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Global sensitivity analysis for uncertainty quantification in fire spread models

Author(s): Ujjwal KC, Jagannath Aryal, Saurabh Garg, J. E. Hilton
Year Published: 2021

Environmental models involve inherent uncertainties, the understanding of which is required for use by practitioners. One method of uncertainty quantification is global sensitivity analysis (GSA), which has been extensively used in environmental modeling. The suitability of GSA methods depends on the model, implementation, and computational complexity. Thus, we present a comparative analysis of different GSA methods (Morris, Sobol, FAST, and PAWN) applied to empirical fire spread models (Dry Eucalypt and Rothermel) and explain their implications. GSA methods such as PAWN, may not be able to explain all the interactions whereas methods such as Sobol can result in high computational costs for models with several parameters. We found that the Morris or the PAWN method should be prioritized over the Sobol and the FAST methods for a balanced trade-off between convergence and robustness under computational constraints. Additionally, the Sobol method should be chosen for more detailed sensitivity information.

Citation: KC, Ujjwal; Aryal, Jagannath; Garg, Saurabh; Hilton, James. 2021. Global sensitivity analysis for uncertainty quantification in fire spread models. Environmental Modelling & Software 143:105110. https://doi.org/10.1016/j.envsoft.2021.105110
Topic(s): Fire Behavior, Data Evaluation or Data Analysis for Fire Modeling, Simulation Modeling
Ecosystem(s): None
Document Type: Book or Chapter or Journal Article
Hot Topic(s): Fire Behavior Prediction
NRFSN number: 23718
FRAMES RCS number: 63833
Record updated: Oct 20, 2021