# A Combined Method for Preparation of Landslide Susceptibility Map in Izmir (Turkije)
###### Abstract  
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 Turkiye, 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. l 17 July 20223 September  
which parts of the site are landslide susceptible; field work studies can be concentrated on these sites. Finally, the budget of the project will be affected positively, not only financially, but also in terms of time taken. Application of different models to different zones is plausible, since landslides in different parts of the world are likely to be affected by different factors. Also, since the behaviour and nature of landslides differ, geo-environmental factors selected in different methods may not correctly simulate the influencing mechanisms. Therefore, it may be necessary to examine applications in different regions, in order to question the performance of these methods. In the literature, there are many LSMs prepared by various researchers using different methods. In this regard, [PERSON] and [PERSON] [8] prepared a LSM to find a better location for a natural gas pipeline in Hendek (Turkiye). Authors used the statistical index (Wi) and weighting factor (Wf) methods while preparing LSMs with GIS. They found that Wf was the better method. Lithology was the most important factor in the analyses. [PERSON] [9] used LR and FR analysis in Penang and its surroundings, in Malaysia. The resulting LSM, created using the LR model, showed 7.5% higher accuracy than the FR model. [PERSON] et al. [10] used the FR model, AHP, the statistical index (Wi), weighting factor (Wf) and the LR model incorporating GIS and RS techniques to assess landslide susceptibility in Trabzon province (Turkiye). The results were verified using landslide inventory map. According the to results, the Wf method was the best predictive method. [PERSON] et al. [11] used fuzzy logic and AHP in the surroundings of Alborz folded zone in Iran. Their results showed that the FL model (89.70%) had a better prediction accuracy, at 8.60%, than the AHP model (81.10%). [PERSON] et al. [12] used the AHP, FR and LR models on the central Zab basin, Iran. The results revealed that the landslide inventory overlapped for highly and very highly susceptible areas in the AHP (69.41%), FR (73.93%) and LR (75.99%) maps.