Half-Duplex Dynamic Data Driven Application System for Forest Fire Spread Prediction

Every year forest fires provoke enormous looses from the ecological, economical and social point of view, by burning millions of hectares and killing several people. Nowadays, several forest fire simulators are used for helping in forest fire prevention and mitigation. Most of these simulators are based on Rothermel model. However, in most cases it is not possible to know the exact real-time values of model input parameters. This input data uncertainty causes predictions that are far from the real fire propagation. For this reason, we propose a fire propagation prediction system based on two stages: a calibration stage and a prediction stage. The calibration stage bases on the observed real fire evolution, so that the behavior of the fire is assimilated by the system at run-time matching the Dynamic Data Driven Application Systems (DDDAS) paradigm.