Abstract: Debris flow, a geological disaster of great destructive power that often occurs in mountainous areas triggered by heavy rain or earthquakes, requires the utilization of specific algorithms to evaluate its hazard in order to prevent and reduce its destructive power in advance. The cloud model fog characteristics were used to conduct a consistency test and coupling of the weights of various evaluation indicators, obtaining a reasonable weight threshold interval. Linear programming optimization algorithm was then utilized to dynamically set the value of each evaluation indicator within the weight interval by setting the objective function, thus obtaining the most dangerous score for each debris flow channel. Furthermore, the cloud digital feature formula for a single indicator was improved to construct a comprehensive cloud model with more comprehensive coverage, and the maximum hazard score was inputted to classify the hazard level of debris flow channels in the study area. The proposed study was applied to 72 instances of debris flow channels in Beichuan County after the Wenchuan earthquake. It is indicated that the evaluation results are consistent with the actual topographical environment of the study area, providing new ideas for the prevention of similar types of debris flow disasters.

摘要： 泥石流是一种由暴雨或地震诱发常发生在山区的破坏力强大的地质灾害，为了提前预防和减少其破坏能力，使用特定算法评价其危险性十分必要。采用云模型雾化特性对各评价指标权重进行一致性检验后耦合，得到合理的权重阈值区间。以线性规划优化算法通过设定目标函数后对各评价指标在权重区间内动态取值，从而得到各个泥石流沟渠最危险评分。改进了单指标的云数字特征公式，构建覆盖更全面的综合云模型，输入最大危险评分对研究区泥石流沟渠进行危险性分级。将该研究应用在汶川地震后北川县产生的72条泥石流沟渠实例中，其评价结果显示与研究区实际地形环境情况相吻合，为相似类型泥石流灾害的预防提供了新思路。