# Application of a Hybrid Model in Landslide Susceptibility Evaluation of the Western Tibet Plateau
## 5 Conclusions  
The investigation of landslides in high-altitude areas presents important challenges. In this study, we employed GIS-based analysis to assess landslide susceptibility in the study area, utilized remote sensing techniques for landslide detection, and applied single and hybrid models such as information volume, evidence weight, and logistic regression for evaluation. This approach can significantly reduce the financial and personnel investments required for landslide investigations. Moreover, by minimizing human factors, the evaluation process ensures objective results.  
The susceptibility to landslides was classified into five hierarchic levels: very low, low, medium, high, and very high, using the natural breakpoint method. The rationality and accuracy of the results were validated by examining the area ratio of each landslide level and analyzing the receiver operating characteristic (ROC) curve. The findings demonstrated that the calculated results were highly reasonable and accurate, with the hybrid model outperforming the single models. Specifically, the IVM-LR model achieved a 90.30% ratio in high-risk and very high-risk areas, an AUC value of 0.873, and an accuracy rate of 87.3%, displaying the highest level of rationality and accuracy. This model proved suitable for landslide risk assessment research in high-altitude areas, providing reliable technical and data support for landslide investigations and assessments.  
In this research, we further confirmed the superior performance of the hybrid model compared to single models, using both traditional and machine learning models. Overall, the hybrid model based on machine learning turned out to be less effective than expected. In the future, we will continue to incorporate more machine learning models into the existing framework to enhance evaluation accuracy. This exploration will aid in advancing the field of landslide risk assessment.  
In addition, the integration of machine learning models into the existing framework should be explored. Incorporating advanced algorithms and techniques in the field of machine learning can potentially enhance the accuracy and predictive capabilities of landslide risk assessment. Furthermore, future research should focus on incorporating additional influential factors, such as climate change data and geological characteristics, to further improve the understanding and prediction of landslide susceptibility in high-altitude areas.  
By continuing to refine and expand the methodology, as well as incorporating new data sources and techniques, we can advance the field of landslide risk assessment. This will contribute to more effective disaster prevention and mitigation strategies, ultimately safeguarding lives, property, and infrastructure in high-altitude regions prone to landslides.  
The work was supported by the Programs of China Geological Survey (grant number DD20211543).  
Not applicable.  
Data Availability StatementData are contained within the article.  
We thank [PERSON] from The 10 th Geological Brigade of Liaoning Province Co., Ltd. for contributing to data resources and validation.  
The authors declare no conflict of interest.