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Abstract

Rapid estimates of the risk from potential wildfires are necessary for operational management and mitigation efforts. Computational models can provide risk metrics, but are typically deterministic and may neglect uncertainties inherent in factors driving the fire. Modeling these uncertainties can more accurately predict risks associated with a particular wildfire, but requires a large number of simulations with a corresponding increase in required computational time. Surrogate models provide a means to rapidly estimate the outcome of a particular model based on implicit uncertainties within the model and are very computationally efficient. In this paper, we detail the development of a surrogate model for the growth of a wildfire based on initial meteorological conditions: temperature, relative humidity, and wind speed. Multiple simulated fires under different conditions are used to develop the surrogate model based on the relationship between the area burnt by the fire and each meteorological variable. The results from nine bio-regions in Tasmania show that the surrogate model can closely represent the change in the size of a wildfire over time. The model could be used for a rapid initial estimate of likely fire risk for operational wildfire management.

Item Type:	Article
Authors/Creators:	KC, U and Aryal, J and Hilton, J and Garg, S
Keywords:	fire spread models, surrogate modeling, sensitivity analysis, global sensitivity analysis, wildfire, cloud computing
Journal or Publication Title:	Fire
Publisher:	MDPI
ISSN:	2571-6255
DOI / ID Number:	10.3390/fire4020020
Copyright Information:	Copyright 2021 by the authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/
Related URLs:	Google Scholar: Aryal, J UTAS Profile: Aryal, J Google Scholar: Garg, S UTAS Profile: Garg, S
Item Statistics:	View statistics for this item

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