State estimation using particle filters in wildfire spread simulation

A fundamental issue in data assimilation of wildfire simulation is to estimate the dynamically changing states, e.g., the fire front position of wildfire, based on observation data of fire sensors. This is a challenging task because of the dynamic and non-linear behavior of fire spread. In this paper, we apply particles filters, also called sequential Monte Carlo methods, to data assimilation in wildfire simulation for estimating the dynamically evolving fire front of a spreading fire. The framework of applying particle filters to the DEVS-FIRE simulation model is presented. Preliminary experiment results show that the particle filtering algorithm was able to track the dynamically changing fire front based on fire sensor data, and thus to provide more accurate predictions of wildfire spread.

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