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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., 8 figures, 35 pages

Cell2Fire is a new cell-based forest and wildland landscape fire growth simulator that is open-source and exploits parallelism to support the modelling of fire growth cross large spatial and temporal scales in a timely manner. The fire environment is characterized by partitioning the landscape into a large number of cells each of which has specified fuel, weather, fuel moisture and topography attributes. Fire spread within each cell is assumed to be elliptical and governed by spread rates predicted by a fire spread model such as the Canadian Forest Fire Behavior Prediction (FBP) System. The simulator includes powerful statistical and graphical output and spatial analysis features to facilitate the display and analysis of projected fire growth. We validated Cell2Fire by using it to predict the growth of real and realistic hypothetical fires, comparing our fire growth predictions with those produced by the state-of-the-art Prometheus fire growth simulator. Cell2Fire is structured to facilitate its use for predicting the growth of individual fires or embedding it in landscape management simulation models. It can be used to produce probabilistic fire scar predictions by allowing for uncertainty concerning the basic spread rate predictions and uncertain weather scenarios that might drive their growth.