  In practical applications, it is common that wildfire simulators do not
correctly predict the evolution of the fire scar. Usually, this is caused due
to multiple factors including inaccuracy in the input data such as land cover
classification, moisture, improperly represented local winds, cumulative errors
in the fire growth simulation model, high level of discontinuity/heterogeneity
within the landscape, among many others. Therefore in practice, it is necessary
to adjust the propagation of the fire to obtain better results, either to
support suppression activities or to improve the performance of the simulator
considering new default parameters for future events, best representing the
current fire spread growth phenomenon. In this article, we address this problem
through a new methodology using Derivative-Free Optimization (DFO) algorithms
for adjusting the Rate of Spread (ROS) factors in a fire simulation growth
model called Cell2Fire. To achieve this, we solve an error minimization
optimization problem that captures the difference between the simulated and
observed fire, which involves the evaluation of the simulator output in each
iteration as part of a DFO framework, allowing us to find the best possible
factors for each fuel present on the landscape. Numerical results for different
objective functions are shown and discussed, including a performance comparison
of alternative DFO algorithms.
