Logo PTI Logo FedCSIS

Position and Communication Papers of the 16th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 26

Optimized stochastic methods for sensitivity analysis for large-scale air pollution model

, , ,

DOI: http://dx.doi.org/10.15439/2021F51

Citation: Position and Communication Papers of the 16th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 26, pages 8588 ()

Full text

Abstract. Environmental security is rapidly becoming a significant topic of present interest all over the world, and environmental modelling has a very high priority in various scientific fields, respectively.  Different optimizations of the Latin Hypercube Sampling algorithm have been used in our sensitivity studies of the model output results for some air pollutants with respect to the emission levels and some chemical reactions rates.


  1. G. Dimitriu: Global Sensitivity Analysis for a Chronic Myelogenous Leukemia Model: Proc. 9th International Conference NMA’2018, Borovets, Bulgaria, August 20-24, 2018, LNCS 11189, Springer, Jan 2019. http://dx.doi.org/10.1007/978-3-030-10692-8 42
  2. Gocheva-Ilieva, Snezhana G., Atanas V. Ivanov, and Ioannis E. Livieris. ”High Performance Machine Learning Models of Large Scale Air Pollution Data in Urban Area.” Cybernetics and Information Technologies 20.6 (2020): 49-60.
  3. Gocheva-Ilieva, S. G., Voynikova, D. S., Stoimenova, M. P., Ivanov, A. V., & Iliev, I. P. (2019). Regression trees modeling of time series for air pollution analysis and forecasting. Neural Computing and Applications, 31(12), 9023-9039.
  4. H. Hamdad, Ch. Pézerat, B. Gauvreau, Ch. Locqueteau, Y. Denoual, Sensitivity analysis and propagation of uncertainty for the simulation of vehicle pass-by noise, Applied Acoustics Vol. 149, Elsevier, pp. 85-98 (June 2019). http://dx.doi.org/10.1016/j.apacoust.2019.01.026
  5. Kroese, D.P., Taimre, T., Botev, Z.: Handbook of Monte Carlo Methods, Wiley Series in Probability and Statistics, (2011)
  6. McKay, M.D., Beckman, R.J., Conover, W.J.: A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21(2), 239–45 (1979)
  7. Minasny B., McBratney B.: A conditioned Latin hypercube method for sampling in the presence of ancillary information Journal Computers and Geosciences archive, Volume 32 Issue 9, November, 2006, Pages 1378-1388.
  8. Minasny B., McBratney B.: Conditioned Latin Hypercube Sampling for Calibrating Soil Sensor Data to Soil Properties, Chapter: Proximal Soil Sensing, Progress in Soil Science, pp. 111-119, 2010.
  9. Pencheva, Velizara, Ivan Georgiev, and Asen Asenov. “Evaluation of passenger waiting time in public transport by using the Monte Carlo method.” AIP Conference Proceedings. Vol. 2321. No. 1. AIP Publishing LLC, 2021.
  10. I. M. Sobol’, Sensitivity estimates for nonlinear mathematical models, Matem. Modelirovanie 2 (1) (1990), 112–118. (2020,
  11. Veleva, E., & Georgiev, I. R. December). Seasonality of the levels of particulate matter PM10 air pollutant in the city of Ruse, Bulgaria. In AIP Conference Proceedings (Vol. 2302, No. 1, p. 030006). AIP Publishing LLC.
  12. Z. Zlatev, Computer treatment of large air pollution models, KLUWER Academic Publishers, Dorsrecht-Boston-London, 1995.
  13. Z. Zlatev, I. T. Dimov, Computational and Numerical Challenges in Environmental Modelling, Elsevier, Amsterdam, 2006.
  14. Z. Zlatev, I. Dimov, K. Georgiev, Three-dimensional version of the Danish Eulerian Model, Zeitschrift für Angewandte Mathematik und Mechanik, 76 (1996) S4, 473-476.