Towards parameter estimation in wildfire spread simulation based on sequential Monte Carlo methods

Simulation models rely on many parameters to model the structure and behavior of systems under study. To achieve accurate simulation results, there is a need to develop methods to dynamically estimate the correct set of model parameters for a given simulation scenario. In this paper, we present a method to dynamically estimate model parameters by assimilating real time data using Sequential Monte Carlo (SMC) methods. We formulate the problem of single and multiple parameter estimations based on the context of wildfire spread simulation. Preliminary results show that the developed method can be applied to parameter estimation in wildfire spread simulation to produce more accurate simulation results. The complexity and difficulties in multiple parameter estimation are discussed as well.