## 5 Discussion  
Currently, some novel ensemble techniques have been proposed in landslide susceptibility mapping, and the excellent performance of ensemble techniques has been proven [92; 106; 107; 108; 109]. Furthermore, the hybrid integration of machine learning algorithms with bivariate statistical models can weaken the hypotheses of the conventional bivariate models and retain the merits of bivariate statistical models and machine learning models [110]. Compared with SysFor, FR, IoE and LR are three common evaluation models which have the merits of model stabilization, higher accuracy and simple calculation. [PERSON] et al. proposed a GIS-based landslide susceptibility mapping with frequency ratio, statistical index, and weights of evidence models for a part of Mazandaran Province, Iran. Both success rate curve and prediction rate curve demonstrated that the frequency ratio (FR) is a reliable model with the highest accuracy. Moreover, the landslide susceptibility map generated by the FR model is trustworthy for hazard mitigation strategies [25]. [PERSON] et al. introduced the index of entropy (IoE) and conditional probability models to landslide susceptibility research in Safarood Basin, Iran. The results indicated that both models have good predictive capacity, while the IoE performed slightly better than the conditional probability model in landslide susceptibility mapping [111]. [PERSON] et al. applied logistic regression (LR) and AHP models to landslide susceptibility assessment. The results indicated that the LR is a suitable model to classify and estimate the probability of landslide occurrence in the process of project research planning and implementation [112]. In the present study, for the class of 600-800 m of elevation (ratio value = 2.650), distance to roads \(<\)300 m (ratio value = 2.126) and 1100-1200 mm/yr of rainfall (ratio value = 2.354) facilitated the occurrence of landslides. The results based on the IoE model showed that soil (0.326), land use (0.311), elevation (0.223), and slope angle (0.143) are the most important factors, which are closely related to the occurrence and spatial distribution of landslides compared with other factors. On the contrary, distance to faults, plane curvature, SPI, and distance to rivers achieved the four lowest weight values, which were 0.022, 0.014, 0.003, and 0.014, respectively. However, the classifications of landslide conditioning factors were based on previous studies and might not be suitable for the present study. Therefore, further studies should be conducted to find an objective classification method for landslide conditioning factors [113; 114]. In order to obtain more reliable landslide susceptibility maps, the significant differences of six landslide susceptibility methods were calculated and compared. According to the relevant research [58; 115], the most common and effective methods include the receiver operating characteristics (ROC) curve, standard error (SE), 95% confidence interval (CI), and Wilcoxon signed-rank tests. Hence, the paper introduced three well known statistical parameters--the ROC curve, SE and 95% CI--to calculate and compare the model performance. For the training data, the results of the ROC curve, SE and 95% CI can be seen in Table 4. The FR_SysFor model performed best and acquired the highest AUC value (0.940), followed by IoE_SysFor, IoE_LR, FR_LR, FR, and IoE models with AUC values of 0.926, 0.783, 0.779, 0.757, and 0.746, respectively. Similarly, the two other statistical parameters showed the same results; the FR_SysFor model obtained the smallest SE and 95% CI. For the validating data, the parameters of the ROC curve, SE and 95% CI are shown in Table 5. The validating data showed that the FR_SysFor model had higher accuracy (AUC = 0.831) than the remaining models and performed best in the research. Meanwhile, the smallest SE and 95% CI also belonged to the FR_SysFor model. Overall, based on the FR and IoE models, combined with SysFor and LR models, the landslide susceptibility of Nanzheng County was studied.