Towards applications of particle filters in wildfire spread simulation

Wildfire propagation is a complex process influenced by many factors. Simulation models of wildfire spread, such as DEVS-FIRE, are important tools for studying fire behavior. This paper presents how the sequential Monte Carlo methods, i.e., particle filters, can work together with DEVS-FIRE for better simulation and prediction of wildfire. We define an application framework of particle filters for the problem of wildfire spread using the DEVSFIRE model, and discuss several applications. A case study example is provided and preliminary results are presented.