Performance profiles for benchmarking of global sensitivity analysis algorithms

Nowadays, sensitivity analysis (SA) is a methodology commonly used to identify important parameters that determine the behavior of the model. The SA of a model allows to determine how uncertainties in the model responses (outputs) can be assigned to the values of the model parameters (input variables). The related literature indicates that there are several methods to perform SA. This work addresses the benchmarking of four widely used methods for Global SA (GSA): Sobol-Jansen, Sobol-Baudin, Sobol-Owen and Sobol 2007, based on the concept of performance profile introduced by Dolan and Moré (2002) and the extension by Mahajan et al. (2012). For the previous methods, a set of 21 models and their variations were considered, which correspond to various applications in chemical engineering (such as heap leaching, water distribution network, milling, flotation circuit, among others). These comparisons show that the Sobol-Jansen method has the best performance profile because it is the first to perform GSA in 83% of the models considered. The four SA methods analyzed proved to be quite stable since they performed the SA in 100% of the models tested.

Keywords: Global sensitivity analysis, uncertainty, Sobol Method, parametric sensitivity, performance profile. .