Fire is an extremely complex phenomenon and therefore fire spread prediction is not trivial. Spread prediction in grassland fires differs from the prediction in forest fires as the factors influencing each one are not the same. Moreover, input values for the algorithms differ depending on the factors they consider to have influence on fire behavior, even though the algorithms might be applicable to surfaces with similar characteristics. Selecting the most suitable algorithm in each particular case is not a simple task.
In this paper, we describe an easily extensible object oriented model for fire spread prediction in, potentially, any surface. This is of great importance since new mathematical algorithms can be added in order to predict fire spread in new surfaces. Additionally, we present an approach to derive unavailable data, required by the algorithms, from other available data. Finally, we provide a way to manage the selection of the “most suitable algorithm” from a collection of applicable algorithms in a particular type of surface, depending on the needs of the user (accuracy, execution time, etc.).
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