The study generally aimed at integrating geographic information system (GIS) and statistical models in predicting landslide prone areas in Wahig-Inabanga Watershed, Bohol, Philippines. Logistic regression (LR) and bivariate statistical analysis (BSA) were employed to determine landslide prone areas using eleven significant landslide-related instability factors such as elevation, slope, aspect, lithology, soil order, soil type, fault line proximity, river proximity, road proximity, rainfall and land cover. The satisfactory results of model evaluation justified the application of the LR model for landslide hazard assessment. Out of eleven instability factors, only soil order and soil type were determined not significant. The first three most important instability factors based on the values of regression coefficients are elevation, slope, and lithology. Landslide hazard assessment revealed around 7,063 ha or 11.33% of the total area of the watershed has high to very high landslide hazard ratings. The study showed that GIS, in tandem with useful models, provides pertinent results which could be used as scientific basis for watershed management and land use planning in relation to landslide disaster risk reduction and management.
Prof. Dr. Bilal BİLGİN