基于分形维数耦合支持向量机和熵权模型的滑坡易发性研究

付泉; 党光普; 李致博; 田润青; 石琳; 赵鑫; 王昆; 石磊; 吕娜娜

doi:10.12401/j.nwg.2023196

基于分形维数耦合支持向量机和熵权模型的滑坡易发性研究

付泉^1, ,,
党光普¹,
李致博^1, ,,
田润青¹,
石琳¹,
赵鑫¹,
王昆^2,,
石磊²,
吕娜娜³

1.
陕西地建土地勘测规划设计院有限责任公司，陕西西安　710075
2.
陕西地建土地工程技术研究院有限责任公司，陕西西安　710075
3.
陕西水务发展供水集团有限公司，陕西西安　710018

基金项目: 陕西省重点研发计划项目（2024SF-YBXM-565），陕西地建土地勘测规划设计院2024年度内部科研项目（KCNY2024-2，KCNY2024-4），陕西省土地工程建设集团内部科研项目（DJNY-ZD-2023-1，DJNY-YB-2023-18，DJNY2024-18）和陕西地建—西安交大土地工程与人居环境技术创新中心开放基金（2024WHZ0240）联合资助。

详细信息

作者简介:
付泉（1992−），男，硕士，工程师，主要从事地质灾害与土地工程研究。E−mail：517043740@qq.com

通讯作者:
李致博（1987−），男，硕士，高级工程师，主要从事地理信息与数据挖掘方面的研究。E−mail：267001531@qq.com。

中图分类号: P694；P280
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- 文章访问数: 132
- HTML全文浏览量: 5
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出版历程
- 收稿日期: 2023-03-21
- 修回日期: 2023-09-23
- 录用日期: 2023-11-06
- 网络出版日期: 2024-01-15
- 刊出日期: 2024-12-19

Study of Landslide Susceptibility Mapping Based on Fractal Dimension Integrating Support Vector Machine with Index of Entropy Model

1.
Shaanxi Geological Construction Land Survey, Planning and Design Institute Co., Ltd, Xi'an 710075, Shaanxi, China
2.
Institute of Land Engineering and Technology, Shaanxi Provincial Land Engineering Construction Group Co., Ltd., Xi’an 710075, Shaanxi, China
3.
Shaanxi Water Development and Supply Group Co., Ltd., Xi'an 710018, Shaanxi, China

摘要

摘要:
陕西省宝鸡市北部黄土高原滑坡灾害频发，严重威胁当地人民的经济发展和生产生活。本研究基于分形维数，分别利用熵权模型（IOE）、支持向量机模型（SVM）和两种混合模型即F-IOE和F-SVM对滑坡可能发生的范围进行定量预测。首先，利用179个滑坡样本制作滑坡编录图，将70%（125个）的滑坡样本用于训练，其余30%（54个）用于测试。随后，提取12种滑坡影响因子，分别计算每个因子的信息增益率和分形维数，并使用训练数据建立4种滑坡易发性分区模型。最后，利用受试者工作特征曲线（ROC）和统计学指标包括阳性预测率（PPR）、阴性预测率（NPR）和准确率（ACC）测试模型的性能，并比较模型的泛化性。结果表明，F-SVM模型在训练和测试数据集上分别得到最高的PPR、NPR、ACC和AUC值，其次是F-IOE模型。最终，F-SVM模型在所有模型中表现最优，因此，基于分形维数构建的混合模型比原始模型更具优势，可为当地滑坡防治决策提供参考。
- GIS /
- 滑坡易发性研究 /
- 混合模型 /
- 分形维数
Abstract:
Landslides occur frequently on the Loess Plateau in the north of Baoji City, Shaanxi Province, which seriously threaten the economic development, production and life of the local people. Based on fractal dimension, entropy weight model (IOE), support vector machine model (SVM) and two hybrid models, namely F-IOE and F-SVM, are used to quantitatively predict the possible occurrence range of landslide. First of all, 179 landslide samples were used to make landslide cataloguing maps, 70% (125) of the landslide samples were used for training, and the remaining 30% (54) were used for testing. Then, 12 kinds of landslide influence factors are extracted, information gain rate and fractal dimension of each factor are calculated respectively, and four landslide vulnerability zoning models are established using training data. Finally, the performance of the model was tested using the receiver operating characteristic curve (ROC) and statistical indicators including positive predictive rate (PPR), negative predictive rate (NPR) and accuracy rate (ACC), and the generalization of the model was compared. The results show that F-SVM model has the highest PPR, NPR, ACC and AUC values in training and test data sets respectively, followed by F-IOE model. Finally, F-SVM model is the best among all models. Therefore, the hybrid model based on fractal dimension has more advantages than the original model, which can provide reference for local landslide control decisions.
- GIS /
- research on landslide susceptibility /
- mixed model /
- fractal dimension

