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信息量法与随机森林耦合模型和临界月平均降雨阈值的区域滑坡危险性评价与区划以重庆市涪陵区为例

彭双庆, 刘朋飞, 陈刚, 王丽萍, 张伟, 罗文文, 景熙亮

彭双庆,刘朋飞,陈刚,等. 信息量法与随机森林耦合模型和临界月平均降雨阈值的区域滑坡危险性评价与区划−以重庆市涪陵区为例[J]. 中国地质灾害与防治学报,2025,36(1): 131-145. DOI: 10.16031/j.cnki.issn.1003-8035.202402015
引用本文: 彭双庆,刘朋飞,陈刚,等. 信息量法与随机森林耦合模型和临界月平均降雨阈值的区域滑坡危险性评价与区划−以重庆市涪陵区为例[J]. 中国地质灾害与防治学报,2025,36(1): 131-145. DOI: 10.16031/j.cnki.issn.1003-8035.202402015
PENG Shuangqing,LIU Pengfei,CHEN Gang,et al. Regional landslide hazard assessment using the IV-RF coupling model and critical monthly average rainfall threshold:A case study from Fuling District, Chongqing[J]. The Chinese Journal of Geological Hazard and Control,2025,36(1): 131-145. DOI: 10.16031/j.cnki.issn.1003-8035.202402015
Citation: PENG Shuangqing,LIU Pengfei,CHEN Gang,et al. Regional landslide hazard assessment using the IV-RF coupling model and critical monthly average rainfall threshold:A case study from Fuling District, Chongqing[J]. The Chinese Journal of Geological Hazard and Control,2025,36(1): 131-145. DOI: 10.16031/j.cnki.issn.1003-8035.202402015

信息量法与随机森林耦合模型和临界月平均降雨阈值的区域滑坡危险性评价与区划——以重庆市涪陵区为例

详细信息
    作者简介:

    彭双庆(1997—),男,四川成都人,资源与环境专业,硕士研究生,主要从事地质灾害风险研究。E-mail:1123327102@qq.com

    通讯作者:

    刘朋飞(1986—),男,河南许昌人,地质工程专业,博士,主要从事地质灾害防治研究。E-mail:273888264@qq.com

  • 中图分类号: P642.22;X43

Regional landslide hazard assessment using the IV-RF coupling model and critical monthly average rainfall threshold:A case study from Fuling District, Chongqing

  • 摘要:

    提高降雨型滑坡易发性预测精度和构建合适的降雨阈值模型对区域滑坡危险性评价具有重要意义。以重庆市涪陵区为例,采用信息量模型、BP神经网络模型、随机森林模型、信息量-BP神经网络耦合模型和信息量-随机森林耦合模型进行区域滑坡易发性评价,对比不同模型下的接受者操作特征曲线、曲线下方面积和易发性分布规律。提出滑坡临界月平均降雨阈值模型,反演出不同时间概率下的临界月平均降雨阈值。将易发性结果与时间概率等级进行耦合得到区域滑坡危险性评价结果并随机选取30次滑坡事件与4次典型滑坡事件进一步验证了评价精度。研究结果表明:信息量和机器学习模型进行耦合,弥补了机器学习在前期数据输入和非样本选择的缺点,提升了单一机器学习模型的预测精度,其中信息量-随机森林耦合模型预测精度最高;随机选取的30例滑坡样本中,有20例滑坡(占67%)位于发生时间概率50%以上区域,验证了临界月平均降雨阈值模型的精度;随机选取的4例典型滑坡样本中,时间概率等级基本为P4或P5,且位置均位于高危险区与极高危险区中,与现场调查结果基本一致,说明基于信息量-随机森林耦合模型和临界月平均降雨阈值的区域滑坡危险性评价结果准确且可靠。

    Abstract:

    Improving the accuracy of susceptibility prediction for rainfall-induced landslides and establishing suitable rainfall threshold models are of great significance for regional landslide hazard assessment. Taking Fuling District of Chongqing as a case study, the information value model, BP neural network model, random forest model, information value-BP neural network coupled model, and information value-random forest coupled model were used to evaluate regional landslide susceptibility. By comparing the receiver operating characteristic (ROC) curves, area under the curve (AUC), and susceptibility distribution patterns of different models, a critical monthly average rainfall threshold model for landslides is proposed, and critical monthly average rainfall thresholds for different temporal probabilities were inferred. The susceptibility results were coupled with temporal probability levels to produce regional landslide hazard assessment results. The evaluation accuracy is further validated with 30 randomly selected landslide events and 4 typical landslide cases. The results show that coupling the Information Value and machine learning models compensates for the shortcomings of machine learning in early data input and non-sample selection, enhancing the predictive accuracy of single machine learning models. Among these, the information value-random forest coupled model exhibits the highest predictive accuracy; of the 30 randomly selected landslide samples, 20 cases (67%) occurred in areas with a temporal probability of over 50%, validating the accuracy of the critical monthly average rainfall threshold model. The 4 typical landslide samples selected randomly were primarily in the P4 or P5 temporal probability levels and were located in high to very high-risk areas, aligning well with field survey results. This indicates that the regional landslide hazard assessment based on the information value-random forest coupled model and the critical monthly average rainfall threshold is accurate and reliable.

