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

• Autorzy: Todorov Venelin , Ostromsky Tzvetan , Dimov Ivan , Georgieva Rayna

Identyfikatory

DOI

10.15439/2021F51

• Konferencja: Federated Conference on Computer Science and Information Systems (16 ; 02-05.09.2021 ; online)
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

environmental security; environmental modelling; sensitivity study; air pollutants

PL

bezpieczeństwo środowiskowe; modelowanie środowiskowe; badanie wrażliwości; zanieczyszczenia powietrza

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2021
• Tom: Vol. 26
• Strony: 85--88
• Opis fizyczny: Bibliogr. 14 poz., tab., wykr.

Twórcy

autor

Todorov Venelin

• Bulgarian Academy of Scienses, Institute of Mathematics and Informatics, ul. G. Bonchev 8, 1113 Sofia, Bulgaria
• Bulgarian Academy of Sciences, Institute of Information and Communication Technologies, ul. G. Bonchev 25A, 1113 Sofia, Bulgaria

autor

Ostromsky Tzvetan

• Bulgarian Academy of Sciences, Institute of Information and Communication Technologies, ul. G. Bonchev 25A, 1113 Sofia, Bulgaria

autor

Dimov Ivan

• Bulgarian Academy of Sciences, Institute of Information and Communication Technologies, ul. G. Bonchev 25A, 1113 Sofia, Bulgaria

autor

Georgieva Rayna

• Bulgarian Academy of Sciences, Institute of Information and Communication Technologies, ul. G. Bonchev 25A, 1113 Sofia, Bulgaria

Bibliografia

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• 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.
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• 5. Kroese, D.P., Taimre, T., Botev, Z.: Handbook of Monte Carlo Methods, Wiley Series in Probability and Statistics, (2011)
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• 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.
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• 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.
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Uwagi

1. Preface

2. Session: 14th International Workshop on Computational Optimization

3. Communication Papers