期刊
ACM TRANSACTIONS ON MODELING AND COMPUTER SIMULATION
卷 22, 期 4, 页码 -出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2379810.2379816
关键词
Data assimilation; Sequential Monte Carlo methods; Wildfire; DEVS
资金
- National Science Foundation [CNS-0841170, CNS-0941432]
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [0841170] Funding Source: National Science Foundation
- Directorate For Geosciences
- Div Atmospheric & Geospace Sciences [0941432] Funding Source: National Science Foundation
- Div Atmospheric & Geospace Sciences
- Directorate For Geosciences [0941491] Funding Source: National Science Foundation
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据