Forest fire propagation prediction based on overalapping DDDAS forecasts

The effects of forest fires cause a widespread devastation throughout the world every year. A good prediction of fire behavior can help on coordination and management of human and material resources in the extinction of these emergencies. Given the high uncertainty of fire behavior and the difficulty of extracting information required to generate accurate predictions, one system able to adapt to fire dynamics considering the uncertainty of the data is necessary. In this work two different systems based on Dynamic Data Driven Application are applied and a new probabilistic method based on the combination of both approaches is presented. This new method uses the computational power provided by high performance computing systems to adapt the chances in these kind of dynamic environments.