Coupling Diagnostic and Prognostic Models to a Dynamic Data Driven Forest Fire Spread Prediction System

Forest fires cause important losses around the world every year. A good prediction of fire propagation is a crucial point to minimize the devastating effects of these hazards. Several models that represent this phenomenon and provide a prediction of its spread have been developed. These models need input parameters which are usually difficult to know or even estimate. A twostage prediction methodology was proposed to improve the quality of these parameters. In this methodology, such parameters are calibrated according to real observations and then, used in the prediction step. However, there are several parameters, which are not uniform along the map, but vary according to the topography of the terrain. Besides, these parameters are not constant along time but they are strongly dynamic. In such cases, it is necessary to introduce complementary models that overcome both restrictions. In the former case, the need of a spatial distribution model of a given variable is needed to be able to provide a spatial distribution for a given variable along the whole terrain by starting from the measured values of that parameter in certain points of the terrain. In the case of time variability, a complementary model such as weather forecasting model, could enable the capability of dealing with dynamic behavior of these parameters along time. In this paper, we describe an enhanced two-stage prediction scheme, where both type of complementary models a wind field model and a weather prediction model are coupled to the prediction scheme by enabling the system to dynamically adapts to complex terrains and dynamic conditions. c © 2013 The Authors. Published by Elsevier B.V. Selection and/or peer-review under responsibility of the organizers of the 2013 International Conference on Computational Science.