This is the source code of the paper "Comparison of global sensitivity analysis methods for a fire spread model with a segmented characteristic". In this work, Sobol index, mutual information, delta index and PAWN index are compared.
If you find the code useful, please give a star :)
Chen, Shi-Shun and Xiao-Yang Li. Comparison of global sensitivity analysis methods for a fire spread model with a segmented characteristic. Mathematics and Computers in Simulation, 2025, 229: 304-318.
Paper link: https://www.sciencedirect.com/science/article/pii/S0378475424004014
PDF: https://dirge1.github.io/shishun_chen.github.io/papers/Chen2025_MATCOM_GSA.pdf
• The effect of segmented characteristics on GSA is explored by a fire spread model.
• Four GSA methods give different importance rankings during the transition region.
• The Sobol index yields a radical importance ranking.
• Analysts should choose GSA methods carefully according to their practical purpose.
Fig. 1. GSA results under different μU: Sobol index; Mutual information; Delta index; PAWN index.
If you want to get the result of Fig.1 directly by our provided results, you can run Fig1_picture_varyU_withbar.m in run_main.
If you want to get the result of Fig.1 on your own computer, you can run main_fire.m in run_main. It should be noted that python environment must be configured in Matlab.
You can configure the python environment in Matlab by following the video released by the author (in Chinese): https://www.bilibili.com/video/BV1xp421Z7Ua/?vd_source=a1dbb3ff5999b954fed0a1a5c93cd04b.
We use the python package ennemi, chaospy (not used but needed) and SALib. The details and references are provided in the paper.