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基于卷积神经网络的福建省区域滑坡灾害预警模型

董力豪 刘艳辉 黄俊宝 刘海宁

董力豪,刘艳辉,黄俊宝,等. 基于卷积神经网络的福建省区域滑坡灾害预警模型[J]. 水文地质工程地质,2023,50(0): 1-9 doi:  10.16030/j.cnki.issn.1000-3665.202211018
引用本文: 董力豪,刘艳辉,黄俊宝,等. 基于卷积神经网络的福建省区域滑坡灾害预警模型[J]. 水文地质工程地质,2023,50(0): 1-9 doi:  10.16030/j.cnki.issn.1000-3665.202211018
DONG Lihao, LIU Yanhui, HUANG Junbao, et al. An early prediction model of regional landslide disasters in Fujian Province based on convolutional neural network[J]. Hydrogeology & Engineering Geology, 2023, 50(0): 1-9 doi:  10.16030/j.cnki.issn.1000-3665.202211018
Citation: DONG Lihao, LIU Yanhui, HUANG Junbao, et al. An early prediction model of regional landslide disasters in Fujian Province based on convolutional neural network[J]. Hydrogeology & Engineering Geology, 2023, 50(0): 1-9 doi:  10.16030/j.cnki.issn.1000-3665.202211018

基于卷积神经网络的福建省区域滑坡灾害预警模型

doi: 10.16030/j.cnki.issn.1000-3665.202211018
基金项目: 国家自然科学基金(42077440;41202217);国家重点研发计划(2018YFC1505503)
详细信息
    作者简介:

    董力豪(1998—),男,硕士,硕士研究生,主要从事地质灾害预警相关研究工作。E-mail:1365234358@qq.com

    通讯作者:

    刘艳辉(1978—),女,博士,正高级工程师,主要从事地质灾害预警与防治、工程地质等方面的研究工作。E-mail:392990563@qq.com

  • 中图分类号: 中图分类号: 文献标志码: 文章编号:

An early prediction model of regional landslide disasters in Fujian Province based on convolutional neural network

  • 摘要: 福建省滑坡灾害频发,开展区域尺度上的滑坡灾害预警是有效防灾减灾的重要手段,由于滑坡成灾机理复杂,传统的区域滑坡预警方法存在精度不足等问题。深度学习主要是指通过构建神经网络模型进行特征的提取、抽象、表示与学习的技术,是机器学习的一种。卷积神经网络作为一种经典的深度学习算法,具有比传统机器学习更强大的分类能力与表征能力。文章以福建省为研究区,将卷积神经网络引入滑坡灾害预警领域,构建福建省区域滑坡预警模型,过程如下:①采用SMOTE优化算法对2010—2018年福建省滑坡灾害样本库进行优化,扩充正样本的个数,将正负样本比例从1∶3.4扩充到1∶2,样本总量达到19151个。②构建卷积神经网络模型结构,模型结构包括一个输入层、两个卷积层、两个最大池化层和一个全连接层以及一个输出层。③使用卷积神经网络对优化后的样本(2010—2018年样本的80%作为训练集)进行训练,并用贝叶斯优化算法优化模型超参数,得到福建省区域滑坡预警模型。④以2010—2018年样本的20%作为测试集对模型进行校验,采用混淆矩阵、ROC曲线进行模型校验,结果显示模型准确度为0.94−0.97,AUC值达到0.975,模型精度与泛化能力良好。⑤以2019年汛期滑坡灾害实况作为正样本,通过时空采样的方法采集负样本,构建2019年区域滑坡样本校验集(样本数603个),对模型进行进一步实况校验,采用混淆矩阵、ROC曲线进行模型校验,结果显示模型准确度为0.75−0.80,AUC值为0.852。虽然仅用了2019年一年汛期的滑坡实况样本进行校验,但也达到较好的效果。本文将卷积神经网络算法应用到区域滑坡预警中,为建立区域滑坡预警模型提供了一种新的途径,初步校验表明,模型效果良好,今后将在福建省对模型进行进一步的应用与校验。
  • 图  1  SMOTE算法合成数据示意图

    Figure  1.  SMOTE algorithm synthesis data diagram

    图  2  福建省训练样本集分布

    Figure  2.  Training sample set of Fujian Province

    图  3  典型CNN网络结构图

    Figure  3.  Typical network structure of CNN

    图  4  训练过程中准确率的变化

    Figure  4.  Changes in accuracy during training

    图  5  CNN模型ROC曲线

    Figure  5.  ROC curves of the CNN model

    图  6  福建省2019年汛期滑坡灾害分布图

    Figure  6.  Distribution map of landslide hazards in 2019 flood season in Fujian Province

    图  7  2019年样本CNN模型校验结果的ROC曲线

    Figure  7.  ROC curves of the 2019 sample CNN model verification results

    表  1  本文CNN模型超参数设置

    Table  1.   Hyperparameter settings of the CNN models

    超参数意义优化后参数
    units1
    units2
    dropout rate
    activation1
    activation2
    activation3
    lr
    第一层卷积核的大小
    第二层卷积核的大小
    每层神经元丢弃率
    第一层激活函数
    第二层激活函数
    全连接层激活函数
    学习率
    512$ \times 1 $
    32$ \times 1 $
    0.1
    relu
    relu
    elu
    0.002
    下载: 导出CSV

    表  2  不同阈值下的 CNN分类结果混淆矩阵

    Table  2.   Confuse matrix of the results of the CNN classification under different thresholds

    阈值实际值
    非滑坡滑坡
    预测值非滑坡232572特异度:0.963
    0.25滑坡731138灵敏度:0.939
    召回率:0.969精确率:0.941总精度:0.959
    预测值非滑坡234453特异度:0.978
    0.5滑坡781133灵敏度:0.935
    真反例率:0.967精确率:0.955准确率:0.964
    预测值非滑坡235344特异度:0.982
    0.75滑坡861125灵敏度:0.929
    真反例率:0.964精确率:0.962总精度:0.964
    下载: 导出CSV

    表  3  2019年样本CNN模型校验结果的混淆矩阵

    Table  3.   Confusion matrix of the 2019 sample CNN model verification results

    阈值实际值
    非滑坡滑坡
    预测值非滑坡43476特异度:0.851
    0.25滑坡1875灵敏度:0.806
    召回率:0.960精确率:0.497总精度:0.844
    预测值非滑坡450120特异度:0.789
    0.5滑坡231灵敏度:0.939
    真反例率:0.996精确率:0.205准确率:0.798
    预测值非滑坡452151特异度:0.750
    0.75滑坡00灵敏度:0
    真反例率:1精确率:0总精度:0.750
    下载: 导出CSV
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  • 收稿日期:  2022-11-07
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