# Research on an Evaluation Method of Snowdrift Hazard for Railway Subgrades
## 4 Discussion  
This study presents an integrated evaluation system that combines the Analytic Hierarchy Process (AHP) and fuzzy comprehensive evaluation (FCE) to assess the risk of snowdrift disasters along the Afu Railway. Our findings indicate that subgrade design parameters and regional snowfield conditions are the two primary metrics affecting snowdrift disasters, consistent with the results of a study by [PERSON] et al. [21], which suggests that the form of subgrade structure significantly impacts the characteristics of snowdrift disasters. Compared to earlier studies, the evaluation system in this research is more comprehensive and detailed. For instance, while [PERSON] et al. [22] focused primarily on the macroscopic distribution characteristics of snowdrift disasters when establishing a grading model, this study further refined the evaluation metrics and indicators, placing special emphasis on the importance of subgrade design parameters and regional snowfield conditions. Additionally, this research considers factors such as topography and vegetation coverage, which have often been insufficiently emphasized in previous studies [23; 24]. The enhanced evaluation framework of this study reflects a deeper understanding of the multifaceted nature of snowdrift hazards. By incorporating a broader range of indicators, the study provides a more nuanced assessment that can better inform the development of strategies for railway subgrade design and snowdrift disaster mitigation. The acknowledgment of previously under-studied factors highlights the need for a holistic approach to risk evaluation, which this study successfully implements. The fuzzy comprehensive evaluation (FCE) method employed in this study has demonstrated high applicability in dealing with complex issues that combine qualitative and quantitative factors, consistent with the findings of [PERSON] et al. [14]. Compared to the Multi-Indicator Comprehensive Assessment method used by [PERSON] et al. [15], this study better addresses uncertainties and ambiguities in the evaluation process by introducing the concept of fuzzy mathematics [23; 24]. Through fuzzy mathematical methods, this study has transformed experts' subjective judgments into quantifiable evaluation indicators, enhancing the objectivity and accuracy of the evaluation. Figure 11: ROC curve. Furthermore, the evaluation results of this study are in accordance with on-site actual observation results, verifying the accuracy and practicality of the evaluation method. This is similar to the research findings of [PERSON] [25], both confirming the effectiveness of the fuzzy comprehensive evaluation method in the risk assessment of snowdrift disasters. However, compared to the research of [PERSON] [17], this study's evaluation system is more comprehensive, considering more contributing factors, such as regional windfield characteristics and subgrade design parameters. Similarly to the research of [PERSON] et al. [26], the importance of comparing model results with on-site observational data is emphasized. However, this study has conducted a more rigorous examination of the validity of the evaluation results through the principle of maximum membership degree and the weighted averaging method, thereby ensuring the reliability of the evaluation results [27; 28]. Despite the achievements of this study in the evaluation of the risk of snowdrift disasters, there are still limitations. The evaluation system of this study mainly relied on existing meteorological data and expert experience. Future research could consider introducing more actual measurement data and machine learning algorithms to improve the accuracy and generalization ability of the evaluation model [29]. This study mainly focuses on snowdrift disasters on railway subgrades, and the evaluation of snowdrift disasters on other transportation infrastructure is also worth further study [15; 30; 31]. Finally, the evaluation results need to be continuously verified and improved through long-term on-site observation.