## 5 Discussion  
The accuracy of classification and risk assessment of debris flows is crucial for preventing and controlling debris flows. According to different classification standards, the same debris flow can belong to different categories simultaneously. The traditional classification standards for debris flows have a certain lag in preventing and controlling current debris flows [25]. For regional debris flows, different regions of debris flows have different impact factors, which need to be comprehensively selected based on the actual situation of different research regions [65]. At the same time, there are also areas for improvement in calculating the weight of the selected influencing factors. For current debris flow risk assessment models, most require manual determination of risk classification standards, which results in the inability of debris flow risk assessment results to escape the influence of human subjectivity [33]. This article proposes associating the combination weighting method with affinity propagation clustering analysis to obtain combination weights scientifically and, based on this, use clustering analysis to obtain accurate classification standards.  
In this article, the reasonable and correct selection of debris flow evaluation indicators and the calculation of indicator weights are prerequisites for using cluster analysis methods for debris flow classification and risk assessment [66]. This article uses various methods, such as on-site geological surveys, multiple remote sensing images, and drone images, to select nine influencing factors based on the essential characteristics of the debris flow clusters in Longmenshan Town. These influencing factors reflect the various geological, material, and trigger conditions of the debris flows within the study area and calculate the weight of the selected factors. The CRITIC method is an objective weight calculation method, but objective methods cannot reasonably exclude singular data in data processing, which may result in incorrect results. The AHP can fully utilize the experience of experts in the corresponding field to calculate weights, which is a subjective method. However, different experts judge different factors, which can cause incorrect results. Therefore, using the combination weighting method to combine the advantages of the two methods can obtain more scientific indicator weights, which is superior to applying a single method.  
Compared with traditional clustering analysis, affinity propagation clustering analysis does not require manually specifying the number of clusters and cluster centers, and the clustering results are unique, with obvious advantages. When using affinity propagation clustering analysis to classify and evaluate the risk of debris flows, optimizing the affinity propagation clustering algorithm, further optimizing the algorithm performance, scientifically obtaining the classification results of debris flows, quantifying the classification standards, and correctly evaluating the risk of debris flows. However, this classification and evaluation method has limitations: (1) it does not apply to the risk assessment of individual debris flows; (2) when the number of debris flows is small, this method cannot  
Figure 12: Multi-phase Debris flow buildup in Huilong Gully.  
be applied; and (3) a certain number of evaluation indicators need to be selected, and this method cannot be applied when the number of evaluation indicators is too small.  
As the results of this article are based on the primary data of 14 debris flows in Longmenshan Town, Pengzhou City, China, with significant regional significance, considering the different development characteristics of debris flows in different regions, when applying this method to debris flows in other regions, the differences in regional conditions should be considered.