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“23•7”特大暴雨引发门头沟区地质灾害发育特征及成因分析

李华东, 邵旭升, 冯少华

李华东,邵旭升,冯少华. “23•7”特大暴雨引发门头沟区地质灾害发育特征及成因分析[J]. 中国地质灾害与防治学报,2025,36(0): 1-9. DOI: 10.16031/j.cnki.issn.1003-8035.202412022
引用本文: 李华东,邵旭升,冯少华. “23•7”特大暴雨引发门头沟区地质灾害发育特征及成因分析[J]. 中国地质灾害与防治学报,2025,36(0): 1-9. DOI: 10.16031/j.cnki.issn.1003-8035.202412022
LI Huadong,SHAO Xusheng,FENG Shaohua. Development characteristics and mechanism of geological hazards in Mentougou District triggered by “23•7” torrential rain[J]. The Chinese Journal of Geological Hazard and Control,2025,36(0): 1-9. DOI: 10.16031/j.cnki.issn.1003-8035.202412022
Citation: LI Huadong,SHAO Xusheng,FENG Shaohua. Development characteristics and mechanism of geological hazards in Mentougou District triggered by “23•7” torrential rain[J]. The Chinese Journal of Geological Hazard and Control,2025,36(0): 1-9. DOI: 10.16031/j.cnki.issn.1003-8035.202412022

“23•7”特大暴雨引发门头沟区地质灾害发育特征及成因分析

详细信息
    作者简介:

    李华东(1984—),男,湖北麻城人,地质工程专业,硕士,高级工程师,主要从事工程地质、水文地质及岩土工程方面的工作。E-mail:sccdut@qq.com

  • 中图分类号: P694

Development characteristics and mechanism of geological hazards in Mentougou District triggered by “23•7” torrential rain

  • 摘要:

    “23•7”特大暴雨引发了1963年以来海河流域最大的洪水灾害,同时诱发了大量地质灾害,造成了房屋损毁、交通中断、人员伤亡和巨大的经济财产损失,北京市门头沟区受灾严重。本文基于灾后详细的地质灾害调查,共统计门头沟区新增地质灾害点353处,类型包括崩塌、滑坡、泥石流、地面塌陷等。分析了形成地质灾害的主要原因,包括降雨量大、坡面冲刷强、排水不畅、切坡修建房屋、房屋修建挤占河(沟)道、坡面防护差、岩土体松散破碎、坡面堆放杂物等。提出了山区村镇建设中防灾减灾的建议,要尊重自然,还河(沟)道生态空间,充分发挥弯曲河道的缓冲和生态屏障作用;要重视建设场地的选址及灾害评估,提高地质灾害防治工程的设计质量和防护等级;在类似的山区流域中要加强对地质灾害的调查、识别、风险评估、预警和防治,避免暴雨引起的地质灾害在类似的流域中再次发生。

    Abstract:

    The “23•7” torrential rain triggered the largest flood disaster in the Haihe River Basin since 1963, and led to numerous geological disasters, causing house damage, traffic disruption, and resulting in casualties and significant economic and property losses. The Mentougou District in Beijing has been severely affected by the disaster. Based on a detailed investigation of geological disasters after the torrential rain, this study counted a total of 353 new geological hazards in Mentougou District, including collapse, landslide, debris flow, and ground subsidence. The main contributing factors to the formation of geological hazards were analyzed, including heavy rainfall, intense slope erosion, inadequate drainage, slope cutting for construction, building on riverbanks or ditches, poor slope protection, loose and fractured rock and soil, and the accumulation of debris on slopes. Suggestions for hazards prevention and reduction in the construction of villages and towns have been proposed. These include following natural laws, restoring ecological space to rivers and ditches, and maximizing the buffering and ecological barrier functions of meandering rivers. Further emphasis should be placed on site selection and disaster risk assessment, along with improving the design quality and protective measures for geological disaster prevention projects. In similar Mountainous watersheds, it is necessary to strengthen the investigation, identification, risk assessment, early warning, and prevention of geological disasters to avoid the recurrence of such disasters in future rainstorm events.

