Version 1
: Received: 10 October 2024 / Approved: 11 October 2024 / Online: 11 October 2024 (11:02:55 CEST)
How to cite:
Waeselynck, V.; Saah, D. Importance Sampling for Cost-Optimized Estimation of Burn-Probability Maps in Wildfire Monte Carlo Simulations. Preprints2024, 2024100879. https://doi.org/10.20944/preprints202410.0879.v1
Waeselynck, V.; Saah, D. Importance Sampling for Cost-Optimized Estimation of Burn-Probability Maps in Wildfire Monte Carlo Simulations. Preprints 2024, 2024100879. https://doi.org/10.20944/preprints202410.0879.v1
Waeselynck, V.; Saah, D. Importance Sampling for Cost-Optimized Estimation of Burn-Probability Maps in Wildfire Monte Carlo Simulations. Preprints2024, 2024100879. https://doi.org/10.20944/preprints202410.0879.v1
APA Style
Waeselynck, V., & Saah, D. (2024). Importance Sampling for Cost-Optimized Estimation of Burn-Probability Maps in Wildfire Monte Carlo Simulations. Preprints. https://doi.org/10.20944/preprints202410.0879.v1
Chicago/Turabian Style
Waeselynck, V. and David Saah. 2024 "Importance Sampling for Cost-Optimized Estimation of Burn-Probability Maps in Wildfire Monte Carlo Simulations" Preprints. https://doi.org/10.20944/preprints202410.0879.v1
Abstract
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 accelerating the estimation of burn probability maps, using L2 distance as the metric of deviation. Results: assuming a large area of interest, we prove that the optimal proposal distribution re-weights the probability of ignitions by the square root of expected burned area divided by expected computational cost, then generalize these results to assets-weighted L2 distance. We also propose a practical approach to searching for a good proposal distribution. Conclusion: these findings contribute quantitative methods for optimizing the precision/computation ratio of wildfire Monte Carlo simulations without biasing results, offer a principled conceptual framework for justifying and reasoning about other computational shortcuts, and can be readily generalized to a broader spectrum of simulation-based risk modeling.
Keywords
Monte Carlo simulations; wildfire; risk modeling; burn probability; variance reduction; sampling error
Subject
Environmental and Earth Sciences, Environmental Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
