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基于SSA-Adam-BP神经网络模型的堰塞坝稳定性预测

宋宜祥 张晓波 黄达

宋宜祥, 张晓波, 黄达. 基于SSA-Adam-BP神经网络模型的堰塞坝稳定性预测[J]. 地质科技通报, 2022, 41(2): 130-138. doi: 10.19509/j.cnki.dzkq.2022.0040
引用本文: 宋宜祥, 张晓波, 黄达. 基于SSA-Adam-BP神经网络模型的堰塞坝稳定性预测[J]. 地质科技通报, 2022, 41(2): 130-138. doi: 10.19509/j.cnki.dzkq.2022.0040
Song Yixiang, Zhang Xiaobo, Huang Da. Stability prediction of landslide dams based on SSA-Adam-BP neural network model[J]. Bulletin of Geological Science and Technology, 2022, 41(2): 130-138. doi: 10.19509/j.cnki.dzkq.2022.0040
Citation: Song Yixiang, Zhang Xiaobo, Huang Da. Stability prediction of landslide dams based on SSA-Adam-BP neural network model[J]. Bulletin of Geological Science and Technology, 2022, 41(2): 130-138. doi: 10.19509/j.cnki.dzkq.2022.0040

基于SSA-Adam-BP神经网络模型的堰塞坝稳定性预测

doi: 10.19509/j.cnki.dzkq.2022.0040
基金项目: 

国家自然科学基金项目 41902290

国家自然科学基金项目 41672300

国家自然科学基金项目 41972297

河北省自然科学基金项目 D2020202002

河北省自然科学基金项目 D202102002

详细信息
    作者简介:

    宋宜祥(1987—),男,讲师,硕士生导师,主要从事地质灾害和岩土工程数值模拟研究。E-mail: syxdlut2010@163.com

    通讯作者:

    黄达(1976—),男,教授,博士生导师,主要从事岩石力学与地质灾害方面研究。E-mail: dahuang@hebut.edu.cn

  • 中图分类号: P642.2

Stability prediction of landslide dams based on SSA-Adam-BP neural network model

  • 摘要: 现有的堰塞坝稳定性预测模型多为线性模型, 无法充分考虑堰塞坝稳定性与其形态特征和水域条件之间的复杂非线性关系。鉴于此, 结合反向传播神经网络模型和樽海鞘优化算法, 提出了一种新型的堰塞坝稳定性预测模型SSA-Adam-BP。该模型通过网格搜索法选取确定模型结构的最佳超参数组合, 进而利用交叉验证和绘制ROC曲线的方式分别对采用不同优化算法的模型进行评估。使用开源数据库中的全球153例堰塞坝数据对模型的实际应用进行了说明及验证。与传统线性模型的对比表明神经网络模型预测准确率较高, 具有较低的误报率。将SSA与Adam优化算法结合提高了BP模型的全局搜索能力, 其平均交叉验证准确率达到了91.73%, 能够使用较少的参数实现对堰塞坝稳定性快速准确的预测。SSA-Adam-BP模型对近年来典型工程的稳定性能够准确预测, 具有一定的实用性和系统平台推广应用价值。

     

  • 图 1  单隐藏层BP神经网络结构图

    Figure 1.  BP neural network structure of single hidden layer

    图 2  SSA-Adam-BP神经网络训练流程图

    Figure 2.  Flow chart of SSA-Adam-BP neural network training

    图 3  采用不同处理方式的变量分布示意图

    Figure 3.  Schematic diagrams of variable distribution using different processing methods

    图 4  不同BP神经网络模型的ROC曲线及AUC

    Figure 4.  ROC curve and AUC value of different BP neural network models

    图 5  预测失误案例位置分布图

    Figure 5.  Location distribution map of prediction error cases

    图 6  中国32例堰塞坝的稳定性预测结果

    Figure 6.  Stability prediction results of 32 landslide dams in China

    表  1  Ermini堰塞坝数据库

    Table  1.   Landslide dam database established by Ermini

    序号 坝体高度H/m 坝体体积V/m3 集水区面积A/km2 稳定性分类 参考文献
    1 20 1 500 000 5 SD [10]
    2 15 400 000 15 SD [10]
    84 90 18 000 000 147 UD [10]
    下载: 导出CSV

