基于改进两步法采样策略和卷积神经网络的崩塌易发性评价

邓日朗; 张庆华; 刘伟; 陈凌伟; 谭建辉; 高泽茂; 郑先昌

doi:10.19509/j.cnki.dzkq.tb20220535

基于改进两步法采样策略和卷积神经网络的崩塌易发性评价

doi: 10.19509/j.cnki.dzkq.tb20220535

1.
广州大学土木工程学院, 广州 510006
2.
广州市城市规划勘测设计研究院, 广州 510060

基金项目:

广州市城市规划勘测设计研究院咨询项目 2023岩28008B-合01

详细信息

作者简介:
邓日朗, E-mail: rldeng@foxmail.com

通讯作者:
郑先昌, E-mail: zhengxianchang@gzhu.edu.cn

中图分类号: P642.22
计量
- 文章访问数: 41
- PDF下载量: 9
- 被引次数: 0
出版历程
- 收稿日期: 2022-09-19
- 录用日期: 2022-12-07
- 修回日期: 2022-12-02

Collapse susceptibility evaluation based on an improved two-step sampling strategy and a convolutional neural network

1.
School of Civil Engineering, Guangzhou University, Guangzhou 510006, China
2.
Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou 510060, China

More Information

Author Bio:
DENG Rilang, E-mail: rldeng@foxmail.com

Corresponding author: ZHENG Xianchang, E-mail: zhengxianchang@gzhu.edu.cn

摘要

摘要:
机器学习在崩塌滑坡泥石流地质灾害易发性分析评价领域已得到广泛的研究性应用, 非灾害样本的选取是易发性建模过程中的关键问题, 传统随机抽样和手工标注方法可能存在随机性和主观性。将土质崩塌易发性评价视为正例无标记(positive and unlabeled, 简称PU)学习, 提出了一种结合信息量(information value, 简称IV)和间谍技术(Spy)的两步卷积神经网络(convolutional neural networks, 简称CNN)框架(ISpy-CNN)。以广州市黄埔区崩塌编录和15类基础环境因子, 通过信息量模型筛选出部分低信息量样本; 采用间谍技术训练CNN模型, 从低信息量样本中识别出具有高置信度的可靠负例划分为非崩塌样本; 分别基于该学习框架、传统间谍技术和随机抽样, 使用支持向量机(support vector machine, 简称SVM)和随机森林(random forest, 简称RF)对比验证。结果表明, ISpy-CNN框架在验证集上的准确率、F1值、敏感度和特异度较随机采样分别提升了6.82%, 6.82%, 6.82%, 8.23%, 较传统Spy技术分别提升了2.86%, 2.89%, 2.86%, 2.31%;PU学习中第2步采用CNN模型的预测精度高于RF和SVM模型; 与传统Spy技术相比, 增加相同数量训练样本, ISpy-CNN框架筛选的样本集表现出较高的稳定性、预测精度和增长率。本研究提出的ISpy-CNN框架能更好地辅助选取高质量非灾害样本, 且崩塌易发性分区结果更符合实际的崩塌空间分布。
- 崩塌 /
- 易发性评价 /
- PU学习 /
- 间谍技术 /
- 信息量 /
- 卷积神经网络 /
- 随机森林 /
- 支持向量机
Abstract: Objective
Machine learning has been widely applied in the fields of collapse, landslide and debris flow susceptibility analysis. The selection of nonhazard samples is a key issue in landslide susceptibility analysis. Traditional random sampling and manual labelling methods may involve randomness and subjectivity.
Methods
In view of the potential randomness and representativeness of noncollapse samples, this paper considered soil collapse susceptibility evaluation a positive-unlabelled (PU) learning problem and proposes a two-step convolutional neural network framework (ISpy-CNN) that combines an information value model and the Spy technique. First, 15 collapse-related factors were selected for modelling based on the geomorphological, geological, hydrological, and artificial environmental conditions of the study area. Low-information-value samples that were able to map the distribution structure of noncollapsing samples were screened by the information value model. Then, through the Spy technique and training the CNN model, negative samples with high confidence were identified from low-information-value samples that were classified as noncollapsed samples. Finally, based on the framework and traditional random sampling, we used support vector machine (SVM) and random forest (RF) models to compare and verify the reliability, prediction accuracy and data sensitivity of the proposed learning framework and other models.
Results
The results illustrate that the proposed ISpy-CNN method can improve the accuracy, F1 value, sensitivity and specificity on the validation set by 6.82%, 6.82%, 6.82%, 8.23%, respectively compared to random sampling and 2.86%, 2.89%, 2.86%, 2.31%, respectively compared to the traditional Spy technique. The prediction accuracy of step 2 in PU learning using the CNN model is higher than that of the RF and SVM models. The sample set screened by the ISpy-CNN framework exhibited greater stability, prediction accuracy and growth rate than those screened by the traditional Spy technique by adding the same number of training samples.
Conclusion
The ISpy-CNN framework proposed in this paper can better assist in the selection of nonhazard samples and real collapse spatial distribution maps, and the results of the framework are more consistent with the actual collapse distributions.
- collapse /
- susceptibility evaluation /
- positive and unlabeled (PU) learning /
- Spy technique /
- information value /
- convolutional neural network /
- random forest /
- support vector machine
注释:

所有作者声明不存在利益冲突。

HTML全文

图 1 PU学习分类过程(图中代号含义见正文)

Figure 1. Classification process of PU learning

下载: 全尺寸图片幻灯片

图 2 基于卷积神经网络和改进两步法采样策略的崩塌易发性预测流程图

Figure 2. Flowchart of collapse prediction based on a convolutional neural network and an improved two-step sampling strategy

下载: 全尺寸图片幻灯片

图 3 研究区崩塌灾害分布概况(a)以及鼓胀式崩塌(b)、滑移式崩塌(c)和倾倒式崩塌(d)示意图

Figure 3. Distribution of collapse disasters in the study area (a) and rotational sliding failure (b), planar sliding failure (c), toppling failure(d)

下载: 全尺寸图片幻灯片

图 4 研究区崩塌易发性评价因子分级图(地层代号含义见正文)

Figure 4. Grading diagram of evaluation factors for collapse susceptibility in the study area

下载: 全尺寸图片幻灯片

图 5 影响因子相关性分析

Figure 5. Correlation analysis of impact factors

下载: 全尺寸图片幻灯片

图 6 信息量模型评价结果(a)和基于ISpy方法选取的非崩塌样本分布(b)示意图

Figure 6. Results obtained through information value model evaluation result (a) and distribution of noncollapse samples selection based on the ISpy method (b)

下载: 全尺寸图片幻灯片

图 7 研究区崩塌灾害易发性分级图

Figure 7. Grading map of collapse disaster susceptibility in the study area

下载: 全尺寸图片幻灯片

图 8 基于Spy(a)和ISpy(b)采样方法的崩塌易发性预测模型ROC曲线

Figure 8. ROC curves for model of collapse susceptibility prediction using the Spy (a) and ISpy (b) sampling methods

下载: 全尺寸图片幻灯片

图 9 基于Spy(a)和ISpy(b)采样方法的崩塌易发性指数分布(IQR为四分位间距)

Figure 9. Correlation between the index distribution and sampling methods using the Spy (a) and ISpy (b) sampling methods

下载: 全尺寸图片幻灯片

图 10 基于Spy(a)和ISpy(b)采样方法的预测准确率与训练集样本数据量的关系

Figure 10. Correlation between prediction accuracy and the number of training samples using the Spy (a) and ISpy (b) sampling methods

下载: 全尺寸图片幻灯片

表 1 CNN模型部分超参数设置

Table 1. Hyperparameter settings of the proposed CNN model

CNN参数项	参数值
卷积核大小	3×1
池化核大小	2×1
激活函数	ReLU
优化器	Adam
损失函数	二元交叉熵损失函数
学习率	0.005

下载: 导出CSV

表 2 使用不同采样方法的CNN模型分类结果对比

Table 2. Performance comparison of CNN models using different sampling methods

方法	ACC/%	F1/%	敏感度/%	特异度/%
随机采样	85.08	85.07	85.08	84.94
Spy	89.04	89.00	89.04	90.86
ISpy	91.91	91.90	91.91	93.17

下载: 导出CSV

表 3 崩塌易发性评价频率比统计结果

Table 3. Frequency ratio statistics of collapse susceptibility evaluation

易发性等级	Spy			ISpy
易发性等级	SVM	RF	CNN	SVM	RF	CNN
极低	0.00	0.00	0.03	0.03	0.00	0.01
低	0.32	0.22	0.48	0.47	0.10	0.16
中等	1.54	0.83	1.12	1.23	0.57	0.44
高	4.94	2.25	1.35	4.49	1.59	1.06
极高	6.48	7.46	7.83	6.75	8.00	8.19

下载: 导出CSV

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