HTML全文

图 1 研究区位置图（a）和滑坡编录图（b）

Figure 1. （a）Location of study area and（b） landslide inventory map

下载: 全尺寸图片幻灯片

图 2 各影响因子图

（a）.坡度；（b）.坡向；（c）.高程；（d）.距河流的距离；（e）.距断层的距离；（f）.距道路的距离；（g）.降雨量；（h）.地层岩性；（i）.NDVI；（j）.土地利用类型；（k）.平面曲率；（l）.剖面曲率

Figure 2. Map of various Conditioning factors

下载: 全尺寸图片幻灯片

图 3 分形维数计算示意图

Figure 3. The schematic about the calculation of fractal dimensions

下载: 全尺寸图片幻灯片

图 4 各影响因子的平均信息增益率图

Figure 4. Average Information Gain ratio of each conditioning factor

下载: 全尺寸图片幻灯片

图 5 对数图和线性方程图

Figure 5. Logarithmic graphs and linear equations

下载: 全尺寸图片幻灯片

图 6 滑坡易发性图

（a）.IOE 模型（LSM）；（b）.F-IOE 模型（LSM）；（c）.SVM 模型（LSM）；（d）.F-SVM 模型（LSM）

Figure 6. Landslide susceptibility map using

下载: 全尺寸图片幻灯片

图 7 滑坡易发性模型的ROC曲线图

（a）训练数据集；（b）测试数据集

Figure 7. ROC curves for landslide susceptibility models

下载: 全尺寸图片幻灯片

表 1 研究区地层岩性单元表

Table 1 Lithological units of study area

类别	地质年代	编码	主要地层岩性
A	新进纪	Q₄	砂，砾石，黄土
A	更新纪	Q₃	黄土，砾石
B	上新统	N₂	砂土
B	中新统	N₁	石英砂，黏土
C	晚白垩纪	K₁	泥岩，砂质泥岩，泥质砂岩
D	早侏罗纪	J₃	块状聚集物，粘胶岩，粉砂质泥岩
	中侏罗纪	J₂	石质，泥岩，粉质泥岩
	晚侏罗纪	J₁	粉砂岩，煤层
E	早三叠纪	T₃	泥岩，页岩，煤层
	中三叠纪	T₂	中细砂岩，粉砂岩，泥岩
	晚三叠纪	T₁	石质，细砂岩，粉砂岩，砂质泥岩
F	二叠纪	P	砂质泥岩，细砂岩，粉砂岩

下载: 导出CSV

表 2 影响因子的VIF和TOL表

Table 2 Variance inflation factors (VIF) and tolerances of each conditioning factor

影响因子	TOL	VIF
坡度	0.934	1.071
坡向	0.926	1.080
高程	0.656	1.525
距河流的距离	0.908	1.101
距道路的距离	0.877	1.141
距断层的距离	0.916	1.092
NDVI	0.597	1.675
土地利用类型	0.650	1.538
地层岩性	0.814	1.228
降雨量	0.814	1.229
平面曲率	0.912	1.096
剖面曲率	0.925	1.082

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表 3 影响因子与滑坡的空间关系表

Table 3 Spatial relationship between influencing factors and landslides

影响因子	等级	f_j	FR_ij	P_ij	H_j	H_jmax	I_j	W_j	F-W_j
坡度（°）	＜5	0.0772	0.3035	0.0367	2.2857	2.5850	0.1158	0.1595	0.1201
	5~10	0.1906	0.8709	0.1053
	10~15	0.2285	0.9124	0.1104
	15~20	0.2372	1.1518	0.1393
	20~25	0.1682	1.9427	0.2350
	＞25	0.2284	3.0864	0.3733
坡向	水平	0.0000	0.0000	0.0000	2.9004	3.1699	0.0850	0.0732	0.0946
	北	0.0459	0.6097	0.0787
	东北	0.0317	0.3564	0.0460
	东	0.2239	0.9802	0.1266
	东南	0.1567	1.0364	0.1338
	南	0.1429	1.1387	0.1470
	西南	0.1990	1.0215	0.1319
	西	0.2260	1.6248	0.2098
	西北	0.1251	0.9771	0.1262
高程（m）	＜850	0.5803	2.9085	0.4024	2.4666	2.8074	0.1214	0.1253	0.1279
	850~950	0.3346	1.1898	0.1646
	950~1050	0.2131	0.7202	0.0996
	1050~1150	0.1009	0.5549	0.0768
	1150~1250	0.0942	0.5738	0.0794
	1250~1350	0.1606	0.8630	0.1194
	＞1350	0.2092	0.4174	0.0578
距河流的距离（m）	＜200	0.3998	2.2738	0.4703	2.0154	2.3219	0.1320	0.1277	0.1009
	200~400	0.2220	0.8336	0.1724
	400~600	0.0539	0.4464	0.0923
	600~800	0.0706	0.8641	0.1787
	＞800	0.1942	0.4171	0.0863
距断层的距离（m）	＜2000	0.4358	1.2216	0.2275	2.2883	2.3219	0.0145	0.0155	0.1263
	2000~4000	0.2725	1.2343	0.2299
	4000~6000	0.3286	1.1496	0.2141
	6000~8000	0.3992	1.1191	0.2084
	＞8000	0.2951	0.6445	0.1200
距道路的距离（m）	＜100	0.4866	1.1476	0.5302	1.3723	2.0000	0.3139	0.1698	0.1030
	100~200	0.1407	0.7709	0.3562
	200~300	0.1334	0.2458	0.1130
	＞300	0.0000	0.0000	0.0000

