Document Type : Original Research Paper

Authors

• Sarina Akbari ¹
• Reza Ghezelbash ²
• Hamidreza Ramazi ³
• Abbas Maghsoudi ⁴

¹ Faculty of Mining Engineering, Amirkabir University of Technology, Tehran, Iran

² Tehran University

³ Amirkabir University of Technology

⁴ Faculty of Mining and Metallurgical Engineering, Amirkabir University of Technology, Tehran, Iran

Abstract

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.

Keywords

• Analytical hierarchy process
• Frequency ratio
• Support vector machine

Main Subjects

• Risk Management