# A Combined Method for Preparation of Landslide Susceptibility Map in Izmir (Turkije)
###### Abstract
[32]; [PERSON] [33]), fuzzy logic ([PERSON] [34]), MCDM methods, including AHP ([PERSON] et al. [35]; [PERSON] et al. [36]; [PERSON] et al. [37]; [PERSON] et al. [38]; [PERSON] et al. [39]; [PERSON] ve [PERSON] [40]), ANP methods ([PERSON] and [PERSON] [41]), ML methods such as support vector machines ([PERSON] [42]; [PERSON] et al. [43]), random forest ([PERSON] et al. [44]; [PERSON] et al. [45]; [PERSON] et al. [46]; [PERSON] et al. [47]; [PERSON] et al. [48]), logistic regression ([PERSON] et al. [49]; [PERSON] et al. [50]; [PERSON] et al. [51]; [PERSON] [9]; [PERSON] and [PERSON] [52]; [PERSON] and [PERSON] [27]; [PERSON] and [PERSON] [53]; [PERSON] and [PERSON] [54]; [PERSON] [55]; [PERSON], et al. [56]; [PERSON] et al. [57]), artificial neural networks ([PERSON] and [PERSON] [58]; [PERSON] et al. [59]) and so on. MCDM methods are based on expert opinion and experience. Reasonable results can be gathered if the method is applied by a person or group of people who know the study area's conditions well [60]. The parameters related to existing landslides and geo-environmental factors need to be examined properly by means of a detailed field survey in these methods. Expert opinion here may move the analysis forward or may cause some mistakes if it is conducted without experience [40]. Statistical methods are based on the relationship between existing landslide sites and landslide-causing factors. Statistical methods are also used during the analyses. The main advantage of these methods is that the researcher can control weighting values assigned to parameters and final input parameters in statistical assessments [61]. However, the results can be negatively affected if input parameters have multi-collinearity. The main aim of the machine learning method is to create models which solve potential problems detecting complex systems using old databases [62]. There is no error margin in the algorithmically conducted analyses used in these models. Nevertheless, in this method, the computer not only runs output entered by user but also takes part in decision-making, and the decisions are given without expert opinion, which may produce poor results [63]. Each method has strengths and weaknesses when analyzed and there is no clear winner among the statistical and ML-based techniques. In this study, the combined method (CM) is developed, aiming to combine the strengths of these methods while minimizing erroneous outputs. CM is a viable method to highlight the necessary measures which must be taken for risky situations, to predict unforeseen risky conditions, and to analyze the effects which trigger landslides and enlarge landslide sites. CM is based on combining LSMs with the highest susceptibility prepared using other methods. Several researchers have utilized a combination of methods in the literature ([PERSON] et al. [64]; [PERSON] et al. [65]; [PERSON] et al. [66]; [PERSON] et al. [21], etc.). The power of combining methods has also been utilized in novel susceptibility studies such as those examining wildfires ([PERSON] and [PERSON] [67]), floods ([PERSON] et al. [68]) and gully erosion ([PERSON] et al. [69]). The key advantage of this study is that a better LSM was developed helped by the combination of the LR, AHP, FR and IOE methods.