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Monte Carlo Adaptive Technique for Sensitivity Analysis of a Large-Scale Air Pollution Model

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Large-Scale Scientific Computing (LSSC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5910))

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Abstract

Variance-based sensitivity analysis has been performed for a study of input parameters contribution into output variability of a large-scale air pollution model — the Unified Danish Eulerian Model. The problem of computing of numerical indicators of sensitivity — Sobol’ global sensitivity indices leads to multidimensional integration. Plain and Adaptive Monte Carlo techniques for numerical integration have been analysed and applied. Numerical results for sensitivity of pollutants concentrations to chemical rates variability are presented.

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References

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Author information

Authors and Affiliations

  1. ACET, The University of Reading, Whiteknights, PO Box 225, Reading, RG6 6AY, UK

    Ivan Dimov

  2. IPP, Bulgarian Academy of Sciences, Acad. G. Bonchev 25 A, 1113, Sofia, Bulgaria

    Ivan Dimov & Rayna Georgieva

Authors
  1. Ivan Dimov
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  2. Rayna Georgieva
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Editor information

Editors and Affiliations

  1. Institute for Parallel Processing, Bulgarian Academy of Sciences, Acad. G. Bonchev, Bl. 25A, 1113, Sofia, Bulgaria

    Ivan Lirkov

  2. Institute for Parallel Processing, Bulgarian Academy of Sciences, Acad. G. Bonchev Str. Bl. 25-A, 1113, Sofia, Bulgaria

    Svetozar Margenov

  3. Department of Informatics and Mathematical Modelling, Technical University of Denmark, Richard Petersens Plads - Building 321, 2800, Kongens Lyngby, Denmark

    Jerzy Waśniewski

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Dimov, I., Georgieva, R. (2010). Monte Carlo Adaptive Technique for Sensitivity Analysis of a Large-Scale Air Pollution Model. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds) Large-Scale Scientific Computing. LSSC 2009. Lecture Notes in Computer Science, vol 5910. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12535-5_45

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  • DOI: https://doi.org/10.1007/978-3-642-12535-5_45

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12534-8

  • Online ISBN: 978-3-642-12535-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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