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This talk demonstrates the capability of particle filters to combine measurements to model simulation in a stochastic framework, in order to formulate some feedback information on the wildfire behavior. This is illustrated based on a reduced-scale controlled grassland fire experiment.
Sampling Importance Re-sampling (SIR) and Auxiliary Sampling Importance Re-sampling (ASIR) filters were built on top of a level-set based front-tracking simulator in order to assimilate the time-evolving positions of the fire front and thereby correct the input environmental parameters of the fire spread model (i.e. vegetation properties, surface wind conditions).
Reference published in October 2014
➞ da Silva, W.B., Rochoux, M.C., Orlande, H., Colaço, M., Fudym, O., El Hafi, M., Cuenot, B., and Ricci, S. (2014) Application of particle filters to regional-scale wildfire spread, High Temperatures-High Pressures, International Journal of Thermophysical Properties Research, 43, 415-440.