论文标题
使用贝叶斯过滤在级别野火模型中朝着数据同化
Towards Data Assimilation in Level-Set Wildfire Models Using Bayesian Filtering
论文作者
论文摘要
水平集方法是基于特征的传播速率对火的演变进行建模的突出方法。但是，它没有提供吸收新数据和量化不确定性的直接手段。如果模型能够实时吸收数据，则火锋预测可以更准确和敏捷。此外，对火的位置和传播的不确定性估计对于决策至关重要。使用贝叶斯过滤方法，我们扩展了级别的方法，以允许数据同化和不确定性定量。我们在受控火灾中证明了这些方法。
The level-set method is a prominent approach to modelling the evolution of a fire over time based on a characterised rate of spread. It however does not provide a direct means for assimilating new data and quantifying uncertainty. Fire front predictions can be more accurate and agile if the models are able to assimilate data in real time. Furthermore, uncertainty estimation of the location and spread of the fire is critical for decision making. Using Bayesian filtering approaches, we extend the level-set method to allow for data assimilation and uncertainty quantification. We demonstrate these approaches on data from a controlled fire.
