The survival of humanity is dependent on the survival of forests and the ecosystems they support, yet annually wildfires destroy millions of hectares of global forestry. Wildfires take place under specific conditions and in certain regions, which can be studied through appropriate techniques. A variety of statistical modeling methods have been assessed by researchers; however, ensemble modeling of wildfire susceptibility has not been undertaken. We hypothesize that ensemble modeling of wildfire susceptibility is better than a single modeling technique. This study models the occurrence of wildfire in the Brisbane Catchment of Australia, which is an annual event, using the index of entropy (IoE), evidential belief function (EBF), and logistic regression (LR) ensemble techniques. As a secondary goal of this research, the spatial distribution of the wildfire risk from different aspects such as urbanization and ecosystem was evaluated. The highest accuracy (88.51%) was achieved using the ensemble EBF and LR model. The outcomes of this study may be helpful to particular groups such as planners to avoid susceptible and risky regions in their planning; model builders to replace the traditional individual methods with ensemble algorithms; and geospatial users to enhance their knowledge of geographic information system (GIS) applications.

中文翻译：

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集合统计方法在林区大火概率和风险图空间预测中的应用

人类的生存取决于森林及其支持的生态系统的生存，然而每年的野火摧毁了数百万公顷的全球林业。野火在特定条件下和某些地区发生，可以通过适当的技术进行研究。研究人员评估了各种统计建模方法；但是，尚未进行野火敏感性的整体建模。我们假设野火敏感性的整体建模比单一建模技术更好。这项研究使用熵指数（IoE），证据信念函数（EBF）和逻辑回归（LR）集成技术对澳大利亚布里斯班流域的野火事件进行了建模，这是一年一度的事件。作为这项研究的次要目标，从城市化和生态系统等不同方面评估了野火风险的空间分布。使用集成的EBF和LR模型可获得最高的准确性（88.51％）。这项研究的结果可能对诸如计划者之类的特定群体有所帮助，以避免他们在计划中易受伤害和有风险的地区。模型构建者将整体算法替换为传统的个体方法；和地理空间用户，以增强他们对地理信息系统（GIS）应用程序的了解。模型构建者将整体算法替换为传统的个体方法；和地理空间用户，以增强他们对地理信息系统（GIS）应用程序的了解。模型构建者将整体算法替换为传统的个体方法；和地理空间用户，以增强他们对地理信息系统（GIS）应用程序的了解。