  • 铁路边坡变形是引起滑坡和泥石流等众多地质灾害的直接原因,给工农业生产带来严重威胁,传统人工巡查的方式存在精度低和时效性差等问题[13]。北斗全球卫星导航系统(global navigation satellite system, GNSS)具有全天时、全天候、高精度和高可靠等优点,基于北斗GNSS建立铁路边坡变形在线监测系统能够有效弥补人工巡查的不足,不仅能够为铁路工作人员提供实时、高精度的变形监测数据,同时结合人工智能技术进行数据分析还可以实现变形趋势预测,为地质灾害发生提供提前预警服务,因此得到了广泛关注[48]。文献[9]系统地介绍了GNSS在滑坡监测中应用时面临的问题以及解决思路;文献[10]以陕西省周至G108路段滑坡为例,提出了一种基于GNSS的滑坡预警系统,利用线性预测模型对滑坡数据进行建模分析;文献[11]研究了复杂条件下北斗GNSS监测数据的异常值检测和变形趋势预测问题;文献[12]设计了一套自动化监测系统并将其应用到河南省某地边坡变形监测;文献[13]考虑了变形监测数据处理中存在噪声抑制问题,提出一种基于小波分析和经验模态分解的监测数据处理和变形趋势预测方法。从上述研究可以看出,目前铁路边坡变形监测主要涉及异常值处理,噪声抑制和变形趋势预测三个方面,而当前已有研究均仅考虑了其中的一个或两个方面,尚未有文献系统性的考虑上述三个方面问题。

    在上述研究的基础上,本文以朔黄铁路北斗GNSS边坡变形在线监测系统为例,针对监测数据处理中异常值检测和修正、噪声抑制以及变形趋势预测开展研究,提出一套数据分析处理方法并进行应用验证。

    利用北斗GNSS系统进行变形监测的原理是根据基准站与不同监测点之间的相对位置关系确定各个监测点位移随时间的变化,通过对位移变化进行解算获得监测点处毫米级精度的位移变化信息。然而,由于部分铁路边坡建设环境较为复杂,施工环境较为恶劣,自动化监测数据中不可避免地会出现偶然误差,因此不能直接利用监测数据进行建模和变形趋势预测,首先需要对数据进行异常值检测和异常值修正预处理,以提升数据质量和后续预测性能。

    3σ准则即莱茵达准则,是一种基于统计学原理实现数据离群点判别的经典方法,其基本思想是根据待检测样本取值与集合中所有样本的均值和均方根的偏差程度判断样本是否为离群点,对于变形监测数据时间序列$ {\boldsymbol{x}} = {\left[ {{x_1},{x_2}, \cdots ,{x_N}} \right]^{\mathrm{T}}} $,$ {x_n},n = 1,2, \cdots ,N $为第$ n $个采样点,$ N $为数据总长度。利用3σ准则对其进行异常值检测首先需要计算新的差值序列($ {d_n} $):

    $$ {d_n} = 2{x_n} - \left( {{x_{n + 1}} + {x_{n - 1}}} \right) $$ (1)

    对于所有$ N $个采样点,分别按照式(1)计算差值序列,可以得到$ N - 2 $个$ {d_n} $,按照式(2)和式(3)分别计算$ {d_n} $的均值($\overline d $)和均方根($\sigma $):

    $$ \overline d = \sum\limits_{n = 2}^{N - 2} {\frac{{{d_n}}}{{N - 2}}} $$ (2)
    $$ \sigma = \sqrt {\sum\limits_{n = 2}^{N - 2} {\frac{{{{\left( {{d_n} - \overline d } \right)}^2}}}{{N - 2}}} } $$ (3)

    进而计算$ {d_n} $相对于$ \overline d $的偏离程度,若$ \left| {{d_n} - \overline d } \right| > 3\sigma $,则判定对应的$ {x_{n + 1}} $为异常值。反之,若$ \left| {{d_n} - \overline d } \right| \leqslant 3\sigma $,判定对应的$ {x_{n + 1}} $为正常值。

    基于3σ准则实现异常值检测后,需要对其进行修正以保持数据的连续性,便于后续建模分析。目前常用的拉格朗日插值,三阶样条插值等方法存在数据适应性差和不稳定的问题。卡尔曼滤波是一种最小均方误差准则下的最优滤波模型,适合于对非平稳、非线性时间序列进行平滑滤波,并且只需要前一个时刻的观测值和当前时刻的估计值即可获得最优估计,具有高精度和运算简单的特点,适合对监测数据异常值进行实时修正。

    对于监测数据时间序列$ {\boldsymbol{x}} = {\left[ {{x_1},{x_2}, \cdots ,{x_N}} \right]^{\mathrm{T}}} $,其状态方程和观测方程可以表示为:

    $$ \left\{ \begin{gathered} {x_n} = {\boldsymbol{A}}{\hat x_{n - 1}} + {\boldsymbol{B}}{\omega _{n - 1}} \\ {\textit{z}_n} = {\boldsymbol{H}}{\hat x_n} + {v_n} \\ \end{gathered} \right. $$ (4)

