GIS-Based Landslide Susceptibility Assessment and National Highway Vulnerability Analysis Using the Frequency Ratio Model: A Case Study of Lamjung District, Nepal Himalaya

Abstract

National highways in Lamjung district, Nepal, are frequently disrupted by landslides during the monsoon season, causing significant human casualties, infrastructure damage, and economic losses. This study developed a landslide susceptibility map for Lamjung district using the bivariate statistical Frequency Ratio (FR) method integrated with GIS and assessed the exposure of National Highways NH-03 and NH-25 to landslide hazards. A total of 264 landslides were identified through visual interpretation of high-resolution Google Earth Pro imagery (2015–2025), supported by field verification and Rapid Visual Assessment (RVA). Eleven geo-environmental factors were analyzed: slope, aspect, elevation, geology, LULC, NDVI, TWI, curvature, distance to road, distance to drainage, and rainfall. Factor importance was evaluated using the Prediction Rate (PR) index, with NDVI (PR = 14.03), geology (PR = 12.91), and rainfall (PR = 10.42) identified as the most influential factors. The model achieved strong predictive performance, with AUC values of 81.03% (training) and 80.76% (validation). Approximately 46% of the district lies in high to very high susceptibility zones, which account for 79.2% of the mapped landslides. Highway corridor analysis revealed that NH-25 is substantially more exposed than NH-03, with 84.7% of high and very high susceptibility points along NH-25. The Ghermu–Sattale section and bridges at Syange Khola and Sirung Khola were identified as critical hotspots. The resulting susceptibility map serves as a robust decision-support tool for landslide risk mitigation, infrastructure resilience planning, and sustainable land-use management in the Nepal Himalaya.