Assessment of landslide susceptibility zonation using frequency ratio and statistical index: a case study of Al-Fawar basin, Tartous, Syria

Abstract

Landslide is one of the most morpho-dynamic patterns of slopes in the coastal region of Syria. The assessment of landslide susceptibility is a complex procedure because of limited data and lack of related literature in Syria. Therefore, the current paper explored the landslide susceptibility in Al-Fawar basin, based on remote sensing data in the GIS environment. Inventorization of the current landslide was carried out as points utilizing satellite images, fieldwork, and photographs. Thirteen dominant factors of landslide including slope angle, slope aspect, curvature, plan curvature, profile curvature, elevation, proximity to faults, proximity to streams, proximity to roads, NDVI, rainfall, lithology, and TWI were utilized for landslide susceptibility mapping. In this study, two bivariate statistical models, i.e., frequency ratio (FR) and statistical index (SI), were used for spatial calibration between the resulting inventory map and the geofactors. The quality of models performance in mapping the landslide susceptibility was assessed utilizing receiver operating characteristic (ROC) curves. The prediction accuracy curves showed that the landslide susceptibility map using FR model has the greatest prediction accuracy (AUC = 0.824) compared with SI model that showed 0.801 of AUC. Identically, the success rate curve graph showed that the AUC for FR and SI models was 0.799 and 0.778, respectively. Although the susceptibility assessment utilizing the FR model produced higher accurate output, the validation results indicate that the two models used in this study can be encouraging approaches for the assessment of landslide susceptibility hazard in the Syrian coastal basin, thus fruitfully creating spatial mitigation and conservation measures.