# A Combined Method for Preparation of Landslide Susceptibility Map in Izmir (Turkije)
###### Abstract
According to their findings, it was concluded that the LR model gave better results. [PERSON] et al. [13] compared the three techniques--namely, functional trees (FT), multilayer perceptron neural networks (MLP neural nets), and naive Bayes (NB)--for determining landslide susceptibility in the Uttarakhand region (India). The predictive capability of the factors they used on landslide models was assessed by a linear support vector machine (LSVM) algorithm. The authors concluded that the models they used worked very well for determining landslide-susceptible zones. [PERSON] and [PERSON] [14] used landslide nominal risk factor (LNRF), LR and AHP at the Nojian watershed in Lorestan province, Iran. According to the results, the AHP and LR models showed higher accuracy than the LNRF model. [PERSON] et al. [15] used the AHP and LR models in China. The susceptibility map created with FR provided slightly better results than that created with AHP. The researchers stated that the LR method was the best model and could be accepted as the best model for similar situations. [PERSON] et al. [16] reviewed landslide susceptibility studies from 1983 to 2016. The authors emphasized that most recent studies evaluated the model performance and prediction performance, and only a few considered the issue of model uncertainty. They provided suggestions for the generation of landslide susceptibility models with the help of their experience in the field, literature reviews, information analysis using literature database. [PERSON] [17] prepared a combined model for landslide susceptibility in the northwest of Iran. Landslide hazard potential and resource damage potential layers were combined. [PERSON] et al. [18] conducted a post-wildfire landslide hazard assessment. The analysis was conducted using a heuristic or expert-based approach with GIS. Finally, they mapped all collected data through an overlay process which could be useful for civil protection warning systems. [PERSON] [19] used the FR and IOE models in Turkiye. As a result, according to the researchers, both methods showed successful results, but the susceptibility map created with the FR yield slightly better results. [PERSON] et al. [20] proposed a new probabilistic technique to conduct a risk assessment for landslides triggered by earthquakes. The authors concluded that the approach could improve slope stability studies. [PERSON] et al. [21] used AHP, FR, LR and their ensemble methods in India. According to the results of the study, the AHP model showed 78.86% accuracy, FR model 80.22%,LR model 80.67%, AHP-FR 83.44%, AHP-LR 84.39% and FR-LR 84.73% accuracy. [PERSON] et al. [22] used logistic regression and the random forest model in China. Although the authors stated that both models provided reasonable results, the random forest model had better stability and predictive capability. [PERSON] et al. [23] used the FR and IOE models in northern Jiangxi Province (China). The researchers concluded that the FR method reached 92.3% accuracy and the IOE method reached 92.3% accuracy, thus both methods had excellent accuracy. [PERSON] et al. [24] focused on landslides triggered by earthquakes in Central Italy. The authors concluded that the methodology could be used in areas where landslide data are not available. [PERSON] et al. [25] used AHP, FR, and Shannon entropy (SE) in Ethiopia. They found that AHP was the best method compared to the FR and SE models. The studies above reveal that, benefiting from remote sensing and GIS techniques, various methods can be employed depending on the specifications of the study area and the complexity of the models. These methods can be split into three distinctive categories: statistical methods, multi-criteria decision-making (MCDM), and machine learning (ML). Statistical methods include frequency ratio ([PERSON] et al. [26]; [PERSON] [9]; [PERSON] and [PERSON] [27]; [PERSON] and [PERSON] [28]; [PERSON] et al. [29]; [PERSON] [30]; [PERSON] [31]), index of entropy ([PERSON] et al.