Comparison of global sensitivity analysis methods for a fire spread model with a segmented characteristic

TL;DR

This study compares four global sensitivity analysis methods applied to a segmented fire spread model, highlighting how segmented characteristics influence importance rankings and guiding analysts on method selection during transition regions.

Contribution

The paper evaluates four GSA indices on a segmented fire spread model, revealing their differing sensitivities and providing guidance for selecting appropriate methods in practical applications.

Findings

Different GSA indices produce varying importance rankings in the transition region.

Segmented model characteristics affect GSA indices differently.

Guidance on selecting GSA methods based on practical purpose.

Abstract

Global sensitivity analysis (GSA) can provide rich information for controlling output uncertainty. In practical applications, segmented models are commonly used to describe an abrupt model change. For segmented models, the complicated uncertainty propagation during the transition region may lead to different importance rankings of different GSA methods. If an unsuitable GSA method is applied, misleading results will be obtained, resulting in suboptimal or even wrong decisions. In this paper, four GSA indices, i.e., Sobol index, mutual information, delta index and PAWN index, are applied for a segmented fire spread model (Dry Eucalypt). The results show that four GSA indices give different importance rankings during the transition region since segmented characteristics affect different GSA indices in different ways. We suggest that analysts should rely on the results of different GSA indices according to their practical purpose, especially when making decisions for segmented models during the transition region. All of our source codes are publicly available at https://github.com/dirge1/GSA_segmented.