Multifidelity prediction in wildfire spread simulation: Modeling, uncertainty quantification and sensitivity analysis
Meteorology and Climate Science
Environmental Modelling & Software
Wildfire behavior predictions typically suffer from significant uncertainty. However, wildfire modeling uncertainties remain largely unquantified in the literature, mainly due to computing constraints. New multifidelity techniques provide a promising opportunity to overcome these limitations. Therefore, this paper explores the applicability of multifidelity approaches to wildland fire spread prediction problems. Using a canonical simulation scenario, we assessed the performance of control variates Monte-Carlo (MC) and multilevel MC strategies, achieving speedups of up to 100x in comparison to a standard MC method. This improvement was leveraged to quantify aleatoric uncertainties and analyze the sensitivity of the fire rate of spread (RoS) to weather and fuel parameters using a full-physics fire model, namely the Wildland-Urban Interface Fire Dynamics Simulator (WFDS), at an affordable computation cost. The proposed methodology may also be used to analyze uncertainty in other relevant fire behavior metrics such as heat transfer, fuel consumption and smoke production indicators.
National Science Foundation
Forest fire, Multifidelity Monte Carlo, Predictive science & engineering, Sensitivity analysis, Uncertainty quantification, FDS
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Mario Miguel Valero, Lluís Jofre, and Ricardo Torres. "Multifidelity prediction in wildfire spread simulation: Modeling, uncertainty quantification and sensitivity analysis" Environmental Modelling & Software (2021). https://doi.org/10.1016/j.envsoft.2021.105050
Emergency and Disaster Management Commons, Fire Science and Firefighting Commons, Meteorology Commons
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