    式中:$ {\boldsymbol{A}} $——状态转移矩阵;

    $ {\boldsymbol{B}} $——系统控制矩阵;

    $ {\boldsymbol{H}} $——量测矩阵;

    $ {\hat x_{n - 1}} $、$ {\hat x_n} $——第$n - 1 $、$ n $个采样点的估计值;

    $ {v_n} $——观测噪声,服从零均值,协方差矩阵为$ {{\boldsymbol{R}}_n} $的高斯分布;

    $ {\omega _{n - 1}} $——系统噪声,服从零均值,协方差矩阵为$ {{\boldsymbol{Q}}_n} $ 的高斯分布。

    得到状态方程和观测方程后,卡尔曼滤波按照式(5)所示对模型进行更新迭代:

    $$ \left\{ \begin{gathered} {{\boldsymbol{K}}_n} = {\boldsymbol{P}}_n^ - {\boldsymbol{H}}_{}^{\mathrm{T}}{\left( {{\boldsymbol{HP}}_n^ - {\boldsymbol{H}}_{}^{\mathrm{T}} + {{\boldsymbol{R}}_n}} \right)^{ - 1}} \\ \hat x_n{{ = }}\hat x_{n - 1}{{ + }}{{\boldsymbol{K}}_n}\left( {{\textit{z}_n} - {\boldsymbol{H}}\hat x_n} \right) \\ {{\boldsymbol{P}}_n}{\text{ = }}\left( {{{1 - }}{{\boldsymbol{K}}_n}{\boldsymbol{H}}} \right){\boldsymbol{P}}_n^ {-1} \\ \end{gathered} \right. $$ (5)

    式中:$ {{\boldsymbol{K}}_n} $——滤波增益;

    $ {{\boldsymbol{P}}_n} $——状态估计误差协方差矩阵;

    $ {\boldsymbol{P}}_n^{ - 1} $——误差协方差矩阵估计值。

    卡尔曼滤波迭代终止时异常值位置对应的采样点即为修正后的样本值$ \hat {\boldsymbol{x}} = {\left[ {\hat x_1^{},\hat x_2^{}, \cdots ,\hat x_N^{}} \right]^{\mathrm{T}}} $。

    铁路变形通常是由渐变慢慢发展到突变的一个缓慢过程,在渐变过程中,监测到的变形数据变化较小,容易受到接收机噪声和环境噪声的影响,导致后续变形趋势信息提取难度增加,因此进行监测数据处理过程中,噪声抑制是一个关键环节。目前常用的小波变换和主成分分析等方法存在运算复杂,小波基函数和主分量个数等模型参数确定困难等问题。

    CLEAN算法最早由Hogbom于1974年为了提升合成孔径雷达成像质量而提出,随着研究的深入,人们发现除了抑制旁瓣,提升图像质量外,CLEAN算法在噪声抑制方面也表现出了独特的优势,并且具有运算量小和实时性高的特点。因此本文将CLEAN算法引入变形监测数据处理领域,利用CLEAN算法对异常值修正后的监测数据$ \hat {\boldsymbol{x}} = {\left[ {\hat x_1^{},\hat x_2^{}, \cdots ,\hat x_N^{}} \right]^{\mathrm{T}}} $进行噪声抑制,具体步骤为:

    步骤1:对$ \hat {\boldsymbol{x}} $进行傅里叶变换,将其转换至频域,得到频谱${\boldsymbol{X}} = FFT\left( {\hat {\boldsymbol{x}}} \right)$,其中$ {\text{FFT}}\left( {} \right) $表示对括号内变量进行快速傅里叶变换算子。

    步骤2:找出${\boldsymbol{X}}$幅度最大值对应的频率${f_1}$,相位${\varphi _1}$和幅度${\rho _1}$。

    步骤3:根据式(6)得到第一个谐波分量为:

    $$ {{\boldsymbol{s}}_1} = \frac{{{\rho _1}}}{T}\exp \left[ {j(2\text{π} {f_1}t + {\varphi _1})} \right],t = 1,2, \cdots ,T $$ (6)

    式中:$ t $——采样时刻;

    $ T $——总采样时间。

    步骤4:从原始数据中减去$ {{\boldsymbol{s}}_1} $,得到剩余数据$ {{\boldsymbol{s}}_r} = \hat {\boldsymbol{x}} - {{\boldsymbol{s}}_1} $。