  • 图  1   区域滑坡危险性评价流程图

    Figure  1.   Flow chart for regional landslide risk assessment

    图  2   BPNN模型

    Figure  2.   Schematic diagram of the BP neural network model

    图  3   随机网络模型

    Figure  3.   Schematic diagram of the random forest model

    图  4   研究区地理位置及雨量站分布情况

    Figure  4.   Geographical location and rainfall stations distribution of the study area

    图  5   涪陵区滑坡相关评价因子图

    Figure  5.   Environmental assessment factors map of landslide in Fuling District

    图  6   负样本空间采样

    Figure  6.   Sketch map of negative sample space sampling

    图  7   各模型预测的滑坡易发性图

    Figure  7.   Landslide susceptibility maps predicted by various models

    图  8   4个模型的AUC

    Figure  8.   AUC value of four models

    图  9   各模型的频率比

    Figure  9.   Frequency ratio of each model

    图  10   雨量站多年月平均降雨量统计图

    Figure  10.   Statistical map of multi-year average monthly cumulative rainfall at rainfall stations

    图  11   斜坡单元月平均降雨量分布图

    Figure  11.   Distribution map of monthly average rainfall across slope units

    图  12   月平均降雨量与滑坡频率

    Figure  12.   Relationship between monthly average rainfall and landslide frequency

    图  13   滑坡累计占比曲线及月平均降雨量分级

    Figure  13.   Cumulative proportion curve of landslides and grading of monthly average rainfall

    图  14   评价模型精度验证

    Figure  14.   Validation of evaluation model accuracy

    图  15   时间概率等级依次为P1P5时的涪陵区滑坡危险性

    Figure  15.   Landslide hazard in Fuling District for temporal probability levels P1 to P5

    表  1   基于易发性与时间概率等级的区域滑坡危险性评价表

    Table  1   Regional landslide hazard assessment table based on susceptibility and temporal probability levels

    时间等级 易发性
    极低
    易发性

    易发性

    易发性

    易发性
    极高
    易发性
    P1(0<P(x)≤P1) 极低
    危险性
    极低
    危险性
    极低
    危险性
    极低
    危险性

