<![CDATA[This study aims to map landslide disaster risk in mountainous areas by integrating Geographic Information Systems (GIS) and the Analytic Hierarchy Process (AHP) method. Using a spatially-based quantitative-descriptive approach, the study evaluates seven key parameters: slope, lithology, rainfall, land use, elevation, population density, and road proximity. Data were analyzed through spatial layer overlay and validated against 50 historical landslide points. Results indicate that 34.5% of the area falls under high to very high-risk zones. ROC validation yielded an Area Under Curve (AUC) score of 0.82, indicating high model accuracy. The study also assesses community risk perception and adaptive capacity, revealing a notable discrepancy between subjective awareness and objective risk maps. Few respondents were aware of official risk maps, and community preparedness remained limited. The novelty of this research lies in its systematic integration of physical, social, and adaptive capacity aspects within a spatial framework. The findings recommend the development of participatory, GIS-based early warning systems aligned with local policy frameworks. This model shows strong potential for replication in other disaster-prone mountainous regions globally.]]>
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