不同栅格尺寸下输电线路地质灾害易发性评价

邬礼扬; 殷坤龙; 曾韬睿; 刘书豪; 刘真意

doi:10.19509/j.cnki.dzkq.tb20220307

不同栅格尺寸下输电线路地质灾害易发性评价

doi: 10.19509/j.cnki.dzkq.tb20220307

a.
中国地质大学(武汉)工程学院, 武汉 430074
b.
中国地质大学(武汉)地质调查研究院, 武汉 430074

基金项目:

国家重点研发计划项目 2018YFC0809402

详细信息

作者简介:
邬礼扬, E-mail: wuliyang@cug.edu.cn

通讯作者:
殷坤龙, E-mail: yinkl@126.com

中图分类号: P642;P694
计量
- 文章访问数: 317
- PDF下载量: 47
- 被引次数: 0
出版历程
- 收稿日期: 2022-06-23
- 录用日期: 2022-09-28
- 修回日期: 2022-09-23

Evaluation of geological disaster susceptibility of transmission lines under different grid resolutions

a.
Faculty of Engineering, China University of Geosciences(Wuhan), Wuhan 430074, China
b.
Institute of Geological Survey, China University of Geosciences(Wuhan), Wuhan 430074, China

More Information

Author Bio:
WU Liyang, E-mail: wuliyang@cug.edu.cn

Corresponding author: YIN Kunlong, E-mail: yinkl@126.com

摘要

摘要:
输电线路的安全运行对国家经济建设与发展具有重要意义，而针对输电线路进行地质灾害易发性评价的研究较少。以京津冀地区的输电线路为例，选取高程、坡度、坡向、地形起伏度、地层岩性、距断层距离、距水系距离、土地利用类型8个指标因子，采用频率比法对各指标因子进行分级，构建易发性评价体系。再利用不同的机器学习模型，使用不同尺寸的栅格单元作为评价单元对研究区进行易发性评价。最后，选取精度最高的机器学习模型与传统的层次分析法完成研究区易发性区划图。研究结果表明：贝叶斯网络模型在区域输电线路易发性评价中的应用效果最好，模型性能最强，最高AUC值为0.876。与传统的层次分析法相比，BN模型在研究区易发性制图中的效果更好，精度更高。此外，采用50 m的栅格作为评价单元在研究区易发性评价中取得了最好的应用效果，研究成果为输电线路地质灾害易发性评价以及栅格尺寸的选用提供了思路以及参考。
- 输电线路 /
- 地质灾害 /
- 栅格尺寸 /
- 机器学习 /
- 易发性评价
Abstract: Objective
The safe operation of transmission lines is of great significance for national economic construction and development, but there were few studies on the evaluation of geological hazards susceptibility to transmission lines.
Methods
This study focuses on the Beijing-Tianjin-Hebei region as an example, where eight index factors, including elevation, slope, aspect, terrain relief, stratigraphic lithology, distance from fault, distance from water system, and land use type were selected. The frequency ratio method was used to classify each index factor to construct a susceptibility evaluation system.Then used different machine learning models and grid of different spatial resolutions as evaluation units to evaluate the susceptibility of the study area.Finally, the machine learning model with the highest accuracy and the traditional Analytic Hierarchy Process (AHP) were selected to complete the susceptibility zoning map of the study area.
Results
The research results show that the Bayesian Network model (Bayesian Network, BN) had the best application effect and the strongest model performance in the susceptibility evaluation of regional transmission lines, and the maximum AUC value was 0.876. The BN model outperformed the traditional AHP model, displaying superior precision in susceptibility mapping in the study area.
Conclusion
In addition, emplpying 50 m grid as the evaluation unit had achieved the best application effect in the evaluation of transmission line geological disaster susceptibility, which provided ideas and references for transmission line geological disaster evaluation and grid resolution selection.
- transmission line /
- geological disaster /
- grid resolution /
- machine learning /
- susceptibility evaluation
注释:

所有作者声明不存在利益冲突。

HTML全文

图 1 输电线路地质灾害易发性评价流程图

Figure 1. Flow chart of geological disaster susceptibility evaluation of transmission lines

