## 5 Discussion  
This work attempted to establish a detailed landslide inventory and used the LR and SVM to carry out the susceptibility mapping for the 2018 Tomakomai, Japan Mw6.6 earthquake. Using the LR and SVM, two landslide susceptibility maps were produced for the study area. The results show that the SVM slightly outperformed LR method. There are two reasons. First, the SVM method  
Figure 16: Area and LAP curves in different susceptibility classes. (**a**) LR and (**b**) SVM. LND is landslide number density. LAD is landslide area density. Figure 15: Area under curve (AUC) for Logistic regression (LR) and SVM: (**a**) Training dataset and (**b**) validating dataset. is not oriented to linear features or even features that interact linearly. It can solve nonlinear and high-dimensional pattern recognition problems better than LR [47]. Secondly, SVM is less prone to over fitting in comparison with LR. While LR is a regression analysis method, which is based on logarithmic transformation of a generalized linear model. Thus, LR does not perform well when there is more complicated relationship between the landslides and the influencing factors [47]. In the application of the LR model to landslide susceptibility mapping (LSM), many scientists have tried to exploit the data using continuous variables [14; 48] or dummy binary variables. When using dummy binary variables, the complexity of the data structure will increase. It means that many parameters are included, the regression equation will be very long, and it may even introduce numerical problems [48]. Thus, some studies extended the application of logistic regression by using the continuous data as they can attain good assessment results [40; 48]. Although this can decreases the complexity of the data structure and increase flexibility of the LR model, for some independent variables such as elevation, distances to epicenter and distances to faults, they may not have the direct positive or negative correlation with the prediction values (susceptibility index). If the independent variables are not converted into categorized variables, it may influence the prediction accuracy. Thus, based on previous studies [17], we used dummy binary variables for LR [17]. By classifying the independent variables we calculated the LR coefficients for each class. Based on the resulting susceptibility index map of LR (Figure 11a) and SVM (Figure 13a), the distribution of susceptibility index are basically the same, and the high-susceptibility areas are mainly distributed along the river valleys, which is basically consistent with the actual landslide distribution. Meanwhile, the susceptibility index was divided into 32 equal intervals, and then the classification area and its LAP within each susceptibility level were counted. From the statistical results (Figure 16), the LAP curves of two models are basically consistent. The LAP increases rapidly with the increase of the susceptibility level. However, the classified area distribution of two models is dissimilar. For LR, the peak values of classified area were mainly concentrated in \(<\)0.1 and \(>\)0.9 intervals (Figure 16a). So the distribution area concentrates on the interval of very high (0.8-1) and very low (0-0.2) (Figure 11b). For SVM, by contrast, the peak values of classified area were distributed in the interval of 0.3-0.45 and 0.65-0.8 (Figure 16b). Thus, the classification area of the high (0.6-0.8) and low (0.2-0.4) is larger (Figure 13b). In this paper, the assessment results of the two models are classified by the commonly used five-class interval method [27]. As the absolute values of the susceptibility index are different, causing different class results, which can also be seen in previous studies [11; 49; 50]. Therefore, this reminds us that when we conduct the landslide susceptibility assessment, different classification criteria will result in different susceptibility mapping results. Thus, how to establish reasonable and objective classification standards is very important in this aspect.