Abstract
This paper employs GIS technology and multi-source data integration methods to assess landslide vulnerability in a city in the southwestern region. The study combines the Analytic Hierarchy Process (AHP) and machine learning techniques, utilizing field survey data and remote sensing information to perform a systematic quantitative analysis of the hazard and vulnerability of landslides. A comprehensive evaluation model was established, assessing the risk levels for populations, buildings, and infrastructure, thereby providing effective decision support for mitigating landslide risks.
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