Parameter Estimation of Fire Propagation Models Using Level Set Methods

TL;DR

This paper presents a novel method combining level set techniques and optimization to accurately estimate parameters in wildland fire propagation models using real and simulated data.

Contribution

It introduces a new approach for parameter estimation in fire models that integrates empirical spread rates with level set methods and optimization.

Findings

Effective parameter estimation demonstrated on real fire data.

Accurate fire front prediction in simulated scenarios.

Method improves fire spread modeling accuracy.

Abstract

The availability of wildland fire propagation models with parameters estimated in an accurate way starting from measurements of fire fronts is crucial to predict the evolution of fire and allocate resources for firefighting. Thus, we propose an approach to estimate the parameters of a wildland fire propagation model combining an empirical fire spread rate and level set methods to describe the evolution of the fire front over time and space. After validating the model, the estimation of parameters in the spread rate is performed by using fire front shapes measured at different time instants as well as wind velocity and direction, landscape elevation, and vegetation distribution. Parameter estimation is performed by solving an optimization problem, where the objective function to be minimized is the symmetric difference between predicted and measured fronts. Numerical results obtained by the application of the proposed method are reported in two simulated scenarios and in an application case study using real data of the 2002 Troy fire in Southern California, thus showing the effectiveness of the proposed approach.