Data assimilation using sequential monte carlo methods in wildfire spread simulation

Autor:	Feng Gu, Haidong Xue, Xiaolin Hu
Rok vydání:	2012
Předmět:	Nonlinear system DEVS Data assimilation Application areas Computer science Modeling and Simulation Simulation modeling Sequential monte carlo methods Particle filter Algorithm Simulation Computer Science Applications
Zdroj:	ACM Transactions on Modeling and Computer Simulation. 22:1-25
ISSN:	1558-1195 1049-3301
DOI:	10.1145/2379810.2379816
Popis:	Assimilating real-time sensor data into large-scale spatial-temporal simulations, such as simulations of wildfires, is a promising technique for improving simulation results. This asks for advanced data assimilation methods that can work with the complex structures and nonlinear behaviors associated with the simulation models. This article presents a data assimilation framework using Sequential Monte Carlo (SMC) methods for wildfire spread simulations. The models and algorithms of the framework are described, and experimental results are provided. This work demonstrates the feasibility of applying SMC methods to data assimilation of wildfire spread simulations. The developed framework can potentially be generalized to other application areas where sophisticated simulation models are used.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::80bf85bf842da7da127726b9f7890898 https://doi.org/10.1145/2379810.2379816 Zobrazit plný text záznamu