Landslide Susceptibility Mapping of BLISTT (Baguio, La Trinidad, Itogon, Sablan, Tuba, Tublay), Benguet Using GIS-Based Binary Logistic Regression Modeling

Abstract. The BLISTT (Baguio City, La Trinidad, Itogon, Sablan, Tuba, Tublay) area in Benguet, Philippines is highly prone to landslides driven by various factors including mountainous terrain, intense rainfall, seismic activity, and human-induced activities. Thus, there is a need to implement reliable landslide monitoring and mitigation approaches in the region. This study uses geographic information systems (GIS) together with remote sensing-based binary logistic regression to analyze and map landslide susceptibility across the BLISTT area. Twelve conditioning factors were considered, namely slope angle, slope aspect, profile curvature, lithology, soil type, land cover, Topographic Wetness Index (TWI), Normalized Difference Vegetation Index (NDVI), distances to roads, rivers, and lineaments, and precipitation. The resulting model demonstrates strong performance, obtaining an ROC value of 0.809. Analysis of these factors reveals the significant influence of geophysical, environmental, and anthropogenic variables on landslide susceptibility, particularly highlighting soil type, land cover, lithology, rainfall, and topographic parameters. Highly susceptible areas are identified in Tublay, Sablan, Tuba, and mountainous regions characterized by steep slopes. Conversely, urbanized areas like Baguio City exhibit lower susceptibility levels due to favorable topographic conditions.