Debris Flow Classification and Risk Assessment Based on Combination Weighting Method and Cluster Analysis: A Case Study of Debris Flow Clusters in Longmenshan Town, Pengzhou, China  
[PERSON]  
1 State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China; [EMAIL_ADDRESS].  
people. Specifically, during the decade between 2001 and 2010, mountain hazards caused 9933 deaths and missing persons, excluding approximately 25,000 deaths caused by landslides, collapses, and mudslides during the Wenchuan earthquake [7]. Especially for the debris flow group located in the active seismic zone and scenic area, because of the dense population and numerous road networks, if the debris flow disaster occurs, it will cause incalculable losses, so it is essential to classify debris flow in particular areas and assess the risk of debris flow in special areas [8; 9].  
In recent years, research on the risk assessment of debris flows has been divided into numerical simulation [10], empirical analysis [11], and artificial intelligence [12]. Among them, numerical simulation can not only simulate the movement process of debris flows but also calculate flow velocity and movement distance. However, the parameters required for numerical simulation calculation are generally difficult to obtain. The simulation process is also relatively complex, making the obtained results not necessarily consistent with the actual situation. Moreover, simulating and calculating each debris flow is impractical for regional debris flow clusters [13; 14]. The empirical analysis mainly depends on the geological engineering scientists' on-site survey results. Human subjectivity plays a leading role. Different researchers may have different evaluation criteria for the same debris flow risk, leading to the need for empirical analysis methods to combine other methods to determine the debris flow risk comprehensively [15].  
With the development of artificial intelligence, extension theory [16], artificial neural networks [17], Bayesian theory [18], genetic algorithms [19], evidence weight method [20], grey correlation method, and other methods have been widely used in debris flow risk assessment [21]. When most algorithms are applied to debris flow risk assessment, complex risk assessment indicators need to be selected, indicator weights need to be calculated, and the final evaluation model needs to be determined. However, things could still be improved in the current method of determining the weight of evaluation indicators. Researchers often pay more attention to the influence of objective indicators or subjective factors, and it is necessary to consider the collective impact of the two comprehensively. Some evaluation models require manual scoring and determination of grading boundaries, leading to certain deficiencies in the evaluation results of debris flow risk. Therefore, understanding how to combine the influence of subjective and objective weights and establish a scientific evaluation model based on this has practical significance for the risk assessment of debris flows.  
Through an on-site geological survey, combined with UAV images and remote sensing images, 14 debris flows were found in Longmenshan Town, Pengzhou City, Sichuan Province, China, distributed on both sides of Baishui River and Jianjiang River, with 5 on both sides of Jianjiang River, and 9 on both sides of Baishui River. Considering the rapid and sudden occurrence of debris flows and many surrounding villages and tourists, it is essential to classify and evaluate the risk of debris flows in this area.