This talk presents a prototype data-driven wildfire spread simulator capable of correcting inaccurate predictions of the fire front position and of subsequently providing an optimized forecast of the wildfire behavior. The potential of the prototype simulator is highlighted on a reduced-scale controlled grassland fire experiment.
The prototype simulator features:
● an Eulerian front-tracking solver that treats the fire as a propagating interface at regional scales
● a series of observations of the fire front position
● a data assimilation algorithm based on an Ensemble Kalman Filter (EnKF), which features a state estimation approach directly correcting the fire front position.
Best Student Paper Award
➞ Rochoux, M.C., Emery, C., Ricci, S., Cuenot, B., and Trouvé, A. (2014) Towards predictive simulation of wildfire spread at regional scale using ensemble-based data assimilation to correct the fire front position, in Fire Safety Science - Proceedings of the Eleventh International Symposium, International Association for Fire Safety Science.