Importance measures in global sensitivity analysis of nonlinear models
The present paper deals with a new method of global sensitivity analysis of nonlinear models. This is based on a measure of importance to calculate the fractional contribution of the input parameters to the variance of the model prediction. Measures of importance in sensitivity analysis have been suggested by several authors, whose work is reviewed in this article. More emphasis is given to the developments of sensitivity indices by the Russian mathematician I.M. Sobol. His formalism is employed throughout this paper where conceptual and computational improvements of the method are presented. The computational novely of this study is the introduction of the total effect parameter index. This index provides a measure of the total effect of a given parameter, including all the possible synergetic terms between that parameter and all the others. Rank transformation of the data is also introduced in order to increase the reproducibility of the method. These methods are tested on a few analytical and computer models. The main conclusion of this work is the identification of a sensitivity analysis methodology which is both flexible, accurate and informative, and which can be achieved at reasonable computational cost.
Bibliographic Reference: Article: Reliability Engineering and System Safety, Vol. 52 (1996), pp. 1-17
Record Number: 199611013 / Last updated on: 1996-09-30
Original language: en
Available languages: en