ISSN 1000-3665 CN 11-2202/P
    盛逸凡, 李远耀, 徐勇, 吴吉明, 林巍. 基于有效降雨强度和逻辑回归的降雨型 滑坡预测模型[J]. 水文地质工程地质, 2019, 46(1): 156-156. DOI: 10.16030/j.cnki.issn.1000-3665.2019.01.21
    引用本文: 盛逸凡, 李远耀, 徐勇, 吴吉明, 林巍. 基于有效降雨强度和逻辑回归的降雨型 滑坡预测模型[J]. 水文地质工程地质, 2019, 46(1): 156-156. DOI: 10.16030/j.cnki.issn.1000-3665.2019.01.21
    SHENGYifan, . Prediction of rainfall-type landslides based on effective rainfall intensity and logistic regression[J]. Hydrogeology & Engineering Geology, 2019, 46(1): 156-156. DOI: 10.16030/j.cnki.issn.1000-3665.2019.01.21
    Citation: SHENGYifan, . Prediction of rainfall-type landslides based on effective rainfall intensity and logistic regression[J]. Hydrogeology & Engineering Geology, 2019, 46(1): 156-156. DOI: 10.16030/j.cnki.issn.1000-3665.2019.01.21

    基于有效降雨强度和逻辑回归的降雨型 滑坡预测模型

    Prediction of rainfall-type landslides based on effective rainfall intensity and logistic regression

    • 摘要: 以湖南省张家界市桑植县为研究区,在全面分析近30年降雨及滑坡数据的基础上,对滑坡及滑坡数量与降雨因子的关系开展了统计分析研究。首先确定了区域最佳有效降雨衰减系数,同时分别按滑坡规模、坡度、厚度大小统计了降雨与历史滑坡信息,得出有效降雨强度(I)与持续时间(D)散点图,由此确定各不同概率下诱发滑坡的区域有效降雨强度阈值,并进行了滑坡灾害危险性等级划分。进而,利用部分样本数据进行逻辑回归分析,得到了该研究区的滑坡发生概率预测方程,并给出了降雨强度临界值定量表达式,最后选用实际降雨诱发滑坡事件与未诱发滑坡事件进行对比验证。结果表明,文章所建立的滑坡预测模型准确性较高,预测情况与实际情况比较吻合。

       

      Abstract: The Sangzhi district of Hunan is taken as the research area. Based on the comprehensive analysis of rainfall and landslide data of the past 30 years in this area, the correlation and partial correlation analysis are carried out between the cumulative rainfall factors, the rainfall factors and the occurrence or not of landslides and the number of landslides. The optimal effective rainfall attenuation coefficient is determined as 0.8. According to the volume and scale of rainfall monitoring data and historical landslide information, the scattered plot of effective rainfall intensity (I) and duration (D) is obtained. The effective rainfall intensity threshold of the landslide induced by each probability is determined, and the classification of landslide hazard grade is carried out. Using the binary data, the logistic regression of the sample data is conducted, and the disaster probability prediction equation of the study area is obtained. The expression of the critical value of the rainfall intensity induced by the landslide in the study area is also obtained. The rainfall landslide event and the rainfall landslide event are selected for verification. The results show that the landslide prediction model established in this paper is of high accuracy and can provide a scientific basis for rainfall landslide prediction.

       

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