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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 6/2021

21.06.2020 | Original Article

Advanced stochastic approaches for Sobol’ sensitivity indices evaluation

verfasst von: Venelin Todorov, Ivan Dimov, Tzvetan Ostromsky, Stoyan Apostolov, Rayna Georgieva, Yuri Dimitrov, Zahari Zlatev

Erschienen in: Neural Computing and Applications | Ausgabe 6/2021

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Abstract

Sensitivity analysis is a modern promising technique for studying large systems such as ecological systems. The main idea of sensitivity analysis is to evaluate and predict (through computer simulations on large mathematical models) the measure of the sensitivity of the model’s output to the perturbations of some input parameters, and it is a technique for refining the mathematical model. The main problem in the sensitivity analysis is the evaluation of total sensitivity indices. The mathematical formulation of this problem is represented by a set of multidimensional integrals. In this work, some new stochastic approaches for evaluating Sobol’ sensitivity indices of the unified Danish Eulerian model have been presented. For the first time, a special type of digital nets and lattice rules are applied for multidimensional sensitivity analysis and their advantages are discussed. A comparison of accuracy of eight stochastic approaches for evaluating Sobol’ sensitivity indices is performed. The obtained results will be important and useful for the surveyed scientists (physicists, chemicals, meteorologists) to make a comparative classification of the input parameters with respect to their influence on the concentration of the pollutants of interest.
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Metadaten
Titel
Advanced stochastic approaches for Sobol’ sensitivity indices evaluation
verfasst von
Venelin Todorov
Ivan Dimov
Tzvetan Ostromsky
Stoyan Apostolov
Rayna Georgieva
Yuri Dimitrov
Zahari Zlatev
Publikationsdatum
21.06.2020
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 6/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05074-4

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