    步骤5:将$ {{\boldsymbol{s}}_r} $作为初始信号,重复步骤1—步骤4,依次提取原始数据中的所有谐波分量,直到满足迭代终止条件。

    步骤6:将所有谐波分量相加,得到噪声抑制后的变形监测数据:$ {{\boldsymbol{s}}} = {\left[ {{s_1},{s_2}, \cdots ,{s_N}} \right]^{\mathrm{T}}} $。

    铁路边坡变形趋势预测的目的通过对在线监测系统记录的变形历史数据进行分析,建立能够描述其未来发展趋势的数学模型,从而在地质灾害发生之前进行预警,最大程度地减少地质灾害带来的损失。因此,完成数据预处理和噪声抑制后,需要建立变形趋势预测模型。

    RBF神经网络是一种具备自学习和自适应能力的前向反馈神经网络模型,理论上能够高精度逼近于任意非线性函数,因此适合于对非线性、非平稳铁路边坡变形过程进行建模。典型的RBF神经网络结构如图1所示,由输入层,中间层和输出层构成,其中输入层与中间层,中间层与输出层之间实现了全连接,而网络同一层神经元之间不连接。输入层神经元通过径向基函数映射到中间层神经元,中间层神经元又通过权向量向输出层神经元映射。上述过程可以总结为:①初始化RBF神经网络输入层、中间层和输出层神经元。其中输入层神经元为噪声抑制后的监测数据$ {{\boldsymbol{s}}} = {\left[ {{s_1},{s_2}, \cdots ,{s_N}} \right]^{\mathrm{T}}} $,中间层神经元为$ {\boldsymbol{c}} = {\left[ {{c_1},{c_2}, \cdots ,{c_J}} \right]^{\mathrm{T}}} $,$ J $为中间层神经元个数,输出层神经元为未来变形趋势预测数据$ {\boldsymbol{y}} = [ {y_1}, {y_2}, \cdots ,{y_M} ]^{\mathrm{T}} $,$ M $为预测数据期数。②利用RBF神经网络将输入层映射至中间层:$ {{{\boldsymbol{z}}} _j} = {\text{exp}}\left( { - \dfrac{{\left\| {{\boldsymbol{s}} - {c_j}} \right\|}}{{2\sigma _j^2}}} \right) $,$ \sigma _j^{} $为RBF神经网络核参数;③利用权向量$ {\boldsymbol{w}} $对中间层神经元进行线性加权,得到网络输出层$ {y_m} = \displaystyle\sum\limits_{j = 1}^J {{w_{mj}}{\textit{z}_j}} $;④将网络输出神经元取值与预期神经元取值之间的均方误差作为代价函数,利用梯度下降法对代价函数求解,得到最优的网络权向量$ {\boldsymbol{w}} $,从而获得最优RBF网络模型。

    图  1  RBF神经网络模型结构
    Figure  1.  RBF neural network model structure

    图2给出了本文所提铁路边坡变形监测系统数据处理流程,对于输入的变形监测数据,首先利用3σ准则实现对监测数据中的异常值检测,然后利用卡尔曼滤波算法对监测数据进行滤波平滑,实现异常值修正。针对监测数据的噪声抑制问题,利用CLEAN算法对卡尔曼滤波后的数据进行分析,提取其中包含变形信息的谐波分量。最后利用RBF神经网络对噪声抑制后的监测数据进行建模分析,提取其中包含的变形趋势信息并实现对未来变形发展趋势的高精度预测。

    图  2  所提方法流程图
    Figure  2.  Flowchart of proposed method for railway slope deformation monitoring system data processing

    为了定量评估数据处理方法的性能,采用变形趋势预测结果与实际变形数据的平均相对误差(mean relative error,MRE)和均方根误差(root mean square error,RMSE)作为评估指标。具体定义为:

    $$ {{MRE}} = \frac{1}{M}\sum\limits_{m = 1}^M {\frac{{\left| {{y_m} - {{\overline y }_m}} \right|}}{{{y_m}}}} $$ (7)
    $$ {{RMSE}} = \sqrt {\frac{1}{M}\sum\limits_{m = 1}^M {{{\left( {{y_m} - {{\overline y }_m}} \right)}^2}} } $$ (8)

    式中:${y_m}$——第$m$期边坡位移预测值;

