惠珊, 芮小平, 李尧. 一种耦合元胞自动机的改进林火蔓延仿真算法[J]. 武汉大学学报 ( 信息科学版), 2016, 41(10): 1326-1332. DOI: 10.13203/j.whugis20140811
引用本文: 惠珊, 芮小平, 李尧. 一种耦合元胞自动机的改进林火蔓延仿真算法[J]. 武汉大学学报 ( 信息科学版), 2016, 41(10): 1326-1332. DOI: 10.13203/j.whugis20140811
HUI Shan, RUI Xiaoping, LI Yao. An Improved Forest Fire Spread Simulation Algorithm Coupled with Cellular Automata[J]. Geomatics and Information Science of Wuhan University, 2016, 41(10): 1326-1332. DOI: 10.13203/j.whugis20140811
Citation: HUI Shan, RUI Xiaoping, LI Yao. An Improved Forest Fire Spread Simulation Algorithm Coupled with Cellular Automata[J]. Geomatics and Information Science of Wuhan University, 2016, 41(10): 1326-1332. DOI: 10.13203/j.whugis20140811

一种耦合元胞自动机的改进林火蔓延仿真算法

An Improved Forest Fire Spread Simulation Algorithm Coupled with Cellular Automata

  • 摘要: 针对传统林火蔓延仿真模型在模拟大范围森林火灾时误差大和效率低的问题,对文献10的林火模型添加时间修正来提升林火蔓延模拟的准确性,提出耦合地理元胞自动机模拟林火蔓延过程的仿真算法。分析了元胞自动机时间步长对模拟精度的影响,优化时间步长选择,提高了模拟大规模森林火灾的精度及效率。以模拟2006年5月大兴安岭林区森林大火蔓延过程为例验证本算法,发现地理元胞自动机算法中时间步长取整个元胞完全燃烧所需时间的1/8效果最好,林火蔓延模拟结果与实际TM影像解译的火情时空一致性较高,Kappa系数平均为0.6352,准确率平均为87.89%。算法可用于实际林火蔓延过程的重现及趋势预测,且算法可逆。

     

    Abstract: Aiming to solve the problem of low efficiency and accuracy when simulating large spreading field forest fires using traditional forest fire spread simulation models, we constructed an improved model coupled with Cellular Automata to ensure=accurate timing of forest fire spread.. We evaluated the impact of time steps on the simulation accuracy to determine an optimal time step value that improves the accuracy and efficiency of large field forest fire simulations. A case study simulation of a spreading forest fire that occurred on Daxing'an Mountain in May 2006 showed that the optimal time step of the forest fire spread geography using a cellular automata simulation algorithm was 1/8 faster when cells were combusted completely. Compared with the real fire situation interpreted from TM image indicates that this model has a higher time and spatial consistency with an average Kappa coefficient of 0.6352, and the average accuracy was 87.89%. This algorithm can be used to simulate and predict forest fire spread in practical applications and the algorithm is reversible.

     

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