基于时间序列与混合核函数SA-SVR的滑坡位移预测模型研究

尚敏 熊德兵 张惠强 赵国飞

尚敏, 熊德兵, 张惠强, 等. 2022. 基于时间序列与混合核函数SA-SVR的滑坡位移预测模型研究[J]. 工程地质学报, 30(2): 575-588. doi: 10.13544/j.cnki.jeg.2021-0584
引用本文: 尚敏, 熊德兵, 张惠强, 等. 2022. 基于时间序列与混合核函数SA-SVR的滑坡位移预测模型研究[J]. 工程地质学报, 30(2): 575-588. doi: 10.13544/j.cnki.jeg.2021-0584
Shang Min, Xiong Debing, Zhang Huiqiang, et al. 2022. Landslide displacement prediction model based on time series and mixed kernel function SA-SVR[J]. Journal of Engineering Geology, 30(2): 575-588. doi: 10.13544/j.cnki.jeg.2021-0584
Citation: Shang Min, Xiong Debing, Zhang Huiqiang, et al. 2022. Landslide displacement prediction model based on time series and mixed kernel function SA-SVR[J]. Journal of Engineering Geology, 30(2): 575-588. doi: 10.13544/j.cnki.jeg.2021-0584

基于时间序列与混合核函数SA-SVR的滑坡位移预测模型研究

doi: 10.13544/j.cnki.jeg.2021-0584
基金项目: 

湖北省自然科学基金面上项目 2017CFB534

湖北长江三峡滑坡国家野外科学观测研究站开放研究基金 2018KTL09

广东省普通高校重点领域专项 2021ZDZX4074

详细信息
    作者简介:

    尚敏(1977-),男,博士,副教授,主要从事地质灾害机理与防治研究. E-mail:summing@126.com

    通讯作者:

    熊德兵(1995-),男,硕士生,地质资源与地质工程专业. E-mail:xiongdebing28@outlook.com

  • 中图分类号: P642.22

LANDSLIDE DISPLACEMENT PREDICTION MODEL BASED ON TIME SERIES AND MIXED KERNEL FUNCTION SA-SVR

Funds: 

the General Program of Natural Science Foundation of Hubei Province 2017CFB534

Open Research Fund of Yangtze River Three Gorges Landslide National Field Scientific Observation and Research Station of Hubei Province 2018KTL09

Special Projects in Key Fields of Guangdong Universities 2021ZDZX4074

  • 摘要: 本文针对阶跃型滑坡变形定量预测困难,提出一种基于时间序列分解与混合核函数SA-SVR的滑坡位移预测模型。首先基于时间序列分解原理,反复使用指数平滑法将滑坡累积位移分解为趋势项位移和周期项位移,使分解后的趋势项位移较平滑且能保证周期项位移的预测精度。同时针对多项式预测容易过拟合造成预测值偏离真实值的问题,采用K-flod交叉验证的3次多项式对趋势项位移进行预测;通过SVR核函数性质,构造泛化能力和学习能力都较强的混合核函数作为SVR模型的核方法,以滑坡诱发因子作为SVR模型输入向量,以模拟退火算法(SA)对使用混合核函数的SVR模型进行参数寻优,从而建立混合核函数的SA-SVR模型预测周期项位移;最后合并趋势项位移和周期项位移得到总位移预测值。以三峡库区白家包滑坡为例,选取ZG325监测点2012年1月~2020年9月数据进行研究,并以ZG324监测点作为辅助验证。结果表明,相较于传统SVR预测模型,模拟退火算法(SA)在参数寻优方面表现良好,混合核函数对SVR模型更加敏感,能较大幅度提高预测精度,具有较高的应用和推广价值。
  • 图  1  混合核函数SA-SVR位移预测技术路线

    Figure  1.  Displacement prediction flow chart based on hybrid kernel function SA-SVR

    图  2  白家包滑坡工程地质平面图

    Figure  2.  Engineering geological map of Baijiabao Landslide

    图  3  白家包滑坡累积位移曲线图

    Figure  3.  Cumulative displacement-time curve of Baijiabao Landslide

    图  4  ZG325累积位移-降雨量-库水位监测曲线

    Figure  4.  Monitoring curves for cumulative displacement reservoir water level and rainfall

    图  5  不同a值的分解效果图

    Figure  5.  Decomposition effect of different smoothing indexes

    图  6  趋势项分解结果

    Figure  6.  Trend displacement decomposition result

    图  7  趋势项位移预测结果

    a. 第2阶段①参数预测结果; b. 第2阶段②参数预测结果

    Figure  7.  Trend displacement prediction results

    图  8  周期项位移提取值

    Figure  8.  Extracted values of periodic displacements

    图  9  周期项位移与影响因子关系

    a. 周期项位移和库水位关系; b. 周期项位移与库水位滞后关系; c. 周期项位移与降雨量和位移增量关系

    Figure  9.  Relationships between periodic displacement and influence factors

    图  10  线性核比例与预测结果RMSE曲线图

    Figure  10.  Linear kernel ratio and prediction result RMSE curve

    图  11  ZG325监测点周期项位移预测结果

    Figure  11.  Predicted values of periodic displacement of ZG325 monitoring point

    图  12  ZG325监测点累积位移预测结果

    Figure  12.  Predicted values of cumulative displacement of ZG325 monitoring point

    图  13  ZG324监测点趋势项位移与周期项位移预测结果

    Figure  13.  Trend term displacement and periodic term displacement prediction results of ZG324 monitoring point

    图  14  ZG324监测点累积位移预测结果

    Figure  14.  Predicted values of cumulative displacement of ZG324 monitoring point

    表  1  趋势项位移多项式预测参数

    Table  1.   Polynomial prediction parameters of trend term displacement

    时间段 a b c d R2
    第1阶段 -0.0004 -0.1680 17.5704 452.0679 0.9944
    第2阶段① 0.0026 -0.6899 66.8027 -934.8835 0.9973
    第2阶段② 0.0023 -0.6265 62.4857 -840.4339 0.9972
    下载: 导出CSV

    表  2  累积位移预测精度

    Table  2.   Accumulated displacement prediction accuracy

    时间 实际累积位移 预测累积位移 绝对误差 相对误差
    2019-10 1418.830 1417.062 1.768 0.125
    2019-11 1418.873 1407.443 11.430 0.806
    2019-12 1416.483 1409.117 7.366 0.520
    2020-01 1419.963 1412.325 7.638 0.538
    2020-02 1419.102 1415.721 3.381 0.238
    2020-03 1418.241 1416.542 1.699 0.120
    2020-04 1422.760 1417.249 5.512 0.387
    2020-05 1416.102 1430.218 14.116 0.997
    2020-06 1447.811 1446.016 1.796 0.124
    2020-07 1458.174 1474.004 15.830 1.086
    2020-08 1461.464 1472.290 10.826 0.741
    2020-09 1463.388 1468.249 4.861 0.332
    统计 RMSE 8.588 R2 0.790
    下载: 导出CSV
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  • 收稿日期:  2021-08-19
  • 修回日期:  2021-12-01
  • 刊出日期:  2022-04-25

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