This function offers the user the possibility to perturb inputs to Rothermel's (1972) fire behavior model and propagate the uncertainty to the resulting estimate of Rate of spread [m/min] by means of Monte Carlo iterative sampling. Random values are extracted from Gaussian distributions with mean = observed values, and spread defined by a custom ratio of standard deviation to the mean defined by the user.
Genetic algorithms (GA) are a technique of machine-based mathematical optimization. The algorithm searches, within user-defined ranges, for values that minimize or maximize a target function. Here, fuel model parameters are searched that minimize root mean square error (RMSE) of forward fire rate of spread predicted by Rothermel (1972) model against observed data. Depends on package "GA" (Scrucca 2013) for the execution of the genetic algorithm; refer to this publication for a full explanation of GA parameters and settings.
● Data Source:
● Keywords: model
● Alias: gaRoth
The function preloads the 13 fire behavior fuel models by Albini (1976) and the 40 fuel models by Scott & Burgan (2005), computes rate of spread using Rothermel's (1972) model for a vector or data frame of fire experiment data, and computes root mean square error and mean bias of each fuel model to observed rate of spread.
R build of Rothermel's (1972) model for surface head fire rate of spread with some additional utilities (best standard fuel model selection, uncertainty propagation, optimization of fuel models by genetic algorithms) and sample datasets.