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Data Assimilation Using Sequential Monte Carlo Methods in Wildfire Spread Simulation

机译:在野火扩散模拟中使用顺序蒙特卡罗方法进行数据同化

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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.
机译:将实时传感器数据纳入大规模的时空模拟(例如野火模拟)是一种改善模拟结果的有前途的技术。这就要求可以使用复杂的结构和与仿真模型关联的非线性行为的高级数据同化方法。本文介绍了使用序列蒙特卡洛(SMC)方法进行野火蔓延模拟的数据同化框架。描述了该框架的模型和算法,并提供了实验结果。这项工作证明了将SMC方法应用于野火蔓延模拟的数据同化的可行性。所开发的框架可以潜在地推广到使用复杂仿真模型的其他应用领域。

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