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一种基于遗传算法的滑坡蠕滑位移预测模型

冯谕 曾怀恩 涂鹏飞

冯谕,曾怀恩,涂鹏飞. 一种基于遗传算法的滑坡蠕滑位移预测模型[J]. 中国地质灾害与防治学报,2023,34(0): 1-11 doi: 10.16031/j.cnki.issn.1003-8035.202209038
引用本文: 冯谕,曾怀恩,涂鹏飞. 一种基于遗传算法的滑坡蠕滑位移预测模型[J]. 中国地质灾害与防治学报,2023,34(0): 1-11 doi: 10.16031/j.cnki.issn.1003-8035.202209038
FENG Yu,ZENG Huaien,TU Pengfei. A prediction model for landslide creep displacement based on Genetic Algorithm[J]. The Chinese Journal of Geological Hazard and Control,2023,34(0): 1-11 doi: 10.16031/j.cnki.issn.1003-8035.202209038
Citation: FENG Yu,ZENG Huaien,TU Pengfei. A prediction model for landslide creep displacement based on Genetic Algorithm[J]. The Chinese Journal of Geological Hazard and Control,2023,34(0): 1-11 doi: 10.16031/j.cnki.issn.1003-8035.202209038

一种基于遗传算法的滑坡蠕滑位移预测模型

doi: 10.16031/j.cnki.issn.1003-8035.202209038
基金项目: 国家自然科学基金资助项目(42074005);湖北省地质局2021年度科技项目(KJ2021-16)
详细信息
    作者简介:

    冯谕:冯 谕(1998-),男,研究生,研究方向:滑坡监测数据处理研究。E-mall:2044207612@qq.com

    通讯作者:

    涂鹏飞(1965-),硕士,教授,研究方向:地质灾害监测与预警研究。E-mall:13872539886@163.com

  • 中图分类号: 中图分类号: 文献标识码:A 文章编号:

A prediction model for landslide creep displacement based on Genetic Algorithm

  • 摘要: 滑坡位移预测是预报滑坡灾害的重要依据,以往的滑坡位移预测模型多数为时间序列预测模型、BP神经网络预测模型、Gaussian拟合预测模型以及其他一些非线性预测模型。这些滑坡位移预测模型在建立上缺乏力学理论支撑,对不同力学特性产生的滑坡位移预测分析上没有针对性。文中针对力学特性为重力蠕变型滑坡位移的预测,提出一种基于遗传优化算法的滑坡蠕滑位移非线性预测模型。以鲁家坡滑坡东侧J05监测点的累计水平位移为例,划定测试区域与预测区域进行模型预测分析,并将新模型预测结果与Gaussian拟合预测模型、BP神经网络预测模型预测结果进行对比分析。结果表明,相较于传统预测模型,新模型的预测效果有所提升,有一定的工程价值与实践价值。
  • 图  1  遗传算法基本流程

    Figure  1.  Basic Flow chart of Genetic Algorithm

    图  2  适应度函数程序设计技术路线

    Figure  2.  Technical roadmap for Fitness Function Program design

    图  3  鲁家坡滑坡全貌

    Figure  3.  Panoramic view of the Lujiapo landslide

    图  4  不同应力条件下的蠕变曲线

    Figure  4.  Creep Curves Under Different Stress Conditions

    图  5  监测点水平位移-时间散点分布

    Figure  5.  Scatter Distribution of Horizontal Displacement vs. Time at Monitoring Points

    图  6  监测点水平位移-时间散点区域分布

    Figure  6.  Scatter Area Distribution of Horizontal Displacement vs. Time at Monitoring Points

    图  7  测试数据统一决定系数迭代过程

    Figure  7.  Iteration process of Determination for the Test Data Iterative Process

    图  8  进化点函数拟合曲线

    Figure  8.  Fitting Curve of the Evolution Point Function

    图  9  测试数据拟合结果

    Figure  9.  Test Data fitting results

    图  10  三种预测模型的测试数据拟合曲线

    Figure  10.  Test Data Fitting Curves of Three Prediction Models

    图  11  三种预测模型的原始数据拟合曲线

    Figure  11.  Fitting Curves of Original Data for Three Prediction Models

    图  12  三种预测模型的预测数据拟合曲线

    Figure  12.  Fitting Curves of Predicted Data for Three Prediction Models

    图  13  三种预测模型绝对误差统计

    Figure  13.  Statistical Analysis of Absolute Errors for Three Prediction Models

    表  1  遗传算法迭代参数

    Table  1.   Iteration parameter of Genetic Algorithm

    参数名称符号
    种群数量npop
    迭代次数maxit
    染色体数nv
    交叉算子npc
    变异算子nmu
    子代数量nc
    下载: 导出CSV

    表  2  遗传算法迭代参数设定表

    Table  2.   Genetic Algorithm iteration parameter configuration table

    迭代参数数值
    npop30
    maxit500
    nvar23
    npc0.9
    nmu0.1
    nc27
    下载: 导出CSV

    表  3  测试数据拟合模型迭代进化表

    Table  3.   Iterative Evolution Table of Test Data Fitting Models

    进化阶段迭代次数统一决定系数RNL
    010.828 0
    11010.839 6
    21060.839 8
    31510.840 6
    42210.845 0
    下载: 导出CSV

    表  4  拟合模型各进化阶段求解参数

    Table  4.   Parameters solutions at each Evolution Stage of the Fit Model

    参数 a1 b1 c1 d1 D RNL
    进化阶段 0 +388.3 −1.351E-02 −3.786E+01 +2.179E-02 −383.2 0.828 0
    1 +421.8 −1.937E-01 −1.003E-03 −1.770E-02 −351.0 0.839 6
    2 +454.2 −9.954E-02 −1.257E-03 −5.410E-03 −411.1 0.839 8
    3 +136.4 −8.182E-01 −1.667E-03 −2.010E-02 −72.96 0.840 6
    4 +6.379 −2.524E+04 −4.264E-02 +1.620E-02 +3.167 0.845 0
    下载: 导出CSV

    表  5  三种预测模型预测区域统计学指标

    Table  5.   Statistical Metrics for Prediction Regions of Three Prediction Models

    预测方法 遗传算法拟合预测 高斯拟合预测 BP神经网络预测
    统计学
    指标
    RNL 0.897 5 0.817 4 0.888 2
    RMSE 2.596 7 4.639 1 2.790 4
    FR 0.994 9 0.992 7 0.994 8
    R2 0.991 2 −1.216 2 0.826 7
    下载: 导出CSV
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