## 5 Discussion  
In this study, a novel bagging-based kernel logistic regression classification model was proposed and applied for landslide susceptibility in Shangnan County, China. A total of 15 conditioning factors were selected to create landslide susceptibility models. The results showed that all the conditioning factors had positive effects on landslide occurrence with different degrees of influence according to the AM values based on LSSVM. Altitude, distance to roads, and distance to faults received the highest AM values, in agreement with some existing reports [33; 39]. Altitude comprehensive controls the local climate and vegetation development characteristics [69], which are related to the stability of slopes [70]. The AM values for lithology, land use, and distance to rivers were both larger than 10.0 as well. Generally, the strength of a slope body varies with different lithology types [71]. For areas with different land use types, the corresponding physical and mechanical characteristics of soils and rocks have notable differences [44]. Rivers can affect the hydro-geological conditions of slopes, which have strong connections with landslide occurrence [39]. On the basis of relevant studies, STI, SPI, and TWI are usually indispensable conditioning factors in landslide susceptibility modeling [23; 66; 72]. Curvature, which includes profile curvature and plan curvature, can reflect the geometric features of slopes, and the geometric features can influence stress distribution of slopes [30; 73]. For slope angle, landslides generally occur in a certain range of slope angles, and slope angle is another critical conditioning factor [45]. The relationships between the other factors (NDVI, soil type, and slope aspect) and landslide occurrence have also been analyzed by other researchers [74; 75; 76].  
To evaluate the performance of BKLR, KLR, and SVM models in landslide susceptibility mapping, ROC curves and AUC values of various models were obtained. The results confirmed that all three models were reasonable for landslide susceptibility mapping, and the BKLR model had the best accuracy and prediction capacity. The results also verified that bagging is an effective tool to increase the prediction accuracy of a single classifier by creating an ensemble learning classifier [77]. In this case, bagging improved the stability of the conventional KLR model using the RBF kernel function. The sensitivity of a single classifier to noise in a dataset could be decreased by bootstrap sampling, so the variance of the classification model decreases correspondingly [53].  
There are some advantages of the BKLR: This model can produce class probabilities, whereas SVM is a deterministic classifier. BKLR is also recommended for future application to the study area with new techniques and models or new hybrid models, and the most appropriate model can be selected for future modeling. As a practical recommendation, we propose that the government and decision makers must complete an extensive field survey to confirm the results of the present study (and especially the areas that have high and very high susceptibility to landslide occurrences). According to case study characteristics, policy makers can make an informed decision for future landslide hazard mitigation.