<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This paper first gives a brief overview of the Lagrangian forest fire propagation model [Ambroz, M.—Balažovjech, M.—Medl’a, M.—Mikula, K.: <italic>Numerical modeling of wildland surface fire propagation by evolving surface curves</italic>, Adv. Comput. Math. <bold>45</bold> (2019), no. 2, 1067–1103], which we apply to grass-field areas. Then, we aim to estimate the optimal model parameters. To achieve this goal, we use data assimilation of the measured data. From the data, we are able to estimate the normal velocity of the fire front (rate of spread), dominant wind direction and selected model parameters. In the data assimilation process, we use the Hausdorff distance as well as the Mean Hausdorff distance as a criterion. Moreover, we predict the fire propagation in small time intervals.</p></abstract>

This paper first gives a brief overview of the Lagrangian forest fire propagation model [Ambroz, M.—Balažovjech, M.—Medl’a, M.—Mikula, K.: Numerical modeling of wildland surface fire propagation by evolving surface curves, Adv. Comput. Math. 45 (2019), no. 2, 1067–1103], which we apply to grass-field areas. Then, we aim to estimate the optimal model parameters. To achieve this goal, we use data assimilation of the measured data. From the data, we are able to estimate the normal velocity of the fire front (rate of spread), dominant wind direction and selected model parameters. In the data assimilation process, we use the Hausdorff distance as well as the Mean Hausdorff distance as a criterion. Moreover, we predict the fire propagation in small time intervals.

Parameter Estimation for the Forest Fire Propagation Model

Department of Mathematics and Descriptive Geometry

Slovak University of Technology in Bratislava

Tatra Mountains Mathematical Publications

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Parameter Estimation for the Forest Fire Propagation Model"}

This paper first gives a brief overview of the Lagrangian forest fire propagation model [Ambroz, M.—Balažovjech, M.—Medl’a, M.—Mikula, K.: Numerical modeling...

Parameter Estimation for the Forest Fire Propagation Model

Published Online: Apr 24, 2020

Page range: 1 - 22

Received: Nov 23, 2019

DOI: https://doi.org/10.2478/tmmp-2020-0001

Keywords
data assimilation, parameter estimation, curve evolution, forest fire

© 2020 Martin Ambroz et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parameter Estimation for the Forest Fire Propagation Model

Published Online: Apr 24, 2020

Page range: 1 - 22

Received: Nov 23, 2019

DOI: https://doi.org/10.2478/tmmp-2020-0001

Keywordsdata assimilation, parameter estimation, curve evolution, forest fire

© 2020 Martin Ambroz et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
data assimilation, parameter estimation, curve evolution, forest fire