Department of Earth Sciences, Quaid-e-Azam University, Islamabad 45320, Pakistan.
Department of Civil Engineering, COMSATS University Islamabad, Wah Cantt 47040, Pakistan.
Sensors (Basel). 2022 Apr 19;22(9):3107. doi: 10.3390/s22093107.
This work evaluates the performance of three machine learning (ML) techniques, namely logistic regression (LGR), linear regression (LR), and support vector machines (SVM), and two multi-criteria decision-making (MCDM) techniques, namely analytical hierarchy process (AHP) and the technique for order of preference by similarity to ideal solution (TOPSIS), for mapping landslide susceptibility in the Chitral district, northern Pakistan. Moreover, we create landslide inventory maps from LANDSAT-8 satellite images through the change vector analysis (CVA) change detection method. The change detection yields more than 500 landslide spots. After some manual post-processing correction, the landslide inventory spots are randomly split into two sets with a 70/30 ratio for training and validating the performance of the ML techniques. Sixteen topographical, hydrological, and geological landslide-related factors of the study area are prepared as GIS layers. They are used to produce landslide susceptibility maps (LSMs) with weighted overlay techniques using different weights of landslide-related factors. The accuracy assessment shows that the ML techniques outperform the MCDM methods, while SVM yields the highest accuracy of 88% for the resulting LSM.
本研究评估了三种机器学习（ML）技术，即逻辑回归（LGR）、线性回归（LR）和支持向量机（SVM），以及两种多准则决策（MCDM）技术，即层次分析法（AHP）和逼近理想解的排序方法（TOPSIS），在巴基斯坦北部奇特拉尔地区进行滑坡易发性制图的性能。此外，我们通过变化向量分析（CVA）变化检测方法从 LANDSAT-8 卫星图像中创建滑坡目录图。变化检测产生了 500 多个滑坡点。经过一些手动后处理校正，滑坡目录点随机分为两组，比例为 70/30，用于训练和验证 ML 技术的性能。研究区域的 16 个地形、水文和地质滑坡相关因素被制备为 GIS 层。它们用于使用滑坡相关因素的不同权重生成基于加权叠加技术的滑坡易发性图（LSM）。精度评估表明，ML 技术优于 MCDM 方法，而 SVM 对生成的 LSM 的准确率最高，达到 88%。
