基于多模型的滑坡易发性评估研究

黄成; 邓云龙; 晏祥省; 周鑫城

doi:10.11932/karst20240615

基于多模型的滑坡易发性评估研究

doi: 10.11932/karst20240615

黄成^{1, 2, 3, 4,},
邓云龙^{1, 2, 3, ,},
晏祥省^{1, 2, 4},
周鑫城^{1, 2, 3}

1.
自然资源部高原山地地质灾害预报预警与生态保护修复重点实验室, 云南昆明 650216
2.
云南省高原山地地质灾害预报预警与生态保护修复重点实验室(筹), 云南昆明 650216
3.
昆明理工大学国土资源工程学院, 云南昆明 650093
4.
云南省地质环境监测院, 云南昆明 650216

基金项目: 云南省地质灾害综合防治体系建设专项计划（2013-2020）(云政发〔2013〕108号)；云南省地质灾害隐患识别中心建设（云财资环〔2021〕22号）；部省合作试点项目—云南高原山地地质灾害隐患综合垫遥感识别监测技术系统研究及应用（2023ZRBSHZ048）

详细信息

作者简介:
黄成（1981－），男，硕士，高级工程师，主要从事地质环境信息化与时空大数据应用、地质灾害隐患识别与综合遥感研究等工作。E-mail：hch2377@163.com

通讯作者:
邓云龙（1997－），男，硕士，主要从事遥感GIS和地质灾害研究。E-mail：1545079868@qq.com。

中图分类号: P642.22
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出版历程
- 收稿日期: 2024-01-01
- 修回日期: 2024-03-15
- 网络出版日期: 2025-03-21
- 刊出日期: 2024-12-25

A study on multiple-model evaluation of landslide susceptibility

HUANG Cheng^{1, 2, 3, 4
,},
DENG Yunlong^{1, 2, 3
, ,},
YAN Xiangsheng^{1, 2, 4},
ZHOU Xincheng^{1, 2, 3}

1.
Key Laboratory of Geohazard Forecast and Geoecological Restoration in Plateau Mountainous Area, MNR, Kunming, Yunnan 650216, China
2.
Yunnan Key Laboratory of Geohazard Forecast and Geoecological Restoration in Plateau Mountainous Area, Kunming, Yunnan 650216, China
3.
Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming, Yunnan 650093, China
4.
Yunnan Institute of Geo-Environment Monitoring, Kunming, Yunnan 650216, China

