Landslide susceptibility mapping using ai-driven geospatial analysis, remote sensing and geophysical validation: A case study of the Manolo Fortich hydro power plant

This paper presents an AI-driven, multi-pass landslide susceptibility mapping approach that integrates AI-based slope stability analysis using Slide3 software, geophysical validation through electrical resistivity tomography (ERT) and standard penetration tests (SPT), and GIS-based spatial classification for comprehensive landslide risk assessment.

The results demonstrate that AI-based geotechnical analysis, coupled with geophysical validation, enhances prediction accuracy and reduces false positives. These findings highlight the importance of digital twin modeling in hydropower risk assessment and infrastructure resilience. The results underscore the value of combining AI and geophysical tools for robust geohazard assessment and infrastructure resilience in landslideprone regions.