  Wildfires are a highly prevalent multi-causal environmental phenomenon. The
impact of this phenomenon includes human losses, environmental damage and high
economic costs. To mitigate these effects, several computer simulation systems
have been developed in order to predict fire behavior based on a set of input
parameters, also called a scenario (wind speed and direction; temperature;
etc.). However, the results of a simulation usually have a high degree of error
due to the uncertainty in the values of some variables, because they are not
known, or because their measurement may be imprecise, erroneous, or impossible
to perform in real time. Previous works have proposed the combination of
multiple results in order to reduce this uncertainty. State-of-the-art methods
are based on parallel optimization strategies that use a fitness function to
guide the search among all possible scenarios. Although these methods have
shown improvements in the quality of predictions, they have some limitations
related to the algorithms used for the selection of scenarios. To overcome
these limitations, in this work we propose to apply the Novelty Search
paradigm, which replaces the objective function by a measure of the novelty of
the solutions found, which allows the search to continuously generate solutions
with behaviors that differ from one another. This approach avoids local optima
and may be able to find useful solutions that would be difficult or impossible
to find by other algorithms. As with existing methods, this proposal may also
be adapted to other propagation models (floods, avalanches or landslides).
