Abstract
This paper shows how a gradient-free optimization method is used to improve the prediction capabilities of wildfire progression by estimating the wind conditions driving a FARSITE wildfire model. To characterize the performance of the prediction of the perimeter as a function of the wind conditions, an uncertainty weighting is applied to each vertex of the measured fire perimeter and a weighted least-squares error is computed between the predicted and measured fire perimeter. In addition, interpolation of the measured fire perimeter and its uncertainty is adopted to match the number of vertices on the predicted and measured fire perimeter. The gradient-free optimization based on iterative refined gridding provides robustness to intermittent erroneous results produced by FARSITE and quickly find optimal wind conditions by paralleling the wildfire model calculations. Results on wind condition estimation are illustrated on two historical wildfire events: the 2019 Maria fire that burned south of the community of Santa Paula in the area of Somis, CA, and the 2019 Cave fire that started in the Santa Ynez Mountains of Santa Barbara County.
Work is supported by WIFIRE Commons and funded by NSF 2040676 under the Convergence Accelerator program.
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Tan, L., de Callafon, R.A., Block, J., Crawl, D., Altıntaş, I. (2021). Improving Wildfire Simulations by Estimation of Wildfire Wind Conditions from Fire Perimeter Measurements. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12746. Springer, Cham. https://doi.org/10.1007/978-3-030-77977-1_18
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