# Application of a Hybrid Model in Landslide Susceptibility Evaluation of the Western Tibet Plateau
## 4 Discussion  
In the results of the models, the most sensitive slope strata are 20-30\({}^{\circ}\), indicating that shear stress increases with an increasing slope. The west direction has the largest weight due to the general orientation of the geological layers. Of the elevations, 4800-5100 m has the highest weight factor. The most affected lithology is hard rock, as the dominant lithology of the high elevation is hard bedrock. The highest susceptibility of the distance to faults is above 2100 m because the area the faults are stable, 900-1200 m away from the roads and  
Figure 10: ROC curves for the nine methods.  
rivers with the highest susceptibility, indicating that disasters have a significant impact on road and river traffic. Therefore, the results show that landslide susceptibility evaluation is important for disaster prevention and mitigation measures of local governments.  
By comparing the percentage of disaster area included in each risk level of the model evaluation results, the percentages of disaster areas in high and very high areas are as follows: IVM is 88.71%, W\({}^{+}\) is 90.06%, W\({}^{-}\) is 85.16%, IVM-LR is 90.30%, W\({}^{+}\)-LR is 88.20%, W\({}^{-}\)-LR is 80.85%, IVM-MLP is 87.01%, W\({}^{+}\)-MLP is 88.82% and W\({}^{-}\)-MLP is 82.87%. At the same time, with the increase in hazard level, the landslide area ratio shows an upward trend, which indicates that landslide sensitivity classification obtained by the seven calculation models is reasonable, and the mixed model is more reasonable than single models [68].  
It can be seen from the success rate curve that the calculation accuracy of the nine models is more than 85%. In contrast to other regional studies, [PERSON], et al. used the WoE model to determine the landslide susceptibility mapping of Long County. The AUC method revealed accuracies of 79.71% [69]. [PERSON], et al. chose Yadong County as the study area for landslide susceptibility with the LR model. The AUC method revealed accuracies of 83.8%. The results of these models indicated that high-altitude landslide susceptibility evaluation using GIS models is feasible [70; 71; 72; 73]. The prediction accuracies of the hybrid model are relatively high. This shows that the calculation results of the model are accurate and reliable, and that the mixed model is more accurate than the single model [74; 75; 76].  
Based on the above analysis, high-altitude landslide susceptibility evaluation is important and feasible. Furthermore, the disaster assessment classification map produced by the hybrid model has the highest rationality (90.3%), which is consistent with the results obtained in other studies. This study also finds more objective distinctions between the different models, which can be used to obtain better model combinations. In most cases, the hybrid model is better than the single model, and the hybrid model can optimize the problems of the single model to a certain extent. For information model and evidence weight, the calculation of the final LSI may deviate from the actual value through simple numerical superposition. The addition of the logical regression model can be used to calculate the coefficient of each factor, which can further highlight the differences in each factor's role in the final calculation of the LSI, thus obtaining more accurate and reliable evaluation results. In addition, this research also verifies the above-mentioned assumption and achieves the expected results. However, the study area is small and contains less human activity. Therefore, the results may not be suitable for use in landslide susceptibility evaluation in large cities with intense human activity.