下载: 全尺寸图片幻灯片

图 2 研究区输电线路位置(a)和地质灾害分布(b)

Figure 2. Location of transmission lines(a) and distribution of geological hazards(b) in the study area

下载: 全尺寸图片幻灯片

图 3 指标因子分区图

a.高程；b.坡度；c.坡向；d.地形起伏度；e.岩性；f.距断层距离；g.距水系距离；h.土地利用类型

Figure 3. Index factor partition map

下载: 全尺寸图片幻灯片

图 4 不同栅格尺寸机器学习模型测试集AUC值

Figure 4. AUC values of the machine learning model test set under different grid sizes

下载: 全尺寸图片幻灯片

图 5 地质灾害易发性区划图

a.BN模型; b.层次分析法模型

Figure 5. Geological disaster susceptibility zoning map

下载: 全尺寸图片幻灯片

表 1 判断矩阵标度及含义

Table 1. Meaning and scale of the judgment matrix

标度值	含义
1	2个因素相比，具有同等重要性
3	2个因素相比，前者比后者稍重要
5	2个因素相比，前者比后者明显重要
7	2个因素相比，前者比后者强烈重要
9	2个因素相比，前者比后者极端重要
2, 4, 6, 8	表示上述相邻判断的中间值
1/i(i=1, 2, …, 9)	与上述情况相反

下载: 导出CSV

表 2 指标因子多重共线性分析

Table 2. Multicollinearity analysis of index factors

评价因子	方差膨胀因子	容忍度
高程	1.255	0.797
坡度	4.273	0.234
坡向	1.002	0.998
地形起伏度	4.280	0.234
地层岩性	1.053	0.950
距断层距离	1.179	0.848
距水系距离	1.028	0.972
土地利用类型	1.021	0.980

下载: 导出CSV

表 3 不同模型的评估指标

Table 3. Evaluation indicators of different models

模型	阶段	AUC	ACC	precision	TPR	TNR	MCC	RMSE	MAE
RBF	训练	0.846	0.779	0.743	0.850	0.709	0.565	0.158	0.319
RBF	测试	0.844	0.775	0.742	0.848	0.701	0.557	0.157	0.319
BN	训练	0.877	0.816	0.767	0.906	0.726	0.644	0.132	0.242
BN	测试	0.876	0.813	0.768	0.902	0.723	0.637	0.133	0.243
SVM	训练	0.863	0.797	0.763	0.852	0.744	0.599	0.150	0.301
SVM	测试	0.857	0.797	0.774	0.861	0.728	0.595	0.152	0.303
MLP	训练	0.871	0.815	0.779	0.876	0.754	0.635	0.134	0.269
MLP	测试	0.871	0.810	0.777	0.873	0.746	0.626	0.136	0.272
LR	训练	0.773	0.738	0.714	0.781	0.695	0.479	0.191	0.385
LR	测试	0.819	0.787	0.787	0.805	0.768	0.575	0.175	0.372
注：AUC：预测为正的概率值比预测为负的概率值还要大的可能性；总体分类精度(accuracy, 简称ACC)：预测正确样本数/所有样本数；精确度(precision): 正确预测正分类数/预测为正样本分类数；TPR：预测为正类的样本数/所有正样本数；TNR：预测为反类的样本/反类样本总数；MCC(matthews correlation coefficient): 分类与预测分类之间的相关系数；均方根误差(RMSE)以及平均绝对误差(MAE)

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表 4 栅格统计

Table 4. Raster statistics

易发性等级	滑坡栅格数		各等级栅格数		占总栅格比例/%		占总灾害比例/%		灾害比率
易发性等级	层次分析法	BN	层次分析法	BN	层次分析法	BN	层次分析法	BN	层次分析法	BN
低	144	375	8 962 333	15 219 038	41.7	70.7	0.9	2.4	0.022	0.034
中	838	408	4 442 850	1 281 954	20.7	6.0	5.4	2.6	0.263	0.443
高	4 680	7 960	4 160 518	3 644 382	19.3	16.9	30.3	51.5	1.566	3.041
极高	9 792	6 711	3 947 443	1 380 308	18.3	6.4	63.4	43.4	3.453	6.768

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