Spatial prediction of landslide susceptibility in Taleghan basin, Iran

Abstract

Identifying landslide-susceptible zones is warranted to prevent and mitigate associated hazards in mountainous regions, where a landslide is a destructive type of erosion. A landslide susceptibility map was developed for the Taleghan basin based on frequency ratio (FR), logistic regression (LR), maximum entropy (MaxEnt), and support vector machine (SVM) with radial base (RBF), sigmoid (SIG), linear (LN), and polynomial (PL) kernel functions. To this end, an inventory map with 166 landslide locations was prepared and partitioned into 70% and 30% to train and validate the models, respectively. Subsequently, the models were designed based on 13 factors including elevation, slope degree, slope aspect, distance to stream, Stream Power Index, Topographic Wetness Index, Stream Transport Index, distance to fault, lithology, soil texture, land use, distance to road and precipitation. The performance of the methods was assessed using the area under the receiver operating characteristic curve, the Seed Cell Area Index (SCAI), the precision index (P). Moreover, statistical measures including sensitivity, specificity, and accuracy were calculated. Friedman test was also applied to confirm significant statistical differences among the seven models employed in this research. The validation results showed that MaxEnt had the maximum area under the curve (0.812). The results obtained using the models LR, FR, PL-SVM, SIG-SVM, LN-SVM, and RBF-SVM were 0.807, 0.732, 0.679, 0.663, 0.643 and 0.660, respectively. The obtained P index showed the better performance of MaxEnt and LR models. Moreover, the trend of changes in the SCAI values, from low- to high-susceptibility, indicated that the MaxEnt and LR models had the best performance. Decision makers can effectively use the findings of the present study to mitigate the financial and human costs resulting from the landslides.