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基于XGBoost和云模型的地质灾害易发性评价

张威 胡舫瑞 綦巍 彭琳 王咏林 陈枫

张威,胡舫瑞,綦巍,等. 基于XGBoost和云模型的地质灾害易发性评价[J]. 中国地质灾害与防治学报,2023,34(6): 134-143 doi: 10.16031/j.cnki.issn.1003-8035.202210041
引用本文: 张威,胡舫瑞,綦巍,等. 基于XGBoost和云模型的地质灾害易发性评价[J]. 中国地质灾害与防治学报,2023,34(6): 134-143 doi: 10.16031/j.cnki.issn.1003-8035.202210041
ZHANG Wei,HU Fangrui,QI Wei,et al. Susceptibility assessment of geological hazard based on XGBoost and cloud model[J]. The Chinese Journal of Geological Hazard and Control,2023,34(6): 134-143 doi: 10.16031/j.cnki.issn.1003-8035.202210041
Citation: ZHANG Wei,HU Fangrui,QI Wei,et al. Susceptibility assessment of geological hazard based on XGBoost and cloud model[J]. The Chinese Journal of Geological Hazard and Control,2023,34(6): 134-143 doi: 10.16031/j.cnki.issn.1003-8035.202210041

基于XGBoost和云模型的地质灾害易发性评价

doi: 10.16031/j.cnki.issn.1003-8035.202210041
详细信息
    作者简介:

    张威:张 威(1982-),男,辽宁丹东人,本科,高级工程师,主要从事自然灾害风险方面的研究。E-mail:5869365@qq.com

    通讯作者:

    胡舫瑞(1988-),男,辽宁营口人,硕士,工程师,主要从事地质灾害风险评价与管理方面的研究。E-mail:hufrcug@163.com

  • 中图分类号: P642.22

Susceptibility assessment of geological hazard based on XGBoost and cloud model

  • 摘要: 传统地质灾害易发性评价中,存在着易发性因子权重选取主观性强、因子分级具有随机性和模糊性的问题。采用单一评价模型只能对地质灾害的易发性进行定性评估,无法定量化评价。针对这一问题,文章基于改进集成算法(XGBoost)和云模型,在朝阳市189个灾害隐患点中选择坡度、气象条件、归一化植被指数、高程等12个易发性因子,通过XGBoost分类算法确定了易发性因子权重,拟合准确率为96.5%,达到了较高的精度。在此基础上利用云模型将因子分级的模糊性问题转化为定量问题,建立了朝阳市地质灾害易发性评价指标体系,进而评价地质灾害的易发性。以朝阳市大东山为评价单元对该评价体系进行验证。结果表明该评价单元的易发程度为高易发,与实际情况吻合。文章提出的方法可对地质灾害易发性评价提供参考。
  • 图  1  云模型示意图

    Figure  1.  Schematic diagram of cloud model

    图  2  朝阳市地质情况区位图

    Figure  2.  Location map of the study area

    图  3  地质灾害易发性因子与地质灾害点关系图

    Figure  3.  The relationship between geological hazard susceptibility factors and disaster points

    图  4  易发性因子相关性图

    Figure  4.  Correlation diagram of susceptibility factors

    图  5  易发性因子数据分布图

    Figure  5.  Data distribution of susceptibility factors

    图  6  样本与总体ROC曲线及P-R曲线

    Figure  6.  ROC curve and P-R curve for sample and overall population

    图  7  易发性因子权重分数图

    Figure  7.  Weighted scores of susceptibility factors

    图  8  评价指标云图

    Figure  8.  Cloud map of evaluation indicators

    图  9  大东山滑坡正射影像

    Figure  9.  Orthophoto image of Dadongshan landslide

    图  10  总体评估等级云相似度图

    Figure  10.  Cloud similarity graph of overall evaluation grades

    表  1  影响因子权重表

    Table  1.   Weight table of impact factors

    序号易发性因子权重
    1坡度0.169
    2气象条件0.151
    3归一化植被指数0.136
    4高程0.133
    5人口密度0.122
    6坡向0.115
    7水文条件0.053
    8公路距离0.042
    9工程地质条件0.029
    10断裂距离0.028
    11水系距离0.017
    12铁路距离0.006
    下载: 导出CSV

    表  2  影响因子分级表

    Table  2.   Grading table of impact factors

    序号 易发性因子 分级
    1 坡度 {平台,缓坡,陡坡,悬崖}
    2 气象条件 {好,较好,较差,差}
    3 归一化植被指数 {好,中等,较差,差}
    4 高程 {平原,低丘,高丘,低山}
    5 人口密度 {好,较好,较差,差}
    6 坡向/(°) {45 ~ 135,315 ~ 45,
    135 ~ 225,225 ~ 315}
    7 水文条件 {富水性差,富水性较差,
    富水性较好,富水性好}
    8 公路距离 {远,较远,较近,近}
    9 工程地质条件 {碎屑岩类,花岗杂岩类、碳酸岩类,其它岩浆岩岩类,第四系松散土类、花岗岩类、片麻杂岩类}
    10 断裂距离 {远,较远,较近,近}
    11 水系距离 {远,较远,较近,近}
    12 铁路距离 {远,较远,较近,近}
    下载: 导出CSV

    表  3  大东山滑坡影响因子云模型评价值

    Table  3.   Cloud model Evaluation values of impact factors for Dadongshan landslide

    序号易发性因子影响因子云模型评价值
    1坡度(6.91,1.270,0.1)
    2气象条件(6.91,1.270,0.1)
    3归一化植被指数(10.00,1.031,0.1)
    4高程(10.00,1.031,0.1)
    5人口密度(10.00,1.031,0.1)
    6坡向(10.00,1.031,0.1)
    7水文条件(10.00,1.031,0.1)
    8公路距离(3.09,1.270,0.1)
    9工程地质条件(10.00,1.031,0.1)
    10断裂距离(10.00,1.031,0.1)
    11水系距离(3.09,1.270,0.1)
    12铁路距离(3.09,1.270,0.1)
    下载: 导出CSV

    表  4  总体评估等级云相似度表

    Table  4.   Cloud similarity table of overall evaluation grades

    云相似度高易发中易发低易发不易发
    大东山滑坡0.99900.99700.96600.1319
    下载: 导出CSV
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  • 收稿日期:  2022-09-25
  • 录用日期:  2023-08-23
  • 修回日期:  2023-07-17
  • 网络出版日期:  2023-08-30

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