Adaptive Modeling of Landslide Susceptibility Using Analytical Hierarchy Process and Multi-Objective Decision Optimization

Abstract

This study develops a detailed landslide susceptibility map for Kermanshah province, Iran, by analyzing field surveys, historical data, and remote sensing. Fifteen key factors-such as geomorphology, geology, climate, seismicity, and human activities-were identified and ranked using Analytical Hierarchy Process (AHP) and Multi-Objective Decision Optimization (MODO) within a GIS framework. The analysis classifies landslide risk into five categories: very high (18.4%), high (33.98%), moderate (24.19%), low (14.36%), and very low (9.07%). Pixel rate assessment confirmed the map's accuracy, showing that eastern and northeastern regions are particularly prone to landslides, with a substantial portion of the province at moderate to high risk. The study recommends using this map to guide targeted risk mitigation and land-use planning efforts to reduce landslide impacts on vulnerable areas. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.