    危险性
    P2(P1<P(x)≤P2) 极低
    危险性
    极低
    危险性

    危险性

    危险性

    危险性
    P3(P2<P(x)≤P3) 极低
    危险性

    危险性

    危险性

    危险性

    危险性
    P4(P3<P(x)≤P4) 极低
    危险性

    危险性

    危险性

    危险性
    极高
    危险性
    P5(P4<P(x)≤1)
    危险性

    危险性

    危险性
    极高
    危险性
    极高
    危险性
    下载: 导出CSV

    表  2   评价因子分级结果

    Table  2   The grading results of assessment factors

    评价
    因子
    分级 分级面积/km2 Si/S
    (×100)
    滑坡面积/km2 Ni/N
    (×100)
    I
    坡度/(°) 0~10 997.6 33.9 1.8 22.8 −0.4
    10~20 996.8 33.9 3.7 46.8 0.3
    20~30 629.7 21.4 2.0 25.3 0.2
    30~40 246.3 8.4 0.4 5.1 −0.5
    >40 71.8 2.4 0.0 0.0 −5.3
    坡向/
    (°)
    北(337.5~22.5) 168.2 5.7 0.3 3.8 −0.4
    东北(22.5~67.5) 67.7 2.3 1.5 19.0 2.1
    东(67.5~112.5) 320.3 10.9 1.0 12.7 0.2
    东南(112.5~157.5) 337.5 11.5 0.8 10.1 −0.1
    南(157.5~202.5) 294.5 10.0 0.8 10.1 0.0
    西南(202.5~247.5) 313.1 10.6 0.8 10.1 0.0
    西(247.5~292.5) 352.2 12.0 1.3 16.5 0.3
    西北(292.5~337.5) 400.8 13.6 1.1 13.9 0.0
    平面(−1) 687.7 23.4 0.3 3.8 −1.8
    曲率 <−9 100.9 3.4 0.0 0.0 −5.6
    −9~−6 172.7 5.9 0.1 1.3 −1.5
    −6~−3 1201.1 40.8 3.4 43.0 0.1
    −3~0 550.7 18.7 0.3 3.8 −1.6
    0~3 550.7 18.7 4.1 51.9 1.0
    3~6 176.0 6.0 0.0 0.0 −6.2
    6~12 92.9 3.2 0.0 0.0 −5.5
    >12 97.7 3.3 0.0 0.0 −5.6
    高程/
    m
    <200 129.4 4.4 1.9 24.1 1.7
    200~300 377.1 12.8 2.6 32.9 0.9
    300~400 449.6 15.3 1.6 20.3 0.3
    400~500 417.2 14.2 0.7 8.9 −0.5
    500~600 377.3 12.8 0.4 5.1 −0.9
    600~700 435.3 14.8 0.4 5.1 −1.1
    700~800 375.7 12.8 0.2 2.5 −1.6
    800~900 124.1 4.2 0.1 1.3 −1.2
    900~1000 72.7 2.5 0.0 0.0 −5.3
    >1000 184.1 6.3 0.0 0.0 −6.2
    地形湿度指数 <10 558.1 19.0 0.9 11.4 −0.5
    10~20 825.4 28.1 2.2 27.8 0.0
    20~30 449.1 15.3 1.6 20.3 0.3
    30~40 215.4 7.3 1.2 15.2 0.7
    40~50 108.0 3.7 0.5 6.3 0.5
    >50 786.5 26.7 1.5 19.0 −0.3
    地表粗
    糙度
    <1.1 2346.2 79.7 6.7 84.8 0.1
    1.1~1.2 400.9 13.6 1.0 12.7 −0.1
    1.2~1.3 120.8 4.1 0.2 2.5 −0.5
    1.3~1.4 55.9 1.9 0.0 0.0 −5.0
    1.4~1.5 15.8 0.5 0.0 0.0 −3.7
    >1.5 2.7 0.1 0.0 0.0 −2.0
    地层 梁山组+栖霞组+
    茅口组并层
    26.1 0.9 0.0 0.1 −1.9
    大冶组与嘉陵江
    组并层
    533.8 18.1 0.3 3.8 −1.6
    巴东组 212.6 7.2 1.0 12.7 0.6
    地层 吴家坪组与长兴
    组并层
    48.8 1.7 0.0 0.0 −4.9
    韩家店组 9.0 0.3 0.0 0.0 −3.2
    须家河组 137.7 4.7 0.4 5.1 0.1
    珍珠冲组 122.2 4.2 0.7 8.9 0.8
    自流井组 85.3 2.9 0.7 8.9 1.1
    新田沟组 85.4 2.9 0.2 2.5 −0.1
    沙溪庙组 841.7 28.6 2.4 30.4 0.1
    龙马溪组与小河坝组并层 2.2 0.1 0.0 0.0 −1.8
    蓬莱镇组 367.7 12.5 0.0 0.5 −3.2
    遂宁组 469.8 16.0 2.2 27.8 0.6
    岩层倾
    角/(°)
    <10 632.0 21.5 1.8 22.8 0.1
    10~20 1096.4 37.3 3.9 49.4 0.3
    20~30 443.6 15.1 1.1 13.9 −0.1
    30~40 364.9 12.4 0.6 7.6 −0.5
    40~50 342.2 11.6 0.5 6.3 −0.6
    50~60 58.2 2.0 0.0 0.0 −5.1
    >60 5.2 0.2 0.0 0.0 −2.6
    岩层倾
    向/(°)
    北(337.5~22.5) 376.3 12.8 1.0 12.9 0.0
    东北(22.5~67.5) 319.5 10.8 0.7 8.5 −0.2
    东(67.5~112.5) 367.2 12.5 1.0 12.5 0.0
    东南(112.5~157.5) 364.8 12.4 0.8 9.6 −0.3
    南(157.5~202.5) 305.1 10.3 0.5 6.6 −0.4
    西南(202.5~247.5) 313.5 10.6 1.5 18.4 0.6
    西(247.5~292.5) 427.1 14.5 1.5 18.7 0.3
    西北(292.5~337.5) 476.2 16.1 1.0 12.8 −0.2
    距断层距离/km <1 101.2 3.4 0.5 6.3 0.6
    1~2 111.9 3.8 0.6 7.6 0.7
    2~3 128.4 4.4 0.2 2.5 −0.5
    3~4 141.3 4.8 0.4 5.1 0.1
    4~5 154.4 5.2 0.3 3.8 −0.3
    >5 2305.2 78.3 5.9 74.7 0.0
    距水系距离/m 0~200 173.6 5.9 2.6 32.4 1.7
    200~400 102.1 3.5 1.2 14.9 1.5
    400~600 104.7 3.6 0.2 2.5 −0.4
    600~800 94.5 3.2 0.2 2.9 −0.1
    800~1000 102.9 3.5 0.2 2.9 −0.2
    10001200 84.8 2.9 0.1 0.9 −1.2
    12001400 100.2 3.4 0.1 0.8 −1.4
    14001600 90.9 3.1 0.7 8.9 1.1
    >1600 2087.6 71.0 2.7 33.9 −0.7
    距道路距离/m 0~200 310.1 10.5 2.1 28.5 1.0
    200~400 197.5 6.7 0.5 6.6 0.0
    400~600 177.1 6.0 0.8 11.2 0.6
    600~800 144.4 4.9 0.4 5.1 0.0
    800~1000 140.5 4.8 0.2 2.2 −0.8
    10001200 110.4 3.8 0.2 2.9 −0.3
    12001400 122.2 4.2 0.3 4.0 0.0
    >1400 1739.1 59.1 3.4 44.9 −0.3
    土层厚
    度/m
    0~2.5 2503.5 85.1 4.0 50.6 −0.5
    2.5~5 272.4 9.3 1.3 16.5 0.6
    5~7.5 30.5 1.0 0.9 11.4 2.4
    7.5~10 69.8 2.4 0.9 11.4 1.6
    10~12.5 18.6 0.6 0.2 2.5 1.4
    12.5~15 19.6 0.7 0.2 2.5 1.3
    15~17.5 13.8 0.5 0.4 5.1 2.4
    17.5~20 9.3 0.3 0.0 0.0 −3.2
    >20 5.2 0.2 0.0 0.0 −2.6
    归一化植被指数 <−0.1 53.1 1.8 0.6 7.6 1.4
    −0.1~0 15.8 0.5 0.2 2.5 1.5
    0~0.1 24.1 0.8 0.1 1.3 0.4
    0.1~0.2 62.9 2.1 0.1 1.3 −0.5
    0.2~0.3 135.6 4.6 0.5 6.3 0.3
    0.3~0.4 720.1 24.5 1.7 21.5 −0.1
    0.4~0.5 1624.4 55.2 3.7 46.8 −0.2
    >0.5 306.1 10.4 1.0 12.7 0.2
    土地利用类别 耕地 1452.9 49.4 5.3 67.5 0.3
    林地 726.6 24.7 0.4 4.7 −1.7
    草地 512.1 17.4 1.3 16.6 −0.1
    灌木地 67.1 2.3 0.1 1.2 −0.7
    湿地 0.3 0.0 0.0 0.1 2.1
    水体 88.7 3.0 0.0 0.1 −3.1
    人造地表 94.4 3.2 0.8 9.8 1.1
    下载: 导出CSV

