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    基于变系数回归模型的三峡库区滑坡位移预测

    喻孟良 梅红波 李冀骅 赵慧 吴润泽

    喻孟良, 梅红波, 李冀骅, 赵慧, 吴润泽, 2016. 基于变系数回归模型的三峡库区滑坡位移预测. 地球科学, 41(9): 1593-1602. doi: 10.3799/dqkx.2016.118
    引用本文: 喻孟良, 梅红波, 李冀骅, 赵慧, 吴润泽, 2016. 基于变系数回归模型的三峡库区滑坡位移预测. 地球科学, 41(9): 1593-1602. doi: 10.3799/dqkx.2016.118
    Yu Mengliang, Mei Hongbo, Li Jihua, Zhao Hui, Wu Runze, 2016. Landslide Displacement Prediction Based on Varying Coefficient Regression Model in Three Gorges Reservoir Area. Earth Science, 41(9): 1593-1602. doi: 10.3799/dqkx.2016.118
    Citation: Yu Mengliang, Mei Hongbo, Li Jihua, Zhao Hui, Wu Runze, 2016. Landslide Displacement Prediction Based on Varying Coefficient Regression Model in Three Gorges Reservoir Area. Earth Science, 41(9): 1593-1602. doi: 10.3799/dqkx.2016.118

    基于变系数回归模型的三峡库区滑坡位移预测

    doi: 10.3799/dqkx.2016.118
    基金项目: 

    三峡库区地质灾害预警指挥系统数据仓库及管理系统建设项目 SXJC-3ZH1B1

    详细信息
      作者简介:

      喻孟良(1978-),男,博士,高级工程师,主要从事水工环地质信息化工作.E-mail:yuml@mail.cigem.gov.cn

      通讯作者:

      梅红波,E-mail:hbmei@cug.edu.cn

    • 中图分类号: P694

    Landslide Displacement Prediction Based on Varying Coefficient Regression Model in Three Gorges Reservoir Area

    • 摘要: 降雨-库水联合作用影响着三峡库区滑坡,而降雨、库水分别对滑坡演化的贡献及作用规律迄今尚不明确.以库区树坪滑坡和八字门滑坡为例,通过分析降雨和库水位资料,采用变系数回归模型,对滑坡位移进行预测.实验结果表明:经过改进的变系数回归模型方法不仅比传统的线性回归模型、自回归积分滑动平均模型、支持向量机模型方法具有更高的预测精度,而且能定量地给出各影响因素对滑坡位移的贡献.

       

    • 图  1  树坪滑坡GPS监测点布置平面图(a)和工程地质剖面(b)

      来源于三峡库区地质灾害防治工作指挥部,经过适当修改整饰

      Fig.  1.  Layout of GPS monitoring points (a) and geological profile (b) of Shuping landslide

      图  2  八字门滑坡GPS监测点布置平面(a)和工程地质剖面(b)

      来源于三峡库区地质灾害防治工作指挥部,经过适当修改整饰

      Fig.  2.  Layout of GPS monitoring points (a) and geological profile (b) of Bazimen landslide

      图  3  监测点累计位移与相对位移

      Fig.  3.  Cumulative displacement and relative displacement of monitoring points

      图  4  降雨与监测点相对位移的关系

      Fig.  4.  The relationship between rainfall and relative displacement of monitoring points

      图  5  库水位差与监测点相对位移的关系

      Fig.  5.  The relationship between water level difference and relative displacement of monitoring points

      图  6  监测点累计位移真实值与拟合/预测值对比

      Fig.  6.  The comparison of true values and fitting/predicted values of monitoring points

      图  7  各自变量因素的系数变化

      Fig.  7.  Coefficient changes of each independent variable factor

      图  8  各自变量因素系数变化及预测值

      Fig.  8.  Predicted value and coefficient changes of each independent variable factor

      表  1  变系数回归模型和改进的变系数回归模型预测结果

      Table  1.   Prediction results from varying coefficient regression and improved varying coefficient regression

      日期变系数回归改进的变系数回归
      原始值(mm)预测值(mm)误差(%)原始值(mm)预测值(mm)误差(%)
      ZG85
      2010-11-122 330.82 316.532-0.612 162 330.82 325.660 762-0.220 490
      2010-12-112 339.32 321.932-0.742 432 339.32 344.587 6960.226 038
      2011-01-142 349.92 316.524-1.420 302 349.92 355.631 2930.243 895
      2011-02-212 369.62 330.334-1.657 082 369.62 389.211 4970.827 629
      2011-03-132 390.52 337.750-2.206 662 390.52 419.628 2011.218 498
      2011-04-092 420.12 344.962-3.104 772 420.12 451.231 3681.286 367
      2011-05-122 493.82 389.472-4.183 512 493.82 519.672 3821.037 468
      2011-06-122 653.82 435.680-8.219 182 653.82 596.337 785-2.165 280
      ZG111
      2008-01-11662.4672.041 21.455 49662.4672.410 51.511 24
      2008-02-16668.0666.478 6-0.227 76668.0666.906 2-0.163 74
      2008-03-11672.2660.134 2-1.794 98672.2660.290 9-1.771 66
      2008-04-10671.8660.483 5-1.684 50671.8660.183 1-1.729 22
      2008-05-12683.8681.754 7-0.299 11683.8680.924 2-0.420 56
      2008-06-18691.6691.406 7-0.027 96691.6690.482 8-0.161 54
      2008-07-13706.0709.959 60.560 86706.0709.399 20.481 47
      2008-08-15719.9743.671 23.302 01719.9744.446 63.409 72
      2008-09-17816.9789.395 1-3.366 99816.9792.501 8-2.986 68
      2008-10-20826.2816.195 9-1.210 86826.2822.810 1-0.410 30
      2008-11-21823.7827.996 70.521 63823.7838.704 71.821 62
      2008-12-21828.4826.307 4-0.252 61828.4838.750 11.249 41
      2009-01-08831.2821.230 5-1.199 41831.8831.200 0-0.081 76
      下载: 导出CSV

      表  2  监测点5种模型的预测对比

      Table  2.   Prediction and comparison of five models

      模型ZG85ZG111
      最大误差最小误差平均绝对误差均方根误差最大误差最小误差平均绝对误差均方根误差
      ARIMA4.090.882.2961.747.510.2104.6036.43
      SVR6.800.381.7068.046.800.3801.7068.04
      线性回归3.240.251.2537.217.900.2601.3619.07
      变系数回归8.210.612.7693.573.360.0271.2212.19
      改进的变系数回归1.280.220.6319.233.400.0801.2412.22
      下载: 导出CSV
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    • 收稿日期:  2016-01-22
    • 刊出日期:  2016-09-15

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