Wildland fire growth prediction method based on Multiple Overlapping Solution

Several Data-Driven Methods have been developed to try to solve the input parameters uncertainty when considering problems like Wildfires Prediction. In general, these methods operate over a large number of input parameters, and consider the most recent known behavior of wildfires. The purpose of the methods is to find the parameter set that best describes the real situation under consideration. Therefore, it is presumed that the same set of values could be used to predict the immediate future.However, because this kind of prediction is based on a single set of parameters, for those parameters that present a dynamic behavior (e.g. wind direction and speed), the new optimized values are not adequate to make a prediction.In this paper we propose an alternative method developed in a new branch of Data-Driven Prediction, which we called Multiple Overlapping Solution. This method combines statistical concepts and HPC (High Performance Computing) to obtain a higher quality prediction.