Landslide susceptibility mapping (LSM) is making increasing use of GIS-based spatial analysis in combination with multi-criteria evaluation (MCE) methods. We have developed a new multi-criteria decision analysis (MCDA) method for LSM and applied it to the Izeh River basin in south-western Iran. Our method is based on fuzzy membership functions (FMFs) derived from GIS analysis. It makes use of nine causal landslide factors identified by local landslide experts. Fuzzy set theory was first integrated with an analytical hierarchy process (AHP) in order to use pairwise comparisons to compare LSM criteria for ranking purposes. FMFs were then applied in order to determine the criteria weights to be used in the development of a landslide susceptibility map. Finally, a landslide inventory database was used to validate the LSM map by comparing it with known landslides within the study area. Results indicated that the integration of fuzzy set theory with AHP produced significantly improved accuracies and a high level of reliability in the resulting landslide susceptibility map. Approximately 53% of known landslides within our study area fell within zones classified as having “very high susceptibility”, with the further 31% falling into zones classified as having “high susceptibility”.

中文翻译：

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基于GIS的滑坡敏感性映射的扩展模糊多标准评估

滑坡敏感性制图 (LSM) 越来越多地使用基于 GIS 的空间分析与多标准评估 (MCE) 方法相结合。我们为 LSM 开发了一种新的多标准决策分析 (MCDA) 方法，并将其应用于伊朗西南部的伊泽河流域。我们的方法基于从 GIS 分析得出的模糊隶属函数 (FMF)。它利用了当地滑坡专家确定的九个滑坡原因。模糊集理论首先与层次分析法 (AHP) 相结合，以便使用成对比较来比较 LSM 标准以进行排名。然后应用 FMF 以确定用于开发滑坡敏感性图的标准权重。最后，滑坡清单数据库用于通过将 LSM 地图与研究区域内的已知滑坡进行比较来验证 LSM 地图。结果表明，模糊集理论与层次分析法的结合显着提高了生成的滑坡敏感性图的准确性和高度的可靠性。我们研究区域内大约 53% 的已知滑坡属于“非常高敏感性”的区域，另外 31% 属于“高敏感性”的区域。