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Akbari, Sarina, Ghezelbash, Reza, Ramazi, Hamidreza, Maghsoudi, Abbas. (1404). Landslide Susceptibility Mapping: Comparison of Knowledge-driven AHP-FR and Data-driven SVM Methods in Zarab-Sanandaj Sub-basins, Kurdistan, Iran. , 17(1), 261-282. doi: 10.22044/jme.2025.15122.2890
Sarina Akbari; Reza Ghezelbash; Hamidreza Ramazi; Abbas Maghsoudi. "Landslide Susceptibility Mapping: Comparison of Knowledge-driven AHP-FR and Data-driven SVM Methods in Zarab-Sanandaj Sub-basins, Kurdistan, Iran". , 17, 1, 1404, 261-282. doi: 10.22044/jme.2025.15122.2890
Akbari, Sarina, Ghezelbash, Reza, Ramazi, Hamidreza, Maghsoudi, Abbas. (1404). 'Landslide Susceptibility Mapping: Comparison of Knowledge-driven AHP-FR and Data-driven SVM Methods in Zarab-Sanandaj Sub-basins, Kurdistan, Iran', , 17(1), pp. 261-282. doi: 10.22044/jme.2025.15122.2890
Akbari, Sarina, Ghezelbash, Reza, Ramazi, Hamidreza, Maghsoudi, Abbas. Landslide Susceptibility Mapping: Comparison of Knowledge-driven AHP-FR and Data-driven SVM Methods in Zarab-Sanandaj Sub-basins, Kurdistan, Iran. , 1404; 17(1): 261-282. doi: 10.22044/jme.2025.15122.2890

Landslide Susceptibility Mapping: Comparison of Knowledge-driven AHP-FR and Data-driven SVM Methods in Zarab-Sanandaj Sub-basins, Kurdistan, Iran

Journal of Mining and Environment
مقاله 13، دوره 17، شماره 1، فروردین و اردیبهشت 2026، صفحه 261-282    اصل مقاله (7.95 M)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22044/jme.2025.15122.2890
نویسندگان
Sarina Akbari1؛ Reza Ghezelbash* 2؛ Hamidreza Ramazi1؛ Abbas Maghsoudi1
1Faculty of Mining Engineering, Amirkabir University of Technology, Tehran, Iran
2College of Engineering, University of Tehran, Tehran, Iran
چکیده
Natural hazards, particularly landslides, have long posed significant threats to people, buildings, and the surrounding environment. Therefore, comprehensive planning for urban and rural development necessitates the development and implementation of landslide risk zoning models. Numerous methodologies have been proposed for generating landslide hazard maps, which can potentially aid in predicting future landslide-prone areas. This study employed an integrated approach that combines statistical and multi-criteria decision-making (MCDM) methodologies. The Frequency Ratio (FR) and Analytical Hierarchy Process (AHP) were utilized as knowledge-driven approaches, while the Support Vector Machine (SVM) using an RBF kernel, a widely recognized machine learning algorithm, was applied as a data-driven method. Ten factors influencing landslides were considered, including slope angle, aspect, altitude, geology, land use, climate, erosion, and distances from rivers, faults, and roads. The results revealed that landslides are more predictable in the southern, southwestern, and central regions of the studied area. A quantitative assessment of the different methods using prediction-rate curves indicated that the SVM method outperformed the FR and AHP-FR approaches in identifying susceptible areas. The findings of this work could be effectively employed to mitigate potential future hazards and associated damages.
کلیدواژه‌ها
Analytical hierarchy process؛ Frequency ratio؛ Support vector machine
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