applied sciences, vol.12, pp.1-28, 2022 (SCI-Expanded)
Landslide susceptibility maps (LSMs) have been used frequently by researchers for many
years in prediction of the occurrence of landslides. Since many landslides have occurred there in
the past, Izmir, which is the third largest city of Türkiye, was selected for landslide susceptibility
assessment using geographical information systems (GIS) and remote sensing (RS) techniques. The
aim of this study is to create a better landslide susceptibility map (LSM) for the Izmir metropolitan
area and its surroundings by minimizing the shortcomings of some of the commonly used methods.
For this purpose, four different LSMs were prepared using the logistic regression (LR), analytical
hierarchy process (AHP), frequency ratio (FR) and index of entropy (IOE) methods with susceptibility
classes ranging from extremely low to extremely high. These four maps were then overlaid. The
highest susceptibility class was chosen for each pixel to form a combined landslide susceptibility map
(CLSM). The final CLSM is a thematic map presenting landslide susceptibility using five different
classes. The geo-environmental factors selected for use in this analysis were slope angle, slope aspect,
lithology, slope curvature, elevation, density of discontinuity, stream power index (SPI), land use
and distance from stream. Finally, the areas under receiver-operating characteristic (ROC) curves
were employed to compare the predictive capability of the five models used. Overall, the Combined
Method (CM) (AUC = 0.887) performed very well for landslide susceptibility assessment. Out of all
the models, the IOE model (AUC = 0.841) had a slightly lower predictive capability than the CM
model, and AHP (AUC = 0.816) was better than FR (AUC = 0.738) and LR (AUC = 0.727). It was
observed that, compared to rural areas, residential areas of Izmir city are particularly susceptible to
landslides.