Theory of Modeling and Simulation
Theory of Modeling and Simulation
Towards applications of particle filters in wildfire spread simulation
Proceedings of the 40th Conference on Winter Simulation
State estimation using particle filters in wildfire spread simulation
SpringSim '09 Proceedings of the 2009 Spring Simulation Multiconference
Parameter Estimation in TV Image Restoration Using Variational Distribution Approximation
IEEE Transactions on Image Processing
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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.