<![CDATA[Landslides represent a significant hazard worldwide, leading to substantial loss of life and property. In Pakistan’s Balakot Valley, severe landslides frequently disrupt key roadways, particularly along the Balakot-Naran route, which serves as a crucial artery for tourism—the region’s primary economic activity. This study aimed to create a high-resolution landslide susceptibility map for a 457 km² area along this road, where landslides are driven by factors such as intense rainfall, weak geological formations, seismic activity, and slope destabilization due to road construction. Using Geographic Information Systems (GIS) and remote sensing, we analyzed nine critical landslide-predictive factors: slope, aspect, lithology, land cover/land use, plan curvature, profile curvature, and proximity to faults, roads, and streams. Our methodology applied the frequency ratio (FR) model to 80% of the landslide inventory data for model construction, while 20% of the inventory was reserved for validation. The resulting susceptibility map, classified into low, moderate, high, and very high-risk zones using the natural breaks method in ArcGIS, indicates that slope gradient, lithology, land cover, and stream proximity are the primary contributors to landslide occurrence in this area. Model performance metrics demonstrate high predictive accuracy, with a success rate of 0.93 and a prediction rate of 0.96. The generated susceptibility map provides a robust tool for hazard management, offering valuable insights for targeted mitigation strategies and sustainable infrastructure development in this landslide-prone region. This study advanced landslide susceptibility mapping by integrating high-impact geospatial factors with an innovative, validated FR approach, supporting safer, data-driven land use planning and disaster preparedness.]]>
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