    表  3   雨量站月平均降雨量

    Table  3   Average monthly rainfall of rainfall stations

    雨量站月平均降雨量/mm
    长寿95.6
    涪陵91.4
    丰都91.6
    凤来106.4
    雨台山81.9
    睦和87.5
    双河口104.2
    大木117.6
    河图75.6
    武陵山89.3
    龙潭102.5
    下载: 导出CSV

    表  4   时间概率等级与月平均降雨量关系表

    Table  4   Relationship between temporal probability levels and monthly average rainfall

    时间概率等级 时间概率 月平均降雨量/mm
    P1 0<P(x)≤0.1 <78
    P2 0.1<P(x)≤0.25 78~87
    P3 0.25<P(x)≤0.5 87~96
    P4 0.5<P(x)≤0.75 96~106
    P5 0.75<P(x)≤1 >106
    下载: 导出CSV

    表  5   30次滑坡样本发生时间概率等级统计表

    Table  5   Temporal probability levels of occurrence for 30 landslide samples

    时间概率等级 P1 P2 P3 P4 P5
    样本数 2 2 6 12 8
    下载: 导出CSV

    表  6   4次典型滑坡信息

    Table  6   Information on four typical landslides

    滑坡事件 日期 月平均降雨量/mm 时间概率等级
    滑坡1 2020-06-29 98.7 P4
    滑坡2 2018-08-01 103.4 P4
    滑坡3 2020-06-27 118.2 P5
    滑坡4 2016-06-02 121.3 P5
    下载: 导出CSV
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出版历程
  • 收稿日期:  2024-02-25
  • 修回日期:  2024-05-27
  • 录用日期:  2024-07-02
  • 网络出版日期:  2024-07-23
  • 刊出日期:  2025-02-24

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