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北京东部山区地质灾害危险性评价

RISK ASSESSMENT OF GEOLOGICAL DISASTERS IN MOUNTAINOUS AREAS EAST OF BEIJING
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摘要 地质灾害危险性评价是地质灾害风险管理重要一环。以北京东部山区斜坡地质灾害为例,选取归一化植被指数(NDVI)、起伏度、坡度、降雨、坡向、距道路距离、距断层距离、工程地质岩组8个因子,基于信息量法和主成分分析(PCA)加权信息量法对斜坡类地质灾害危险性进行预测,运用地理探测器评价各因子对地质灾害危险性的解释力。结果表明:地质灾害高危险区主要分布在东北部,低危险区分布在西部河谷及西南部平原区。两种评价方法都能很好预测研究区危险性,普通信息量法精度更高,但空间上在平原区PCA加权信息量法预测更合理。地理探测器结果表明距道路距离对普通信息量模型评价结果解释力达54.8%;地形起伏度、降雨、工程地质岩组对PCA加权评价结果解释力分别为55.8%、32.6%、28%,其他因子解释力较小。评价结果可为研究区应急管理及防灾减灾工作提供参考。 The Risk assessment of geological disaster susceptibility is one of the important processes of geological disaster risk management. In the eastern Beijing, for example, select the normalized vegetation(NDVI), undulation, slope, rainfall, aspect, road distance, fault distance, engineering geological rock group 8 factors, based on information and PCA weighted information method for regional prediction of slope geological disaster, and use the geographical detector to evaluate the interpretation of geological disaster prone. The results show that the high risk area of geological disasters is mainly distributed in the northeast, and the low danger area is distributed in the western valley and the southwest plain area. Both evaluation methods can predict the risk of the research area well, and the general information method has higher accuracy, but the PCA is more reasonable in the plain area. The results of geographic detector show that the explanatory power of road distance is 54.8%;the explanatory power of PCA weighted evaluation results of topographic relief, rainfall and engineering geological rock groups is 55.8%, 32.6% and 28% respectively, while other factors are small. The evaluation results can provide reference for emergency management and disaster prevention and mitigation in the study area.
作者 蔡佳明 CAI Jia-ming(Resources&Environmental Engineering Co.,Ltd,Beijing 101300,China)
出处 《地质灾害与环境保护》 2024年第2期49-55,共7页 Journal of Geological Hazards and Environment Preservation
关键词 信息量法 主成分分析 地质灾害 地理探测器 information value model principal component analysis geological disaster geographic detector
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