasts in real time for various classification groups of settlements.
Activity 6.5
Algorithmization, testing and software implementation of the complex of statistical methods for online short-term forecasts of air cleanliness / pollution.
Activity 6.6
(dropped from the work program)
Activity 6.7
Renewal and improvement of the established complex of methods and approaches for modeling and forecasting air pollution.
Results achieved during stage 2
During stage 2, all planned results under FP6 have been achieved, as summarised below:
- A total of about 180 models have been developed, with built-in testing and self-learning (supervised machine learning), and with validation implemented by predicting an external test sample, which is not used in the modeling procedure and simulates future data.
- Through testing for short-term forecasts, it has been proven that the models are fully suitable for real future air pollution forecasting in the presence of data/forecasts for meteorological and atmospheric variables for a given settlement. Licensed software versions of: Salford Predictive Modeler, IBM SPSS, Wolfram Mathematica, Excel and free software such as Phyton, etc. were used.
- The results achieved under this work package are presented in the following scientific publications:
- In journals with an impact factor / impact rank – 1.75 papers1
[6.1*] S.G. Gocheva-Ilieva, A.V. Ivanov, H.N. Kulina, M.P. Stoimenova-Minova. 2023. “Multi-Step Ahead Ex-Ante Forecasting of Air Pollutants Using Machine Learning”. Mathematics, Vol. 11, Issue 7, Article No. 1566. MDPI. eISSN: 2227-7390. DOI: 10.3390/math11071566
WoS, IF=2.3 / Q1 (top 5%); Scopus, SJR=0.475 / Q2
https://www.mdpi.com/2227-7390/11/7/1566[6.2*] S.G. Gocheva-Ilieva, A.V. Ivanov, M.P. Stoimenova-Minova, S.K. Koleva-Pavlova. 2023. “Discrete Wavelet Transform and Ensemble Tree Algorithms for Air Pollutant Modeling: A Case Study”. International Journal of Membrane Science and Technology, Vol. 10, No. 4, pp. 1357-1373. September. ISSN: 2410-1869. DOI: 10.15379/ijmst.v10i4.2251. Scopus, SJR2022=0.143 / Q4
https://cosmosscholars.com/phms/index.php/ijmst/article/view/2251 - In conference proceedings, referenced in WoS/Scopus – 3 publications
[6.3*] A.V. Ivanov, S.G. Gocheva-Ilieva, M.P. Stoimenova-Minova. 2023. “Temporal-causal modeling of air pollution in the city of Plovdiv, Bulgaria: A case study”. 15th Conference of the Euro-American Consortium for Promoting the Application of Mathematics in Technical and Natural Sciences (AMiTaNS 2023), Albena, Bulgaria. 21-26 June. Journal of Physics: Conference Series, Vol. 2675 (1), Art. No. 012002, IOP Publishing. ISSN: 1742-6588 / 1742-6596. DOI: 10.1088/1742-6596/2675/1/012002. Scopus, SJR=0.180 / Q4
https://iopscience.iop.org/article/10.1088/1742-6596/2675/1/012002[6.4*] A. Ivanov, S. Gocheva-Ilieva, M. Stoimenova-Minova. 2024. “Exploring SO2 air pollution in Plovdiv through multivariate adaptive regression splines: A case study”. 16th Conference of the Euro-American Consortium for Promoting the Application of Mathematics in Technical and Natural Sciences (AMiTaNS 2024), Albena, Bulgaria. 21-26 June. Journal of Physics: Conference Series, Vol. 2910, Art. No. 012017, IOP Publishing. ISSN: 1742-6588 / 1742-6596. DOI: 10.1088/1742-6596/2910/1/012017. Scopus, SJR=0.180 / Q4
https://iopscience.iop.org/article/10.1088/1742-6596/2910/1/012017[6.5*] M. Stoimenova-Minova, S. Gocheva-Ilieva, A. Ivanov. 2024. “Application of Discrete Wavelet Transform and Tree-Based Ensemble Machine Learning for Modeling of Particulate Matter Concentrations”. In: Gayoso Martínez, V., Yilmaz, F., Queiruga-Dios, A., Rasteiro, D.M., Martín-Vaquero, J., Mierluş-Mazilu, I. (eds.), Mathematical Methods for Engineering Applications. Springer Proceedings in Mathematics and Statistics, Vol. 439, pp. 171-183. Springer, Cham. ISSN: 2194-1009 / 2194-1017. ISBN: 978-303149217-4. DOI: 10.1007/978-3-031-49218-1_12.
Scopus, SJR2023=0.168
https://link.springer.com/chapter/10.1007/978-3-031-49218-1_12 - Monograph – 1 publication
[6.5*] S. Gocheva-Ilieva, A. Ivanov, M. Stoimenova-Minova. 2025. “Stochastic and Tree-Based Machine Learning for Air Pollution Forecasting”. Nova Science Publishing, New York. (monograph approved for publication; signed contract with the publisher – see directory '9. Scientific publications' on the attached flash drive; will be referenced in Scopus)
1 The total number of publications in this category is not an integer, since the contribution of the publication project [6.1*] is estimated at 75%, taking into account the ratio of authors - members of the project team (3) to the total number of authors (4), since the article also expresses gratitude to another (Bulgarian) project of ФНИ с № KP-06-N52/9.