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基于BP神经网络的滑坡监测多源异构数据融合算法研究

王智伟 王利 黄观文 韩清清 徐甫 岳聪

王智伟, 王利, 黄观文, 等, 2020. 基于BP神经网络的滑坡监测多源异构数据融合算法研究. 地质力学学报, 26 (4): 575-582. DOI: 10.12090/j.issn.1006-6616.2020.26.04.050
引用本文: 王智伟, 王利, 黄观文, 等, 2020. 基于BP神经网络的滑坡监测多源异构数据融合算法研究. 地质力学学报, 26 (4): 575-582. DOI: 10.12090/j.issn.1006-6616.2020.26.04.050
WANG Zhiwei, WANG Li, HUANG Guanwen, et al., 2020. Research on multi-source heterogeneous data fusion algorithm of landslide monitoring based on BP neural network. Journal of Geomechanics, 26 (4): 575-582. DOI: 10.12090/j.issn.1006-6616.2020.26.04.050
Citation: WANG Zhiwei, WANG Li, HUANG Guanwen, et al., 2020. Research on multi-source heterogeneous data fusion algorithm of landslide monitoring based on BP neural network. Journal of Geomechanics, 26 (4): 575-582. DOI: 10.12090/j.issn.1006-6616.2020.26.04.050

基于BP神经网络的滑坡监测多源异构数据融合算法研究

doi: 10.12090/j.issn.1006-6616.2020.26.04.050
基金项目: 

国家自然科学基金项目 41877289

国家自然科学基金项目 41731066

国家自然科学基金项目 41604001

国家重点研发计划项目重点专项 2018YFC1504805

国家重点研发计划项目重点专项 2018YFC1505102

详细信息
    作者简介:

    王智伟(1995-), 男, 在读硕士, 主要从事滑坡变形监测数据处理方法研究。E-mail:wangzw_chd@163.com

    通讯作者:

    王利(1975-), 男, 博士, 教授, 主要从事GNSS精密定位和变形监测理论与方法研究。E-mail:wangli@chd.edu.cn

  • 中图分类号: P642.22

Research on multi-source heterogeneous data fusion algorithm of landslide monitoring based on BP neural network

  • 摘要: 针对滑坡监测中的多源异构数据融合问题,论文提出了一种基于BP神经网络的多源异构监测数据融合算法。该算法将影响滑坡变形的温度、湿度、风力、云量、单日降水量和累计降水量等多环境因子变量作为输入变量,以滑坡位移变化量数据作为期望输出数据,并利用各环境因子变量和滑坡位移变化量的相关性及显著性进行环境因子变量筛选,以提高算法的预测精度。论文采用甘肃省永靖县黑方台党川滑坡的实测数据进行了试验,结果表明:反向传播(Back-Propagation,BP)神经网络数据融合算法适用于具有多源异构监测数据的滑坡变形预测;在进行环境变量因子筛选后,BP神经网络数据融合算法的决定系数达到0.985,均方根误差(RMSE)达到0.4787 mm,从而有效提高了变形预测结果的精度。

     

  • 图  1  研究区位置和监测点位分布图

    Figure  1.  Location of the study area and distribution map of the landslide monitoring points

    图  2  不同隐层神经元数目的预测误差标准差

    Figure  2.  Standard deviation of prediction error for different hidden layer neurons

    图  3  两种方案下融合模型的预测位移变化量和实际位移变化量的相关图

    Figure  3.  Correlation diagram of the predicted displacement change and the actual displacement change of the fusion model under two schemes

    表  1  多环境因子变量及GNSS位移量样本数据

    Table  1.   Sample data of multiple environmental factor variables and GNSS displacement

    序号 温度/℃ 湿度/% 风力/级 云量/% 单日降水量/mm 累计降水量/mm 位移变化量/(mm/d)
    1 -6 54.5 6 53 0.5 0.5 2.35
    2 -7.5 72.5 6 89 17.7 18.2 3.15
    3 -7 74.5 5 66 13.6 31.8 2.77
    19 -2.5 83.5 5 71 3.9 54.3 4.13
    20 -1.5 79.5 5 55 3.6 57.9 3.80
    下载: 导出CSV

    表  2  环境因子变量相关系数

    Table  2.   Correlation coefficients of environmental factor variables

    相关系数 温度/℃ 湿度/% 风力/级 云量/% 单日降水量/mm 累计降水量/mm
    温度/℃ 1 -0.197 -0.764 -0.468 -0.625 0.428
    湿度/% -0.197 1 -0.081 0.818 0.508 0.521
    风力/级 -0.764 -0.081 1 0.267 0.239 -0.466
    云量/% -0.468 0.818 0.267 1 0.597 0.169
    单日降水量/mm -0.625 0.508 0.239 0.597 1 -0.171
    累计降水量/mm 0.428 0.521 -0.466 0.169 -0.171 1
    下载: 导出CSV

    表  3  各环境因子变量和滑坡位移变化量的相关性及显著性

    Table  3.   Correlation and significance of various environmental factor variables and landslide displacement changes

    变量 温度/℃ 湿度/% 风力/级 云量/% 单日降水量/mm 累计降水量/mm
    相关性 -0.190 0.598 0.063 0.465 0.206 0.475
    显著性 0.423 0.005 0.792 0.039 0.383 0.034
    下载: 导出CSV

    表  4  两种方案下融合模型的预测位移变化量和实际位移变化量的对比

    Table  4.   Comparison of the predicted displacement change and the actual displacement change of the fusion model under two schemes

    序号 预测位移变化量/mm 实际位移变化量/mm
    方案一 方案二
    16 2.09 2.53 2.69
    17 2.50 2.74 3.03
    18 2.20 2.89 3.15
    19 3.07 3.35 4.13
    20 2.80 3.20 3.80
    MAE 0.8280 0.4180 /
    RMSE 0.8564 0.4787 /
    下载: 导出CSV

    表  5  两种方案下融合模型的决定系数与残差平方和

    Table  5.   The residual sum of squares and the determination coefficient of the fusion model under two schemes

      R2 RSS
    方案一 0.890 0.073
    方案二 0.985 0.006
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-05-25
  • 修回日期:  2020-06-20
  • 刊出日期:  2020-08-28

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