Forest fires are considered one of the most highly damaging and devastating of natural disasters, causing considerable casualties and financial losses every year. Hence, it is important to produce susceptibility maps for the management of forest fires so as to reduce their harmful effects. The purpose of this study is to map the susceptibility to forest fires over Nowshahr County in Iran, using an integrated approach of index of entropy (IOE) with fuzzy membership value (FMV), frequency ratio (FR), and information value (IV) with a comparison of their precision. The spatial database incorporated the inventory of forest fire and conditioning factors. As a whole, 41 forest fire locations were identified. Out of these, 29 locations (≈70%) were randomly chosen for the forest fire susceptibility modeling (FFSM), and the remaining 12 locations (≈30%) were utilized for the validation of the models. Subsequently, utilizing FMV‐IOE, FR‐IOE, and IV‐IOE models, forest fire susceptibility maps were acquired. Finally, the modeling ability of the models for FFSM was assessed using an area under the receiver operating characteristic (AUROC) curve. The results manifested that the prediction accuracy of the FMV‐IOE model is slightly higher than that of the FR‐IOE and IV‐IOE models. The incorporation of IOE with FMV, FR, and IV models had AUROC values of 0.890, 0.887, and 0.878, respectively. The resulting FFSM can be effective in fire repression resource planning, sustainable development, and primary warning in regions with similar conditions.

中文翻译：

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使用混合方法的森林火灾敏感性建模

森林火灾被认为是自然灾害造成的最严重破坏和毁灭性破坏，每年造成大量人员伤亡和经济损失。因此，重要的是要为森林大火的管理绘制敏感性图，以减少其危害。这项研究的目的是使用熵指数（IOE），模糊隶属度值（FMV），频率比（FR）和信息值（IV）的综合方法，绘制伊朗诺沙尔县森林火灾的易感性图比较它们的精度。空间数据库纳入了森林火灾和调节因素清单。总体而言，确定了41个森林火灾地点。其中，随机选择了29个地点（≈70％）进行森林火灾敏感性建模（FFSM），其余12个位置（≈30％）用于模型验证。随后，利用FMV-IOE，FR-IOE和IV-IOE模型，获取了森林火灾敏感性图。最后，使用接收器工作特性（AUROC）曲线下方的面积评估FFSM模型的建模能力。结果表明，FMV-IOE模型的预测精度略高于FR-IOE和IV-IOE模型。将IOE与FMV，FR和IV模型结合使用时，AUROC值分别为0.890、0.887和0.878。由此产生的FFSM可以有效地用于条件相似地区的灭火资源规划，可持续发展和主要预警。获取森林火灾敏感性图。最后，使用接收器工作特性（AUROC）曲线下方的面积评估FFSM模型的建模能力。结果表明，FMV-IOE模型的预测精度略高于FR-IOE和IV-IOE模型。将IOE与FMV，FR和IV模型结合使用时，AUROC值分别为0.890、0.887和0.878。由此产生的FFSM可以有效地用于条件相似地区的灭火资源规划，可持续发展和主要预警。获取森林火灾敏感性图。最后，使用接收器工作特性（AUROC）曲线下方的面积评估FFSM模型的建模能力。结果表明，FMV-IOE模型的预测精度略高于FR-IOE和IV-IOE模型。将IOE与FMV，FR和IV模型结合使用时，AUROC值分别为0.890、0.887和0.878。由此产生的FFSM可以有效地用于条件相似地区的灭火资源规划，可持续发展和主要预警。结果表明，FMV-IOE模型的预测精度略高于FR-IOE和IV-IOE模型。将IOE与FMV，FR和IV模型结合使用时，AUROC值分别为0.890、0.887和0.878。由此产生的FFSM可以有效地用于条件相似地区的灭火资源规划，可持续发展和主要预警。结果表明，FMV-IOE模型的预测精度略高于FR-IOE和IV-IOE模型。将IOE与FMV，FR和IV模型结合使用时，AUROC值分别为0.890、0.887和0.878。由此产生的FFSM可以有效地用于条件相似地区的灭火资源规划，可持续发展和主要预警。