基于环境因子优化TSES法选择负样本及其在滑坡易发性评价中的应用

崔玉龙; 朱路路; 徐敏; 缪海波

doi:10.19509/j.cnki.dzkq.tb20230400

基于环境因子优化TSES法选择负样本及其在滑坡易发性评价中的应用

doi: 10.19509/j.cnki.dzkq.tb20230400

安徽理工大学土木建筑学院, 安徽淮南 232001

基金项目:

国家自然科学基金项目 42277136

安徽省高校自然科学优秀青年科研项目 2023AH030041

详细信息

通讯作者:
崔玉龙，E-mail：ylcui@aust.edu.cn

中图分类号: P642.22
计量
- 文章访问数: 134
- PDF下载量: 3
- 被引次数: 0
出版历程
- 收稿日期: 2023-07-14
- 录用日期: 2023-09-08
- 修回日期: 2023-09-07

Optimizing TSES method based on the environmental factors to select negative samples and its application in landslide susceptibility evaluation

School of Civil Engineering and Architecture, Anhui University of Science and Technology, Huainan Anhui 232001, China

More Information

Corresponding author: CUI Yulong, E-mail: ylcui@aust.edu.cn

摘要

摘要:
滑坡易发性评价是滑坡灾害防治的重要手段之一, 而不合理的滑坡负样本会影响滑坡易发性评价, 从而影响到滑坡灾害的防治, 因此提供一种合理的负样本选取方法变得尤为关键。以西藏米林市的古滑坡为例, 选择高程、坡度、坡向、坡位、距道路距离、距断层距离、距水系距离、地形起伏度、地层岩性、土地利用类型10类环境因子, 使用Relief算法计算环境因子的贡献值并依据贡献值优化选择环境因子; 基于环境因子优化的目标空间外向化采样法(target space exteriorization sampling, 简称TSES)选择负样本, 作为性能优异的随机森林模型的输入变量; 之后结合优化的环境因子和正或负样本预测米林市的滑坡易发性, 并用混淆矩阵和ROC曲线评价构建模型的性能。为检验环境因子优化的TSES法的有效性和先进性, 采用耦合信息量法和TSES法选择滑坡负样本并构建随机森林模型, 与环境因子优化的TSES法构建的随机森林模型进行对比研究。结果表明, 环境因子优化的TSES法构建的随机森林模型的评价效果较好, 其ACC为93.7%、AUC为0.987, 均高于耦合信息量、TSES法构成的模型。环境因子优化的TSES法能够提高模型的精度, 解决多因子作为约束条件取样中因子选取的问题, 为滑坡易发性评价采集负样本提供了新的思路。
- 滑坡易发性评价 /
- Relief算法 /
- 负样本 /
- 环境因子优化TSES法 /
- 随机森林 /
- 古滑坡
Abstract: Objective
Landslide susceptibility evaluation is an important means for landslide disaster prevention and control. Unreasonable negative landslide samples will affect landslide susceptibility evaluation, thereby affecting landslide disaster prevention and control. Therefore, it is particularly critical to provide a reasonable negative sample selection method.
Methods
In view of the selection of negative landslide samples, the ancient landslides in Milin City, Xizang are taken as an example, and 10 environmental factors, including elevation, slope, slope aspect, slope position, distance to road, distance to fault, distance to water system, topographic relief, lithology and land use type, are selected. The Relief algorithm is used to calculate the contribution values of environmental factors and to select the optimized environmental factors. The target space exteriorization sampling (TSES) method based on the optimization of environmental factors is applied to select negative samples as the input variables of the random forest (RF)model with excellent performance.Then, the optimized environmental factors and positive/negative samples are combined to predict the landslide susceptibility of Milin city, and the confusion matrix and receiver operating characteristic (ROC) curve are used to evaluate the prediction performance.To test the effectiveness and advancement of the TSES method optimized by environmental factors, the coupled information method and TSES method are respectively used to select negative landslide samples and constructs RF models to conduct comparative research with the RF model constructed by the TSES method optimized by environmental factors.
Results
The results show that the evaluation effect of the RF model constructed by the optimized TSES method based on environmental factors is better with an ACC value of 93.7% and an AUC value of 0.987, both of which are greater than those of the RF models constructed by the coupling information method and the TSES method.
Conclusion
The TSES optimized by environmental factors can improve the accuracy of the RF model, solve the problems of environmental factor selection in multifactor constrained sampling, and provide a new approach to collect negative landslide samples for landslide susceptibility evaluation.
- landslide susceptibility evaluation /
- Relief algorithm /
- negative sample /
- TSES method based on the optimixation of environmental factor /
- random forest /
- ancient landslide
注释:

所有作者声明不存在利益冲突。

HTML全文

图 1 技术路线图

Figure 1. Technology roadmap

下载: 全尺寸图片幻灯片

图 2 目标空间外向化采样法

Figure 2. Target space exteriorization sampling method

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图 3 研究区地理位置

Figure 3. Geographical location of the study area

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图 4 滑坡解译实例

a.沿江的大型岩质滑坡；b.由风化引起的岩质滑坡；c.解译滑坡分布图

Figure 4. Examples of landslide interpretation

下载: 全尺寸图片幻灯片

图 5 影响因子分类

a.高程；b.坡度；c.坡向；d.坡位；e.距道路距离；f.距断层距离；g.距水系距离；h.地形起伏度；i.地层岩性；j.土地利用类型

Figure 5. Classification of influencing factors

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图 6 影响因子贡献值

Figure 6. Contribution values of influencing factors

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图 7 3种采集负样本方法构成随机森林易发图

a.耦合信息量法；b.TSES法；c.环境因子优化TSES法

Figure 7. Landslide susceptibility maps of random forest model by using three negative samples collecting methods

下载: 全尺寸图片幻灯片

图 8 3种采集负样本方法构成随机森林的ROC曲线

Figure 8. ROC curves of random forest model using three negative samples collecting methods

下载: 全尺寸图片幻灯片

表 1 影响因子数据来源

Table 1. Data sources of influencing factors

影响因子	数据名称	数据来源
高程、坡度、坡向、距水系距离、地形起伏度	12.5 m分辨率的ALOS数字高程模型(DEM)	http://wwwearthdate.org
坡位	地理空间数据云	https://www.gscloud.cn
距道路距离	全球地理信息资源目录服务系统	https://www.webmap.cn
距断层距离	中国活动构造图(1∶400万)
土地利用类型	鹏城实验室	https://data.ess.tsinghua.edu.cn
地层岩性	中国地质调查局公开的1∶50万中国地质图	http://www.ngac.org.cn

下载: 导出CSV

表 2 随机森林模型的混淆矩阵

Table 2. Confusion matrix of random forests model

模型种类	是否为滑坡(实际)	是否为滑坡(预测)		准确率ACC/%
模型种类	是否为滑坡(实际)	是	否	准确率ACC/%
耦合信息量法	是	340	72	88.1
随机森林模型	否	26	386	88.1
TSES法随机	是	365	47	79.4
森林模型	否	123	289	79.4
环境因子优化的	是	406	6	93.7
TSES法随机森林模型	否	46	366	93.7

下载: 导出CSV

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