Data assimilation techniques can be used to improve the estimation of the wildfire spread simulation by assimilating the real time data into the simulation models, such as sequential Monte Carlo (SMC) methods. The standard SMC methods employ the same number of particles and don't utilize the runtime state inference particle convergence characterization information during the execution of the algorithm. Therefore, a large number of particles are required to ensure that the particles can converge to the true posterior of the system state. For such large-scale spatial temporal systems as wildfire spread simulation, the system's state is large and its behaviors are heterogeneous in various areas. This heterogeneous and dynamic behavior makes the uncertainty of state inference dynamically change. In this paper, we design the adaptive particle filtering algorithm to dynamically adjust the number of particles based on the quantified uncertainty of state inference, thus to achieve more efficient simulation results.
['Feng Gu', 'Mohammad Butt', 'Chunyu Ai', 'Xiaoke Shen', 'Jiehao Xiao']
Adaptive particle filtering in data assimilation of wildfire spread simulation