Comparable analysis of analytical hierarchy process and fuzzy logic methods based on GIS and remote sensing for anticipating landslide susceptibility in the Iraqi Kurdistan Region, Koya district

Landslides pose a serious threat in urban areas with steep and varied terrain, such as the Koya district in northern Iraq. This study aims to produce landslide susceptibility maps (LSMs) using remote sensing data and three models: Analytical Hierarchy Process (AHP), Fuzzy Logic SUM, and Fuzzy Gamma 0.9. A total of 280 landslides were identified through field surveys, GPS, and Google Earth, and used for model training and validation. Fifteen conditioning factors were used, including slope, geology, land cover, rainfall, and drainage density. The resulting LSMs were classified into five susceptibility levels. The Fuzzy Gamma 0.9 model identified 9.15% of the area as highly susceptible, while the Fuzzy SUM model showed 20.79%. Validation using the Area Under the Curve (AUC) revealed that the Fuzzy Gamma 0.9 model (AUC = 0.80) performed better than AHP (0.711) and Fuzzy SUM (0.716). These results provide valuable insights for land use planning and hazard mitigation.

滑坡对地形陡峭且地貌多样的城区存在严重威胁，伊拉克北部的科伊（Koya）地区即为典型实例。本研究旨在利用遥感数据与三类模型生成滑坡敏感性图（Landslide Susceptibility Maps，下称LSMs）：层次分析法（Analytical Hierarchy Process，下称AHP）、模糊逻辑求和法（Fuzzy Logic SUM）以及模糊Gamma 0.9模型（Fuzzy Gamma 0.9）。本研究通过野外调查、全球定位系统（Global Positioning System，下称GPS）与谷歌地球（Google Earth）共识别出280处滑坡，用于模型训练与验证。研究共选取15类滑坡孕灾因子，包括坡度、地质条件、土地覆盖、降雨量与河网密度。所生成的滑坡敏感性图被划分为5个敏感性等级。模糊Gamma 0.9模型将9.15%的区域识别为高敏感区，而模糊逻辑求和法的识别比例为20.79%。采用受试者工作特征曲线下面积（Area Under the Curve，下称AUC）进行验证的结果显示，模糊Gamma 0.9模型（AUC=0.80）的表现优于层次分析法（0.711）与模糊逻辑求和法（0.716）。上述研究结果可为土地利用规划与防灾减灾工作提供极具价值的参考依据。