Spatial Partition-Based Particle Filtering for Data Assimilation in Wildfire Spread Simulation

Abstract

This article develops a spatial partition-based particle filtering framework to support data assimilation for large-scale wildfire spread simulation effectively. The developed spatial partition-based particle filtering framework breaks the system state and observation data into smaller spatial regions and then carries out localized particle filtering based on these spatial regions. Particle Filters (PFs) hold great promise to support data assimilation for spatial temporal simulations, such as wildfire spread simulation, to achieve more accurate simulation or prediction results. However, PFs face major challenges to work effectively for complex spatial temporal simulations due to the high-dimensional state space of the simulation models, which typically cover large areas and have a large number of spatially dependent state variables. The developed framework exploits the spatial locality property of system state and observation data and employs the divide-and-conquer principle to reduce state dimension and data complexity. This framework is especially developed for a discrete event cellular space model (the wildfire simulation model), which significantly differs from prior works that use numerical models specified by partial differential equations (PDEs) with continuous variables. Within this framework, a two-level automated spatial partitioning method is presented to provide automated and balanced spatial partitions with fewer boundary sensors. The developed framework is applied to a wildfire spread simulation and achieved improved results compared to using standard PF-based data assimilation methods.