摘要

摘要: 滑坡是我国最常见的地质灾害之一，其突发性和不确定性给防灾减灾工作带来巨大的挑战。滑坡易发性评估是一个复杂的过程，常规手段是依赖于静态因子分析，难以实现滑坡易发性的动态评估。随着合成孔径雷达干涉测量（InSAR）技术的发展，可实现地表形变的动态监测，文章以双江县作为研究区，在常规地质灾害易发性评价中，引入地表形变表征性因子，并使用三种模型分别进行滑坡易发性评估，改善了滑坡地质灾害易发性评估的不确定性，提高了评估精度。综合考量研究区地形地貌、地质构造、水文环境、人类工程活动等静态地质环境条件因子，同时引入InSAR地表形变速率动态因子，共同构建多维度的评估指标体系，并使用信息量模型、确定性系数模型和频率比模型进行滑坡区域易发性评估比较。试验结果表明，CF模型在高易发区和较高易发区滑坡密度比值较高，为7.77、1.10，其准确值和AUC值最大，分别为0.822、0.879，均优于其他模型。基于InSAR技术获取地表形变因子，结合CF模型的滑坡易发性具有最好的评估精度。使用滑坡密度比值、ROC曲线和AUC评估绘制出的滑坡易发性图的精度更具有竞争优势。
- 滑坡易发性 /
- InSAR /
- 信息量 /
- 确定性系数 /
- 频率比
Abstract: Landslides are one of the most common geological disasters in China, characterized by sudden occurrence and uncertainty. The evaluation of landslide susceptibility is a complex process. Conventional methods mainly use static factors, making it difficult to achieve dynamic assessment of landslide susceptibility. With the ongoing advancement of science and technology, interferometric synthetic aperture radar (InSAR) has been successively applied to the study of geological disasters. This technology is characterized by its all-weather capability, continuous operation, and extensive coverage, allowing for real-time monitoring of the Earth's surface under varying environmental conditions. InSAR enables a comprehensive understanding of the movement of the surface rocks and soil masses associated with landslide geological disasters. It effectively captures the dynamic deformation characteristics of landslides in the vertical direction, thereby enhancing the identification and dynamic monitoring of surface deformation and improving the accuracy of evaluating landslide susceptibility. In this study, the surface deformation representative factor has been introduced into the conventional evaluation of geological disaster susceptibility. This addition improves the reliability of the evaluation of landslide susceptibility and enhances the overall accuracy.This study focused on Shuangjiang county as the research area. It utilizes evaluation index factors such as Digital Elevation Model (DEM), slope gradients, aspects, curvatures, stratigraphic lithology, faults, land use, annual average rainfall, roads, and rivers. The representative factor of InSAR surface deformation was comprehensively considered to evaluate landslide susceptibility. Through an extensive analysis of InSAR deformation, a dataset of landslides was established, identifying a total of 116 landslide geological disasters. Among them, 56 landslide areas exhibited deformation, with some slopes showing significant signs of deformation. The information quantity, certainty factor, and frequency ratio models were employed to evaluate the susceptibility of areas to landslides. The accuracy of the generated landslide susceptibility was evaluated with the use of the landslide density ratio, curve of Receiver Operating Characteristic (ROC), and the Area Under the Curve (AUC). In this study, 70% of the landslide events were randomly selected for spatial modeling training, while the remaining 30% were used for model verification. The segment set statistical tool in ArcGIS software was utilized to conduct the mutual independence test on the evaluation factors. Research findings indicate that all the correlation coefficients are less than 0.3, suggesting that the evaluation factors are independent of one another. According to the natural paragraph point method in Geographic Information System (GIS), the susceptibility can be categorized into five intervals: low susceptibility area, relatively low susceptibility area, medium susceptibility area, relatively high susceptibility area, and high susceptibility area. The high landslide susceptibility areas are mainly distributed in the northern part of Shuangjiang county; the low landslide susceptibility areas are mainly concentrated in its northwestern part. In the relatively high susceptibility area and the high susceptibility area, the raster of the inspection samples accounts for 88.69% of the total landslide inspection raster. The experimental results show that the Certainty Factor (CF) model exhibits a relatively high landslide density ratio in both the high susceptibility area and the relatively high susceptibility area, with ratios of 7.77 and 1.10, respectively. Additionally, the model demonstrates the highest accuracy and AUC values, which are 0.822 and 0.879, respectively. The accuracy of Frequency Ratio (FR) model is followed by CF model, and that of Information Quantity (I) model is the lowest. The landslide susceptibility map generated by the CF model provides a more accurate evaluation of slope instability in Shuangjiang county. Therefore, deriving the surface deformation factor based on InSAR technology and the CF model for evaluating landslide susceptibility yields the highest accuracy.
- landslide susceptibility /
- InSAR /
- information quantity /
- certainty factor /
- frequency ratio

HTML

图 1 InSAR形变速率分布图

Figure 1. Distribution of InSAR deformation rate

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图 2 评价因子分级图

Figure 2. Grading chart of evaluation factors

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图 3 模型易发性图

Figure 3. Model susceptibility

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图 4 滑坡点密度比值图

Figure 4. Ratio of landslide site density

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图 5 ROC曲线和AUC值

Figure 5. ROC curve and AUC values

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表 1 评价因子相关系数矩阵

Table 1. Correlation coefficient matrix of evaluation factors

评价因子	a	b	c	d	e	f	g	h	i	j	k	l
a	1.000
b	0.048	1.000
c	−0.044	−0.018	1.000
d	−0.184	−0.025	0.028	1.000
e	0.148	0.082	−0.146	−0.103	1.000
f	0.152	0.182	0.109	−0.140	−0.224	1.000
g	0.201	0.014	−0.078	−0.143	0.116	0.034	1.000
h	−0.043	−0.011	0.016	0.016	−0.018	−0.004	−0.038	1.000
i	0.002	0.012	0.002	0.004	−0.001	0.052	0.025	0.001	1.000
j	−0.007	0.005	0.016	0.001	0.003	−0.096	0.044	0.067	−0.016	1.000
k	−0.020	0.017	−0.017	−0.008	−0.004	0.014	0.036	−0.052	0.038	0.042	1.000
l	0.033	0.039	0.061	−0.030	0.111	−0.065	−0.039	0.003	−0.001	0.017	−0.021	1.000
注：a.道路 b.河流 c.断层 d.土地利用 e.降雨量 f.DEM g.坡度 h.坡向 i.曲率 j.降轨形变速率 k.升轨形变速率 l.地层岩性。 Note: a. road b. river c. fault d. land use e. rainfall f. DEM g. slope gradient h. aspect i.curvature j. orbital deformation rate k. rail lifting deformation rate l. stratigraphic lithology.

