Landslide susceptibility mapping using geospatial, analytical hierarchy process (AHP), and binary logistic regression (BLR) techniques – A study of Wadi Habban Basin, Shabwah, Yemen

Results in Earth Sciences Pub Date : 2025-05-21 DOI:10.1016/j.rines.2025.100103

Haial Al-kordi , Abdulmohsen Al-Amri , Govinda raju

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Abstract

Wadi Habban Basin is continuously prone to numerous geological hazards, such as landslides. This study aims to generate landslide susceptibility maps by integrating of remote sensing, Geographic-Information-System (GIS) techniques, Analytical-Hierarchy-Process (AHP), and Binary -Logistic-Regression (BLR) models. In this study, multiple datasets were utilized for delineating landslide susceptibility maps, including slope, elevation, lithology, aspect, proximity to faults, proximity to drainage, proximity to roads, proximity to lineaments, geomorphology, soil texture, rainfall, land use/land cover, Normalized Difference Vegetation Index, curvature, and stream power index. Spatially distributed maps and thematic layers for all the aforementioned parameters were generated using a combination of remote sensing data and ground-based observations within a GIS environment. A comparative analysis of the AHP and BLR models was conducted to evaluate their predictive capability. The BLR model classified 49 % of the area as high to very high susceptibility, compared to 26 % by AHP, and showed a stronger delineation of low-risk zones. ROC curve analysis indicated high predictive accuracy for BLR than AHP models, with AUC values of 90.4 % for BLR and 81.7 % for AHP. Validation using confusion matrices demonstrated that the (BLR) model achieved an overall accuracy of 91.5 %, with a precision of 93 % and a recall of 91 %. In comparison, the (AHP) model yielded an overall accuracy of 75 %, a precision of 87.5 %, and a recall of 75 %.Results confirm the robustness of the BLR model for effective landslide susceptibility mapping and highlight its potential for risk informed planning. This study provides valuable insights for disaster risk reduction, sustainable land-use management, and the application of targeted mitigation strategies in landslide-prone regions.

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利用地理空间、层次分析法（AHP）和二元逻辑回归（BLR）技术绘制滑坡易感性图——对也门沙布瓦瓦的瓦迪哈班盆地的研究

瓦底哈班盆地一直是滑坡等多种地质灾害频发的地区。本研究旨在整合遥感、地理信息系统（GIS）技术、层次分析法（AHP）和二元logistic回归（BLR）模型，生成滑坡易感性图。在这项研究中，利用多个数据集来描绘滑坡易感性图，包括坡度、高程、岩性、坡向、断层邻近、排水邻近、道路邻近、地形邻近、地貌、土壤质地、降雨、土地利用/土地覆盖、归一化植被指数、曲率和溪流功率指数。所有上述参数的空间分布地图和专题层都是在地理信息系统环境中利用遥感数据和地面观测相结合而生成的。对比分析AHP和BLR模型的预测能力。BLR模型将49% %的区域划分为高至高易感性区域，而AHP模型将其划分为26% %，并显示出更强的低风险区划分。ROC曲线分析表明，BLR模型的预测准确率高于AHP模型，BLR模型的AUC值为90.4 %，AHP模型的AUC值为81.7 %。使用混淆矩阵验证表明（BLR）模型的总体准确率为91.5 %，精密度为93 %，召回率为91 %。相比之下，（AHP）模型的总体准确度为75 %，精确度为87.5 %，召回率为75 %。结果证实了BLR模型在有效的滑坡易感性制图方面的稳健性，并强调了其在风险知情规划方面的潜力。这项研究为减少灾害风险、可持续土地利用管理以及在滑坡易发地区应用有针对性的减灾战略提供了有价值的见解。

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Results in Earth Sciences

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