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- W4224303230 endingPage "3107" @default.
- W4224303230 startingPage "3107" @default.
- W4224303230 abstract "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." @default.
- W4224303230 created "2022-04-26" @default.
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- W4224303230 date "2022-04-19" @default.
- W4224303230 modified "2023-10-03" @default.
- W4224303230 title "Evaluation of Different Landslide Susceptibility Models for a Local Scale in the Chitral District, Northern Pakistan" @default.
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- W4224303230 doi "https://doi.org/10.3390/s22093107" @default.
- W4224303230 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35590797" @default.
- W4224303230 hasPublicationYear "2022" @default.
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