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表 2 评价因子分级计算

Table 2. Grading calculation of evaluation factors

评价因子	分类	I	CF	FR	断层缓冲区/m	900~1 200	0.723 7	0.515 3	2.053 6
降轨形变速率/mm·y⁻¹	＜−15	−1.419 0	−0.758 1	0.242 0	断层缓冲区/m	＞1 200	−0.436 3	−0.353 7	0.664 9
	−15~−5	−0.514 3	−0.402 2	0.597 9	河流缓冲区/m	0~200	−0.598 4	−0.450 4	0.528 5
	−5~5	0.272 1	0.238 3	1.312 7		200~400	0.2139	0.1926	1.3107
	5~15	0.474 8	0.378 1	1.607 6		400~600	0.2823	0.2460	1.3414
	＞15	−1.004 6	−0.633 9	0.366 2		600~800	−1.2248	−0.7063	0.3191
升轨形变速率/mm·y⁻¹	＜−15	−1.134 4	−0.678 5	0.321 6		800~1000	−0.2825	−0.2462	0.7612
	−15~−5	−0.181 2	−0.165 8	0.834 3		＞1000	0.1779	0.1631	1.1695
	−5~5	0.079 6	0.076 6	1.082 9	降雨量/mm	＜1150	1.0940	0.6654	2.9087
	5~15	0.290 7	0.252 3	1.337 3		1150~1200	0.1038	0.0986	1.1266
	＞15	−1.233 9	−0.708 9	0.291 1		1200~1250	−0.6966	−0.5018	0.5182
DEM/m	＜1000	−2.0000	−1.0000	0.0000		1250~1300	−1.1307	−0.6773	0.3424
	1000~1500	0.0387	0.0379	1.0414		1300~1350	−1.5561	−0.7891	0.2215
	1500~2000	0.4885	0.3866	1.6270		＞1350	−1.9933	−0.8638	0.1430
	2000~2500	−1.7878	−0.8327	0.1688	道路缓冲区/m	0~200	1.0529	0.6514	2.9438
	＞2500	−2.0000	−1.0000	0.0000		200~400	0.2886	0.2508	1.3625
坡度/°	0~5	−1.9420	−0.8566	0.1405		400~600	0.3021	0.2608	1.3413
	5~15	−0.0195	−0.0193	0.9383		600~800	0.9409	0.6100	2.5809
	15~25	−0.1405	−0.1311	0.9131		800~1000	0.6058	0.4546	1.6748
	25~35	0.2523	0.2231	1.2455		＞1000	−0.9685	−0.6204	0.3770
	35~45	0.2097	0.1892	1.2594	土地利用	建设用地	−1.3142	−0.7314	0.1881
	＞45	0.2392	0.2128	1.3336		林地	−0.5301	−0.4115	0.5889
坡向/°	平面	−2.0000	−1.0000	0.0000		水域	−0.4605	−0.3691	0.5888
	北坡	−0.8777	−0.5843	0.3920		耕地	0.6864	0.4968	1.9965
	东北坡	−1.3344	−0.7368	0.2735		草地	0.1144	0.1081	1.1126
	东坡	−0.0745	−0.0718	0.9857	地层岩性	Pz₁ln^b	1.0508	0.6506	2.7529
	东南坡	0.5670	0.4330	1.7808		Pz₁ln^a	1.0322	0.6441	2.8523
	南坡	0.8518	0.5736	2.2895		J₂s	−1.3024	−0.7282	0.2976
	西南坡	−0.5084	−0.3987	0.6242		N₁	1.9544	0.8587	7.2381
	西坡	−0.7342	−0.5202	0.4891		D_2-3	−2.1400	−0.8824	0.1441
	西北坡	−0.4770	−0.3795	0.5792		Pz₁ln^c	−1.6072	−0.7996	0.2044
曲率	＜0	0.0997	0.0949	1.0999		Q	0.2877	0.2501	1.3066
	l	0.1511	0.1403	1.1598		Pt	0.0295	0.0290	0.9839
	＞0	−0.1347	−0.1261	0.8793		T₃sc	−2.6868	−0.9319	0.0953
断层缓冲区/m	0~300	0.2659	0.2336	1.3440		C₁b	−0.6366	−0.4710	0.3703
	300~600	0.3696	0.3091	1.3927		γ$_5^{1}$	−0.4582	−0.3677	0.6640
	600~900	0.4485	0.3616	1.4663		γ m$_5^{1}$	0.0514	0.0502	1.0718

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表 3 检验样本占比表

Table 3. Proportions of test samples

模型	易发性等级	I/%	CF/%	FR/%
检验样本占比	低	0.00	0.00	0.68
	较低	1.81	2.49	7.47
	中	9.50	8.60	16.97
	较高	22.17	20.36	22.85
	高	66.52	68.55	52.04

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表 4 混淆矩阵

Table 4. Confusion matrix

	是否滑坡（实际）	预测结果		准确值
	是否滑坡（实际）	是	否	准确值
I	是	1123	277	0.795
I	否	314	1160	0.795
FR	是	1166	289	0.806
FR	否	271	1148	0.806
CF	是	1203	280	0.822
CF	否	234	1157	0.822

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