A Hammersley and van der Corput sequences comparison for multidimensional sensitivity analysis.

In this paper for the first time a Hammersley and van der Corput sequences comparison for multidimensional sensitivity analysis has been done. Sensitivity analysis of model outputs to variation or natural uncertainties of model inputs is very significant for improving the reliability of these models. By definition, sensitivity analysis is a procedure for studying how sensitive are the output results of large-scale mathematical models to some uncertainties of the input data. The algorithms for multidimensional sensitivity analysis are applied to compute global Sobol sensitivity measures corresponding to the influence of several input parameters on the concentrations of important air pollutants. A deep experimental investigation is done to demonstrate the pros and cons of both approaches in the case study of the considered model. [ABSTRACT FROM AUTHOR]