ISSN 1003-8035 CN 11-2852/P
    郭飞,王秀娟,陈玺,等. 基于不同模型的赣南地区小型削方滑坡易发性评价对比分析[J]. 中国地质灾害与防治学报,2022,33(6): 125-133. DOI: 10.16031/j.cnki.issn.1003-8035.202205027
    引用本文: 郭飞,王秀娟,陈玺,等. 基于不同模型的赣南地区小型削方滑坡易发性评价对比分析[J]. 中国地质灾害与防治学报,2022,33(6): 125-133. DOI: 10.16031/j.cnki.issn.1003-8035.202205027
    GUO Fei, WANG Xiujuan, CHEN Xi, et al. Comparative analyses on susceptibility of cutting slope landslides in southern Jiangxi using different models[J]. The Chinese Journal of Geological Hazard and Control, 2022, 33(6): 125-133. DOI: 10.16031/j.cnki.issn.1003-8035.202205027
    Citation: GUO Fei, WANG Xiujuan, CHEN Xi, et al. Comparative analyses on susceptibility of cutting slope landslides in southern Jiangxi using different models[J]. The Chinese Journal of Geological Hazard and Control, 2022, 33(6): 125-133. DOI: 10.16031/j.cnki.issn.1003-8035.202205027

    基于不同模型的赣南地区小型削方滑坡易发性评价对比分析

    Comparative analyses on susceptibility of cutting slope landslides in southern Jiangxi using different models

    • 摘要: 赣南地区滑坡灾害点多、面广、规模小,具有群发性和突发性的特点,90%以上的滑坡是因人工切坡导致的。为研究赣南地区小型削方滑坡对易发性评价模型的适用性,以赣州市于都县银坑镇为例,基于野外地质调查成果,并利用地理探测器,选取坡度、坡体结构、岩组、断层、道路、植被等6个评价指标,分别选用信息量模型、人工神经网络模型、决策树模型和逻辑回归模型开展易发性评价。结果表明:信息量、人工神经网络、决策树和逻辑回归等模型得到的AUC值分别为0.800、0.708、0.672和0.586,信息量模型所得的易发性结果与研究区滑坡实际分布情况较吻合,高易发区和中易发区滑坡占比近80%。信息量模型较其他三个模型,更适合于赣南地区小型削方滑坡易发性评价,评价结果对该地区地质灾害易发性评价模型选取提供了参考与借鉴。

       

      Abstract: There are many landslide disasters in southern Jiangxi, with a wide area and a small scale, and are characterized by mass and suddenness. More than 90% of landslides are caused by artificial slope cutting. In order to study the applicability of the susceptibility evaluation model for cutting slope landslides caused by cutting slopes in southern Jiangxi, taking Yinkeng Town, Yudu County, Ganzhou City as an example, based on the results of field geological surveys, and using GeoDetectors, the slope, the slope structure, rock formation, fault, road, and vegetation, were selected to carry out landslide susceptibility assessment by using the information value model (I), artificial neural network model (ANN), decision tree model (DT) and Logic regression model respectively. The results show that the AUC values obtained from information value model, artificial neural network model, decision tree model and logistic regression model are 0.800, 0.708, 0.672 and 0.586, respectively. The susceptibility results obtained by the information value model are in good agreement with the actual distribution of landslides in the study area. The specific value of the proportion of landslides in high-prone areas and medium-prone areas exceeds 80%. The information model is more suitable for the landslide susceptibility assessment under cutting slope in southern Jiangxi than the other three models. The assessment results provide a reference for the selection of the assessment model for the geohazard susceptibility in this region.

       

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