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Wildfire Hazard Mapping Using Cellular Automata

机译:使用蜂窝自动机的野火危险映射

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Since fuel load is a major factor influencing wildfire risk, the standard approach to build related hazard maps is mainly grounded on land-cover data. However, the risk level is also influenced by other factors interacting nonlinearly, such as wind, fuel moisture, ignition sources and topography. For these reasons, an increasingly used approach for the computation of hazard maps involves the explicit simulation of the fire dynamics. This paper exploits a novel CA model for wildfire simulation to evaluate fire risk within a Monte Carlo approach. The adopted CA model has the ability to provide accurate burned areas, taking much less computing time than a typical vector approach for wildfire simulations. The improved accuracy and efficiency were obtained: (i) relaxing the restriction to a few pre-defined directions of spread, which characterizes most of the techniques for simulating wildfires on a raster space; (ii) using an adaptive time-step duration, which allows for avoiding unnecessary computation. The preliminary tests presented in this paper indicate that the model under study can be a suitable component of a tool for wildfire risk assessment.
机译:由于燃料负荷是影响野火风险的主要因素,因此构建相关危险地图的标准方法主要接地为陆地覆盖数据。然而,风险水平也受到非线性相互作用的其他因素的影响,例如风,燃料水分,点火源和地形。由于这些原因,越来越多地用于计算危险地图的方法涉及明确模拟消防动力学。本文利用野火模拟的新型CA模型来评估Monte Carlo方法中的火灾风险。采用的CA型号具有提供准确的燃烧区域,比典型的野火模拟的典型传染媒介方法取得更少的计算时间。获得了提高的精度和效率:(i)放松对少数预定分布方向的限制,这表征了用于在光栅空间上模拟野火的大多数技术; (ii)使用自适应时间阶跃持续时间,其允许避免不必要的计算。本文提出的初步测试表明,研究模型可以是野火风险评估工具的合适组成部分。

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