Spatial assessment and zoning of landslide risk in Zamkan watershed using support vector machine and  logistic regression

Esfandyari Darabad, Fariba; Rostami, Ghobad; Mostafazadeh, Raoof; Abedini, Mousa

doi:10.22034/hyd.2024.61467.1737

Document Type : پژوهشی

Authors

¹ Professor, Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran

² Ph.D student, Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran

³ Associate Professor, Department of Natural Resources and member of Water Management Research Center, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili

https://doi.org/10.22034/hyd.2024.61467.1737

Abstract

In the current study, the risk of landslides in the Zamkan Watershed, located in Kermanshah Province, was evaluated. Two machine learning models, Support Vector Machine (SVM), and Logistic Regression, were used to prepare a landslide susceptibility map. Toward this, 13 informational layers including elevation, slope, aspect, Melton ruggedness number, terrain convexity, stream length, valley depth, topographic wetness index, precipitation, geological formations, distance from rivers, distance from roads, and vegetation cover were utilized as independent variables. Approximately 70% of the watershed's landslide pixels were used for model training, and 30% for model validation. Model validation was performed using ROC curves. The results indicated the higher performance and accuracy of the radial basis function (RBF) kernel of the SVM model for generating landslide hazard maps in the study area. The area under the curve (AUC) for the RBF kernel was approximately 0.951 for model training and 0.944 for model testing. The results suggest that slope with a coefficient of 0.28, precipitation with a coefficient of 0.27, lithology with a coefficient of 0.26, and elevation with a coefficient of 0.22 are the main controlling factors for landslides occurrence in the Zamkan Watershed. Both the SVM model and logistic regression confirmed the deterministic effects of selected factors on landslides. About 35% of the study area as classified as highly susceptible to landslides, primarily in the eastern half of the watershed. Factors such as high elevation, steep slopes, heavy precipitation, and the Kazhdomi Formation's composition were identified as key contributors to this susceptibility.

Keywords

Main Subjects

Geomorphology

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Spatial assessment and zoning of landslide risk in Zamkan watershed using support vector machine and logistic regression

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Volume 11, Issue 40 - Serial Number 40October 2024Pages 123-102

Volume 11, Issue 40 - Serial Number 40
October 2024
Pages 123-102