Importance Sampling for Cost-Optimized Estimation of Burn Probability Maps in Wildfire Monte Carlo Simulations

Background: Wildfire modelers rely on Monte Carlo simulations of wildland fire to produce burn probability maps. These simulations are computationally expensive. Methods: We study the application of importance sampling to accelerate the estimation of burn probability maps, using L2 distance as the m...

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Hauptverfasser:	Valentin Waeselynck, David Saah
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	MDPI AG 2024-12-01
Schriftenreihe:	Fire
Schlagworte:	Monte Carlo simulations wildfire risk modeling burn probability variance reduction sampling error
Online-Zugang:	https://www.mdpi.com/2571-6255/7/12/455
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