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

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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Bibliographic Details
Main Authors: Valentin Waeselynck, David Saah
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Fire
Subjects:
Monte Carlo simulations
wildfire
risk modeling
burn probability
variance reduction
sampling error
Online Access:https://www.mdpi.com/2571-6255/7/12/455

Internet

https://www.mdpi.com/2571-6255/7/12/455

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