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基于环境因子优化TSES法选择负样本及其在滑坡易发性评价中的应用

崔玉龙 朱路路 徐敏 缪海波

崔玉龙, 朱路路, 徐敏, 缪海波. 基于环境因子优化TSES法选择负样本及其在滑坡易发性评价中的应用[J]. 地质科技通报, 2024, 43(3): 192-199. doi: 10.19509/j.cnki.dzkq.tb20230400
引用本文: 崔玉龙, 朱路路, 徐敏, 缪海波. 基于环境因子优化TSES法选择负样本及其在滑坡易发性评价中的应用[J]. 地质科技通报, 2024, 43(3): 192-199. doi: 10.19509/j.cnki.dzkq.tb20230400
CUI Yulong, ZHU Lulu, XU Min, MIAO Haibo. Optimizing TSES method based on the environmental factors to select negative samples and its application in landslide susceptibility evaluation[J]. Bulletin of Geological Science and Technology, 2024, 43(3): 192-199. doi: 10.19509/j.cnki.dzkq.tb20230400
Citation: CUI Yulong, ZHU Lulu, XU Min, MIAO Haibo. Optimizing TSES method based on the environmental factors to select negative samples and its application in landslide susceptibility evaluation[J]. Bulletin of Geological Science and Technology, 2024, 43(3): 192-199. doi: 10.19509/j.cnki.dzkq.tb20230400

基于环境因子优化TSES法选择负样本及其在滑坡易发性评价中的应用

doi: 10.19509/j.cnki.dzkq.tb20230400
基金项目: 

国家自然科学基金项目 42277136

安徽省高校自然科学优秀青年科研项目 2023AH030041

详细信息
    通讯作者:

    崔玉龙,E-mail:ylcui@aust.edu.cn

  • 中图分类号: P642.22

Optimizing TSES method based on the environmental factors to select negative samples and its application in landslide susceptibility evaluation

More Information
  • 摘要:

    滑坡易发性评价是滑坡灾害防治的重要手段之一, 而不合理的滑坡负样本会影响滑坡易发性评价, 从而影响到滑坡灾害的防治, 因此提供一种合理的负样本选取方法变得尤为关键。以西藏米林市的古滑坡为例, 选择高程、坡度、坡向、坡位、距道路距离、距断层距离、距水系距离、地形起伏度、地层岩性、土地利用类型10类环境因子, 使用Relief算法计算环境因子的贡献值并依据贡献值优化选择环境因子; 基于环境因子优化的目标空间外向化采样法(target space exteriorization sampling, 简称TSES)选择负样本, 作为性能优异的随机森林模型的输入变量; 之后结合优化的环境因子和正或负样本预测米林市的滑坡易发性, 并用混淆矩阵和ROC曲线评价构建模型的性能。为检验环境因子优化的TSES法的有效性和先进性, 采用耦合信息量法和TSES法选择滑坡负样本并构建随机森林模型, 与环境因子优化的TSES法构建的随机森林模型进行对比研究。结果表明, 环境因子优化的TSES法构建的随机森林模型的评价效果较好, 其ACC为93.7%、AUC为0.987, 均高于耦合信息量、TSES法构成的模型。环境因子优化的TSES法能够提高模型的精度, 解决多因子作为约束条件取样中因子选取的问题, 为滑坡易发性评价采集负样本提供了新的思路。

     

  • 图 1  技术路线图

    Figure 1.  Technology roadmap

    图 2  目标空间外向化采样法

    Figure 2.  Target space exteriorization sampling method

    图 3  研究区地理位置

    Figure 3.  Geographical location of the study area

    图 4  滑坡解译实例

    a.沿江的大型岩质滑坡;b.由风化引起的岩质滑坡;c.解译滑坡分布图

    Figure 4.  Examples of landslide interpretation

    图 5  影响因子分类

    a.高程;b.坡度;c.坡向;d.坡位;e.距道路距离;f.距断层距离;g.距水系距离;h.地形起伏度;i.地层岩性;j.土地利用类型

    Figure 5.  Classification of influencing factors

    图 6  影响因子贡献值

    Figure 6.  Contribution values of influencing factors

    图 7  3种采集负样本方法构成随机森林易发图

    a.耦合信息量法;b.TSES法;c.环境因子优化TSES法

    Figure 7.  Landslide susceptibility maps of random forest model by using three negative samples collecting methods

    图 8  3种采集负样本方法构成随机森林的ROC曲线

    Figure 8.  ROC curves of random forest model using three negative samples collecting methods

    表  1  影响因子数据来源

    Table  1.   Data sources of influencing factors

    影响因子 数据名称 数据来源
    高程、坡度、坡向、距水系距离、地形起伏度 12.5 m分辨率的ALOS数字高程模型(DEM) http://wwwearthdate.org
    坡位 地理空间数据云 https://www.gscloud.cn
    距道路距离 全球地理信息资源目录服务系统 https://www.webmap.cn
    距断层距离 中国活动构造图(1∶400万)
    土地利用类型 鹏城实验室 https://data.ess.tsinghua.edu.cn
    地层岩性 中国地质调查局公开的1∶50万中国地质图 http://www.ngac.org.cn
    下载: 导出CSV

    表  2  随机森林模型的混淆矩阵

    Table  2.   Confusion matrix of random forests model

    模型种类 是否为滑坡(实际) 是否为滑坡(预测) 准确率ACC/%
    耦合信息量法 340 72 88.1
    随机森林模型 26 386
    TSES法随机 365 47 79.4
    森林模型 123 289
    环境因子优化的 406 6 93.7
    TSES法随机森林模型 46 366
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-07-14
  • 录用日期:  2023-09-08
  • 修回日期:  2023-09-07

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