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    基于集成学习与径向基神经网络耦合模型的三峡库区滑坡易发性评价

    周超 殷坤龙 曹颖 李远耀

    周超, 殷坤龙, 曹颖, 李远耀, 2020. 基于集成学习与径向基神经网络耦合模型的三峡库区滑坡易发性评价. 地球科学, 45(6): 1865-1876. doi: 10.3799/dqkx.2020.071
    引用本文: 周超, 殷坤龙, 曹颖, 李远耀, 2020. 基于集成学习与径向基神经网络耦合模型的三峡库区滑坡易发性评价. 地球科学, 45(6): 1865-1876. doi: 10.3799/dqkx.2020.071
    Zhou Chao, Yin Kunlong, Cao Ying, Li Yuanyao, 2020. Landslide Susceptibility Assessment by Applying the Coupling Method of Radial Basis Neural Network and Adaboost: A Case Study from the Three Gorges Reservoir Area. Earth Science, 45(6): 1865-1876. doi: 10.3799/dqkx.2020.071
    Citation: Zhou Chao, Yin Kunlong, Cao Ying, Li Yuanyao, 2020. Landslide Susceptibility Assessment by Applying the Coupling Method of Radial Basis Neural Network and Adaboost: A Case Study from the Three Gorges Reservoir Area. Earth Science, 45(6): 1865-1876. doi: 10.3799/dqkx.2020.071

    基于集成学习与径向基神经网络耦合模型的三峡库区滑坡易发性评价

    doi: 10.3799/dqkx.2020.071
    基金项目: 

    国家自然科学基金 41907253

    国家自然科学基金 41702330

    国家重点研发计划 2018YFC0809402

    详细信息
      作者简介:

      周超(1989-), 男, 副教授, 博士, 主要从事地质灾害监测预警与风险评价研究

    • 中图分类号: P954

    Landslide Susceptibility Assessment by Applying the Coupling Method of Radial Basis Neural Network and Adaboost: A Case Study from the Three Gorges Reservoir Area

    • 摘要: 准确的滑坡易发性评价结果是滑坡风险评价的重要基础.为提升滑坡易发性评价精度,以三峡库区龙驹坝为例,选取坡度等10个因子构建滑坡易发性评价指标体系,应用频率比方法定量分析各指标与滑坡发育的关系.在此基础上,随机选取70%/30%的滑坡数据作为训练/测试样本,应用径向基神经网络和Adaboost集成学习耦合模型(RBNN-Adaboost),径向基神经网络和逻辑回归模型分别开展易发性评价.结果显示:水系距离、坡度等是滑坡发育的主控因素;RBNN-Adaboost耦合模型的预测精度最高(0.820),优于RBNN模型和LR模型的0.781和0.748.Adaboost集成算法能进一步提升模型的预测性能,所提出的耦合模型结合了两者的优点,具有更强的预测能力,是一种可靠的滑坡易发性评价模型.

       

    • 图  1  径向基神经网络结构

      Fig.  1.  The structure of radical basis neural network

      图  2  RBNN-Adaboost耦合模型流程图

      Fig.  2.  The flow chart of the RBNN-Adaboost method

      图  3  (a) 三峡库区地图和(b)研究区高程及滑坡分布图

      Fig.  3.  The map of Three Gorges reservoir area (a) and the landslide distribution map with elevation (b)

      图  4  堆积层滑坡野外调查现场

      Fig.  4.  Field investigation site of colluvial landslide

      图  5  滑坡指标分布图

      a.高程;b.坡度;c.坡向;d.径流强度指数;e.地形湿度指数;f.地层岩性;g.斜坡结构;h.构造距离;i.水系距离;j.道路距离;A~F意义见附表 2

      Fig.  5.  Landslide causal factors of the study area

      图  6  非滑坡样本空间分布

      Fig.  6.  The distribution of non-landslide samples

      图  7  滑坡易发性分级图

      a. RBNN-Adaboost模型;b.RBNN模型;c. LR模型

      Fig.  7.  The susceptibility maps

      图  8  ROC精度曲线

      a.训练样本;b.测试样本

      Fig.  8.  ROC curves of the three used models

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    • 收稿日期:  2020-03-02
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