    表  2  Tabata堰塞坝数据库

    Table  2.   Landslide dam database established by Tabata

    序号 坝体高度H/m 坝体体积V/m3 集水区面积A/km2 稳定性分类 参考文献
    1 10 180 000 60 UD [26]
    2 20 100 000 83 UD [26]
    37 20 22 000 14.5 SD [26]
    下载: 导出CSV

    表  3  121例堰塞坝数据基本统计信息

    Table  3.   Basic statistical information of 121 cases based on the landslide dam database

    特征变量 坝体高度H/m 坝体体积V/m3 集水区面积A/km2
    最小值 3.00 8.70×107 0.19
    最大值 500.00 2.20×109 4.50×104
    平均值 52.88 5.70×107 731.61
    标准差 65.87 2.86×108 4 321.71
    下载: 导出CSV

    表  4  Shapiro-Wilk检验结果

    Table  4.   Testing results of Shapiro-Wilk

    变量 统计量W p 在5%水平下的结论
    H 0.68 0.00 排除正态性
    V 0.19 0.00 排除正态性
    A 0.14 0.00 排除正态性
    lg(H′) 0.99 0.79 不能排除正态性
    lg(V′) 0.999 0.70 不能排除正态性
    lg(A′) 0.99 0.11 不能排除正态性
    下载: 导出CSV

    表  5  不同BP神经网络模型的交叉验证准确率

    Table  5.   Cross-verification accuracy of different BP neural network models

    模型 准确率/% 平均准确率/%
    K=1 K=2 K=3 K=4 K=5
    Adam-BP 87.50 87.50 91.67 91.67 88.00 89.27
    SSA-BP 87.50 83.33 91.67 91.67 88.00 88.43
    SSA-Adam-BP 91.67 87.50 95.83 91.67 92.00 91.73
    下载: 导出CSV

    表  6  堰塞坝稳定性线性预测模型

    Table  6.   Linear prediction model of landslide dam stability

    提出者 判别公式 判别标准
    Ermini等[10] $DBI = {\rm{lg}}\left( {\frac{{A \times H}}{V}} \right)$ DBI < 2.75稳定;
    2.75 < DBI < 3.08
    无法确定;DBI>3.08
    不稳定
    Dong等[27] $\begin{array}{c} D = - 2.13{\rm{lg}}\left( A \right) - \\ 4.08{\rm{lg}}\left( H \right) + \\ 2.94{\rm{lg}}\left( V \right) + 4.09 \end{array}$ D>0稳定;
    D≤0不稳定
    Dong等[28] $\begin{array}{c} {L_{\rm{s}}} = - 4.48{\rm{lg}}\left( A \right) - \\ 9.31{\rm{lg}}\left( H \right) + \\ 6.61{\rm{lg}}\left( V \right) + 6.39\\ {P_{\rm{s}}} = \frac{1}{{1 + {{\rm{e}}^{ - {L_{\rm{s}}}}}}} \end{array}$ Ps>0.5稳定;
    Ps≤0.5不稳定
    下载: 导出CSV

    表  7  各堰塞坝稳定性预测模型的预测结果

    Table  7.   Prediction results of the stability prediction models of landslide dams

    模型 准确数量 准确率/% 误报数量 误报率/%
    DBI 23 62.16 7 18.92
    AHV_Dis 26 70.27 6 16.22
    AHV_Log 27 72.97 5 13.51
    SSA-Adam-BP 32 86.49 1 2.70
    下载: 导出CSV

    表  8  各输入参数的平均影响值

    Table  8.   Mean influence value of each input parameter

    影响因素 Y1 Y2 平均影响值
    坝体高度 32.04 46.31 -0.17
    坝体体积 55.54 23.56 0.38
    集水区面积 31.02 51.27 -0.24
    下载: 导出CSV

    表  9  中国近年来典型堰塞坝事件[31]

    Table  9.   Typical landslide dam cases in China in recent years

    序号 形成时间 名称 坝体高度H/m 坝体体积V/m3 集水区面积A/km2 稳定性分类
    1 2000 易贡 80 300 000 000 13 533 不稳定
    2 2008 白果村 15 400 000 3 564 不稳定
    32 2018 白格(2) 81 10 000 000 173 484 不稳定
    下载: 导出CSV
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