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表 4 模型性能评价统计指标计算结果表

Table 4 Calculation result of statistical indicators for model performance evaluation

指标	模型
指标	IOE	SVM	F-IOE	F-SVM
真阳性	108	113	110	118
真阴性	104	110	109	110
假阳性	21	15	16	15
假阴性	17	12	15	7
PPR（%）	83.72	88.28	87.30	88.72
NPR（%）	85.95	90.16	87.90	94.02
ACC（%）	84.80	89.00	87.60	91.20

下载: 导出CSV

表 5 模型测试评价统计指标计算结果表

Table 5 Calculation result of statistical indicators for model validation evaluation

指标	模型
指标	IOE	SVM	F-IOE	F-SVM
真阳性	48	52	110	118
真阴性	47	48	109	110
假阳性	7	6	16	15
假阴性	6	2	15	7
PPR（%）	87.27	89.66	86.21	92.73
NPR（%）	88.68	96.00	92.00	94.34
ACC（%）	87.96	92.59	88.89	93.52

下载: 导出CSV

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1.	韩伟，李玉宏，任战利，刘晓晔，周俊林，李成福. 渭河盆地构造热演化对富氦天然气成藏的影响. 天然气地球科学. 2025(03): 390-398 . 百度学术
2.	康锐，范立勇，惠洁，李小燕，马晓峰，唐敏，郝乐伟. 鄂尔多斯盆地庆阳气田潜在氦源岩有效性评价. 天然气地球科学. 2025(03): 413-429 . 百度学术
3.	李剑，王晓波，徐朱松，崔会英，王晓梅，张斌，国建英，陶士振，陈践发，谢增业，田继先，王义凤. 中国氦气资源成藏规律与开发前景. 天然气地球科学. 2024(05): 851-868 . 百度学术
4.	Pengpeng LI，Quanyou LIU，Dongya ZHU，Di ZHU，Zheng ZHOU，Xiaoqi WU，Qingqiang MENG，Jiahao LV，Yu GAO. Distributions and accumulation mechanisms of helium in petroliferous basins. Science China Earth Sciences. 2024(10): 3143-3168 . 必应学术
5.	李朋朋，刘全有，朱东亚，朱地，Zheng ZHOU，吴小奇，孟庆强，吕佳豪，高宇. 含油气盆地氦气分布特征与成藏机制. 中国科学:地球科学. 2024(10): 3195-3218 . 百度学术
6.	司庆红，曾威，刘行，胡少华，张超，王佳营，黄忠峰，何鹏. 临汾–运城盆地氦气富集要素及成藏条件. 西北地质. 2023(01): 129-141 . 本站查看
7.	张健，张海华，贺君玲，卞雄飞，张德军，陈树旺，孙雷. 东北地区氦气成藏条件与资源前景分析. 西北地质. 2023(01): 117-128 . 本站查看
8.	赵欢欢，梁慨慷，魏志福，王永莉，曹春辉. 松辽盆地富氦气藏差异性富集规律及有利区预测. 天然气地球科学. 2023(04): 628-646 . 百度学术
9.	李玉宏，张国伟，周俊林，李济远，吕鹏瑞. 氦气资源调查理论与技术研究现状及建议. 西北地质. 2022(04): 1-10 . 本站查看
10.	晁海德，陈建洲，王国仓，赵维孝，蔡廷俊，李吉庆，李青，谢菁. 柴达木盆地水溶氦气资源的发现及富集机理. 西北地质. 2022(04): 61-73 . 本站查看
11.	李玉宏，张文，周俊林，李济远，张云鹏. 花岗岩在氦气成藏中的双重作用：氦源与储集. 西北地质. 2022(04): 95-102 . 本站查看
12.	赵安坤，王东，时志强，程锦翔，王启宇，何江林，李龙，雷子慧. 四川盆地及周缘地区氦气资源调查研究进展与未来工作方向. 西北地质. 2022(04): 74-84 . 本站查看