Determining map partitioning to minimize wind field uncertainty in forest fire propagation prediction

•Forest fire propagation prediction is a crucial issue to mitigate fire effects.•Wind is the parameter that most significantly affects fire propagation.•Wind field calculation is mandatory to provide accurate propagation prediction.•When maps are large, wind field calculation takes too long time.•A map partitioning methodology has been developed to reduce execution time.•The methodology has tested on real terrain maps with successful results.

Wind speed and direction are the parameters that most significantly affect forest fire propagation. The accurate estimation of such parameters is critical in providing precise predictions of forest fire propagation. Wind speed and direction can be measured in meteorological stations or estimated from meteorological models, but, in both cases, they are obtained at a very low resolution. Moreover, the meteorological wind is modified by the terrain topography and a complete wind field is generated with different wind speed and direction at any point of the terrain. So, wind field models providing wind speed and direction at a very high resolution (30 m) are crucial in forest fire propagation prediction. WindNinja is one of the most widely used wind field simulators in this area. However, when the terrain map under consideration is very large (30 km × 30 km or more), the execution time becomes unaffordable and the simulator cannot be used in real operation. A map partitioning strategy was developed to parallelize the wind field calculation and reduce the execution time to make it affordable. Map partitioning introduces a certain error in wind field that is propagated to forest fire propagation prediction. So, a complete methodology has been developed to determine the map partitioning that accomplishes real operation execution time constraints and keeps the forest fire propagation error below feasible limits. The experimental results show the execution time reduction accomplished and the accuracy of the wind field generated and forest fire propagation prediction.