Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 23

Communication Papers of the 2020 Federated Conference on Computer Science and Information Systems

Sensitivity Study of a Large-Scale Air Pollution Model by Using Optimized Stochastic Algorithm

, , ,

DOI: http://dx.doi.org/10.15439/2020F107

Citation: Communication Papers of the 2020 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 23, pages 2932 ()

Full text

Abstract. In this work a systematic procedure for multidimensional sensitivity analysis in the area of air pollution modeling by an optimized latin hypercube sampling has been done. The Unified Danish Eulerian Model (UNI-DEM) is used in our work, because this model is one of the most advanced large-scale mathematical models that describes adequately all physical and chemical processes. We study with new optimized stochastic method the sensitivity of concentration variations of some of the most dangerous air pollutants with respect to the anthropogenic emissions levels and with respect to some chemical reactions rates.

References

  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. I.T. Dimov, R. Georgieva. Monte Carlo Method for Numerical Integra tion based on Sobol’ Sequences. Numerical Methods and Applications (I. Dimov, S. Dimova, N. Kolkovska - Eds.), LNCS 6046, Springer, 2011, 50–59, https://doi.org/10.1007/978-3-642-18466-6_5.
  3. I. T. Dimov, R. Georgieva. Multidimensional Sensitivity Analysis of Large-scale Mathematical Models. O.P. Iliev et al. (eds.), Numerical Solution of Partial Differential Equations: Theory, Algorithms, and Their Applications, Springer Proceedings in Mathematics & Statistics 45, Springer Science+Business Media, New York, 2013, 137–156, https://doi.org/10.1007/978-1-4614-7172-1_8.
  4. Dimov I., Karaivanova A., Error analysis of an adaptive Monte Carlo method for numerical integration, Mathematics and Computers in simulation 47 pp. 201–213, 1998, https://doi.org/10.1016/S0378-4754(98)00103-7.
  5. Dimov I., Karaivanova A., Georgieva R., Ivanovska S. (2003) Parallel Importance Separation and Adaptive Monte Carlo Algorithms for Multiple Integrals, Springer Lecture Notes in Computer Science, 2542, Springer-Verlag, Berlin, Heidelberg, New York: 99–107, https://doi.org/10.1007/3-540-36487-0_10.
  6. 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
  7. T. Homma, A. Saltelli, Importance Measures in Global Sensitivity Analysis of Nonlinear Models, Reliability Engineering and System Safety 52, 1996, 1–17, https://doi.org/10.1016/0951-8320(96)00002-6.
  8. Jarosz, W.: Efficient Monte Carlo Methods for Light Transport in Scattering Media, PhD dissertation, UCSD, (2008).
  9. 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), https://www.jstor.org/stable/1268522?seq=1.
  10. 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, https://doi.org/10.1016/j.cageo.2005.12.009.
  11. S. Poryazov, E. Saranova, and I. Ganchev. Conceptual and Analytical Models for Predicting the Quality of Service of Overall Telecommunication Systems. Autonomous Control for a Reliable Internet of Services. Springer, Cham, 2018, 151-181, https://doi.org/10.1007/978-3-319-90415-3_7.
  12. Sobol I.M., Tarantola S., Gatelli D., Kucherenko S., Mauntz W., Estimating the approximation error when fixing unessential factors in global sensitivity analysis, Reliability Engineering & System Safety, 2007, 92, 957–960, https://doi.org/10.1016/j.ress.2006.07.001.
  13. Zheleva, I., Georgiev, I., Filipova, M., & Menseidov, D. (2017, October). Mathematical modeling of the heat transfer during pyrolysis process used for end-of-life tires treatment. In AIP Conference Proceedings (Vol. 1895, No. 1, p. 030008). AIP Publishing LLC, https://doi.org/10.1063/1.5007367.
  14. Z. Zlatev, Computer treatment of large air pollution models, KLUWER Academic Publishers, Dorsrecht-Boston-London, 1995.
  15. Z. Zlatev, I. T. Dimov, Computational and Numerical Challenges in Environmental Modelling, Elsevier, Amsterdam, 2006.