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

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

董力豪^{1, 2,},
刘艳辉^1, ,,
黄俊宝³,
刘海宁²

1.
中国地质环境监测院（自然资源部地质灾害技术指导中心），北京　100081，中国
2.
华北水利水电大学，郑州　450045，中国
3.
福建省地质环境监测中心，福州　350002，中国

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:
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出版历程
- 收稿日期: 2022-11-07
- 录用日期: 2023-03-13
- 修回日期: 2023-03-06

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

DONG Lihao^{1, 2
,},
LIU Yanhui^{1
, ,},
HUANG Junbao³,
LIU Haining²

1.
China Institute of Geological Environment Monitoring （Technical Guidance Center for Geological Hazards, Ministry of Natural Resources）, Beijing　100081
2.
North China University of Water Resources and Electric Power, Zhengzhou, Henan　450045, China
3.
Fujian Geological Environment Monitoring Center, Fuzhou, Fujian　350002, China

摘要

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。
- 滑坡灾害 /
- 预警模型 /
- 深度学习 /
- 卷积神经网络 /
- 模型构建
Abstract: Landslide disasters occur frequently in Fujian Province, and early warning of landslide disasters on a regional scale is an important means of effective disaster prevention and mitigation. Due to the complex mechanism of landslide disasters, the traditional regional landslide early warning methods have such problems as insufficient accuracy. Deep learning mainly refers to the technology of feature extraction, abstraction, representation and learning by constructing the neural network model, which is a kind of machine learning. As a classical deep learning algorithm, convolutional neural network has more powerful classification and representation ability than traditional machine learning. Taking Fujian Province as the research area, this paper introduces the convolution neural network into the field of landslide disaster early warning and constructs a regional landslide early warning model of Fujian Province. The process is as follows: ① the SMOTE optimization algorithm is used to optimize the sample database of landslide disasters in Fujian Province from 2010 to 2018, enlarging the number of positive samples and expanding the proportion of positive and negative samples from 1∶3.4 to 1∶2, and the total number of samples reaches 19151. ② Construct a convolution neural network model structure, which includes an input layer, two convolution layers, two maximum pooling layers, a full connection layer and an output layer. ③ Use the convolution neural network to train the optimized samples (80% of the samples from 2010 to 2018 as the training set), and use the Bayesian optimization algorithm to optimize the model parameters to obtain the regional landslide early warning model of Fujian Province. ④ The model is verified with 20% of the samples from 2010 to 2018 as the test set, and the confusion matrix and ROC curve are used to verify the model. The results show that the accuracy of the model ranges from 0.94 to 0.97, the AUC value is 0.975, indicating that the model accuracy and generalization ability are good. ⑤ The actual situation of the landslide disaster in the flood season of 2019 is taken as a positive sample, negative samples are collected through the method of time-space sampling, and the 2019 regional landslide sample verification set (603 samples) is constructed. The model is further verified by using the confusion matrix and ROC curve. The results show that the accuracy of the model ranges from 0.75 to 0.80, and the AUC value is 0.852. Although only the actual landslide samples in the flood season of 2019 is used for verification, good results is also achieved. In this paper, the convolution neural network algorithm is applied to the regional landslide early warning, which provides a new way to establish the regional landslide early warning model. The preliminary verification shows that the model is effective and will be further applied and verified in Fujian Province in the future.
- landslide disaster /
- early warning model /
- deep learning /
- convolutional neural network /
- model building

HTML全文

图 1 SMOTE算法合成数据示意图

Figure 1. SMOTE algorithm synthesis data diagram

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图 2 福建省训练样本集分布

Figure 2. Training sample set of Fujian Province

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图 3 典型CNN网络结构图

Figure 3. Typical network structure of CNN

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图 4 训练过程中准确率的变化

Figure 4. Changes in accuracy during training

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图 5 CNN模型ROC曲线

Figure 5. ROC curves of the CNN model

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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

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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

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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

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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
阈值			非滑坡	滑坡
	预测值	非滑坡	2325	72	特异度：0.963
0.25	预测值	滑坡	73	1138	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	2344	53	特异度：0.978
0.5	预测值	滑坡	78	1133	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	2353	44	特异度：0.982
0.75	预测值	滑坡	86	1125	灵敏度：0.929
		真反例率：0.964		精确率：0.962	总精度：0.964

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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
阈值			非滑坡	滑坡
	预测值	非滑坡	434	76	特异度：0.851
0.25	预测值	滑坡	18	75	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	450	120	特异度：0.789
0.5	预测值	滑坡	2	31	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	452	151	特异度：0.750
0.75	预测值	滑坡	0	0	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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