    ${\overline y _m}$——对应的实测值。

    朔黄铁路是神黄铁路的重要组成部分,是我国投资与建设规模最大的双线电气化合资铁路,也是我国西煤东运第二大通道,在全国路网中占有重要地位。朔黄铁路K237+208~K237+623段线路出露地层主要由砂质黄土和二叠系长石石英砂岩夹泥岩组成,黄土厚度为5~20 m,黄土层和基岩形成了坡体的土石分层面,在坡体中基岩裂隙水和入渗雨水的作用下,在该土石分层面易形成软弱夹层,降低坡体的稳定性,导致边坡处发生裂隙和脱空,护坡面形成鼓胀和勾缝,在水分、震动等长期作用下易失稳产生溜坍、滑坡等现象。本文基于朔黄铁路边坡在线监测系统数据开展验证试验。该边坡共设置8个点位进行变形监测,监测点位平面布置图如图3所示。

    图  3  监测点位布置平面图
    Figure  3.  Layout plan of monitoring points

    试验中选取上行K237+384左坡坡顶监测点2019年1月至2021年12月的36期变形监测数据进行分析研究,变形监测数据变化曲线如图4所示。可以看出,曲线变化较为剧烈,波动性较大,给数据处理和建模分析带来了较大难度。图5给出了利用3σ准则得到监测数据异常值检测结果,可以看出,3σ准则检测得到了5期采样异常数据,分别为第9期、第10期、第24期、第28期和第33期数据,对其取值进行分析可知,5期异常数据取值相对于监测数据均值偏差均超过3σ,与理论模型一致。

    图  4  变形监测数据
    Figure  4.  Deformation monitoring data
    图  5  监测数据异常值检测结果
    Figure  5.  Detection results of abnormal values in monitoring data

    利用卡尔曼滤波对图5所示异常值检测结果进行异常值修正得到的结果如图6所示,对图6所示数据进行噪声抑制得到的结果如图7所示。可以看出,经过卡尔曼滤波和噪声抑制后,监测数据变化曲线能够很好地反映铁路边坡变形趋势,开始监测时变形趋势增长较快,之后进入平缓期,变形趋势不明显,而后变形趋势逐渐增大,结合当地气候数据进行分析可知,变形趋势增大是由于当地气候变化较为恶劣导致。相对于原始数据,经过异常值修正和噪声抑制后得到的监测数据变化曲线较为平滑,更利于后续进行变形趋势建模预测,提升预测精度。

    图  6  监测数据异常值修正结果
    Figure  6.  Correction results of abnormal values in monitoring data
    图  7  监测数据噪声抑制结果
    Figure  7.  Monitoring data noise suppression results

    为了进一步对所提方法的变形趋势预测性能进行验证,根据图2所示流程,对变形监测数据进行数据集划分,将前30期数据作为训练样本,用于实现RBF神经网络模型参数的学习,剩余6期数据作为测试样本,对最优RBF神经网络模型的预测性能进行验证,图8给出了变形趋势预测结果。同时为了对比,图8中一并给出了在相同条件下分别采用文献[11]所提一阶灰色理论模型(grey model, GM(1,1))和文献[13]所提小波变换方法得到的结果,图9给出了三种方法得到结果的预测误差。表1给出了三种方法预测性能的定量分析结果。

    图  8  变形趋势预测结果
    Figure  8.  Prediction results of deformation trend
    图  9  不同方法预测误差
    Figure  9.  Prediction error of different methods
    表  1  不同方法预测结果对比
    Table  1.  Comparison of prediction results using different methods
    GM(1,1)小波变换所提方法
    MRE0.680.270.12
    RMSE0.870.520.23
    下载: 导出CSV 
    | 显示表格

    图8图9所示结果可以看出,对于测试集合的每一期样本,所提方法均能获得最高的预测精度,同时对于6期测试数据获得的预测误差差别不大,而2种对比方法对于第1—3期测试数据的预测误差均较大,对于第4—6期测试数据的预测误差明显减小,表明所提方法的模型收敛速度较快,只需要少量样本即可获得较高的预测精度,而2种对比方法的模型收敛速度较慢,实时性较差。表1给出的结果与图8图9一致,所提方法在MRE指标方面相对于GM(1,1)提升超过82.4%,相对于小波方法提升超过55.6%,在RMSE指标方面相对于GM(1,1)提升超过73.6%,相对于小波方法提升超过55.8%,究其原因在于,GM(1,1)作为一种线性模型,预测性能对噪声敏感,因此低信噪比条件下性能有限,而小波方法虽然能够实现噪声抑制,但是监测数据中异常值的存在会导致分解性能下降从而影响后续预测性能。所提方法综合考虑了监测数据中异常值和噪声分量的影响,结合RBF神经网络分线性数据拟合能力,故而可以获得最优的预测性能。

    (1) 监测数据中异常值存在会影响数据分析以及变形信息提取,提出利用3σ准则进行异常值检测并利用卡尔曼滤波进行异常值修正的方法能够有效解决异常值处理问题,同时可以保持数据完整性;

    (2) CLEAN算法能够在实现噪声抑制的同时有效提取监测数据中的变形趋势信息,提升后续变形趋势预测精度;

    (3) 基于朔黄铁路K237+208—K237+623段线路边坡变形监测系统实际数据的试验结果表明,所提方法的变形趋势预测精度高,能够较好地反映实际工况中的变形趋势信息,具有较高的应用价值。

  • 图  1   “23•7”特大暴雨北京市降水量分布图

    Figure  1.   Distribution of precipitation in Beijing during the "23•7" torrential rain

    图  2   暴雨后地质灾害点分布图

    Figure  2.   Distribution of geological hazards after rainstorm

    图  3   暴雨前后各乡镇/街道地质灾害发育对比图

    Figure  3.   Comparison of geological hazards development in Towns/Streets before and after rainstorm

    图  4   大台街道西洼村、斋堂镇新兴村受损房屋

    Figure  4.   Damaged houses in Xiwa Village in Datai Street and Xinxing Village in Zhaitang Town

    图  5   清水镇达摩庄村、王平镇西马各庄村

    Figure  5.   Damozhuang Village in Qingshui Town and Ximagezhuang Village in Wangping Town

    图  6   东辛房街道西山印工地及西山炮楼地基冲刷情况

    Figure  6.   Scouring of the Xishanyin construction site and Xishanpaolou foundation in Dongxinfang Street

    图  7   坡面冲刷及基坑淤积地质剖面

    Figure  7.   Geological profile of slope erosion and foundation pit sedimentation

    图  8   雁翅镇高台村路基冲刷、王平镇南港村房基冲刷

    Figure  8.   Subgrade erosion in Gaotai Village in Yanchi Town and scouring of house foundation in Nangang Village in Wangping Town

    图  9   完工后的拦挡坝和排导槽[20]

    Figure  9.   Retaining dam and guide groove after completion

    图  10   暴雨后的拦挡坝和排导槽

    Figure  10.   Retaining dam and guide groove after rainstorm

    图  11   清水镇江水河村、上清水村

    Figure  11.   Jiang Shuihe and Shangqingshui Village in Qingshui Town

    图  12   清水镇梁家庄村干砌石墙垮塌、雁翅镇泗水村挡墙无排水孔

    Figure  12.   Dry stone wall collapse in Liangjiazhuang Village in Qingshui Town and Retaining wall without drainage holes in Sishui Village in Yanchi Town

    图  13   王平镇南港村沟道内房屋及被泥石流淤埋的房屋

    Figure  13.   Houses in the gully buried by debris flow in Nangang Village in Wangping Town

    图  14   潮白河流域示意图

    Figure  14.   Schematic diagram of Chaobaihe watershed

    表  1   门头沟区“23•7”暴雨前后地质灾害隐患点统计表

    Table  1   Statistics of geological hazards before and after the “23•7” torrential rain in Mentougou District

    乡镇隐患点数崩塌滑坡泥石流地面塌陷
    暴雨前暴雨后新增暴雨前暴雨后新增暴雨前暴雨后新增暴雨前暴雨后新增暴雨前暴雨后新增
    城子街道31112
    大台街道878797216
    大峪街道26112112
    东辛房街道1225117
    军庄镇1341021211
    龙泉镇5841552871323
    妙峰山镇1715917053214
    清水镇213401953421642
    潭柘寺镇1564714724373163
    王平镇1263811129128121
    雁翅镇27072262581678
    永定镇1981522611
    斋堂镇2002717121165131
    总计133035312222607335153507
    下载: 导出CSV

    表  2   暴雨前、后各类地质灾害统计表

    Table  2   Statistics of various geological hazards before and after rainstorm

    灾害类型 暴雨前潜在发育 暴雨后形成 触发比例/%
    崩塌 1222 260 21.28
    滑坡 7 33 471.43
    泥石流 51 53 103.92
    地面塌陷 50 7 14.00
    合计 1330 353 26.54
    下载: 导出CSV
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    1. 孟迎. 考虑降雨量数值变化的山洪灾害动态预警方法研究. 水资源开发与管理. 2024(03): 73-76+56 . 百度学术

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