基于信息量、逻辑回归及其耦合模型的滑坡易发性评估研究:以青海沙塘川流域为例

doi:10.19657/j.geoscience.1000-8527.2019.01.23

现代地质 ›› 2019, Vol. 33 ›› Issue (01): 235-245.DOI: 10.19657/j.geoscience.1000-8527.2019.01.23

基于信息量、逻辑回归及其耦合模型的滑坡易发性评估研究:以青海沙塘川流域为例

栗泽桐¹^,²(), 王涛¹(), 周杨³, 刘甲美¹, 辛鹏¹

1.中国地质科学院地质力学研究所,北京 100081
2.中国地质大学(北京),北京 100083
3.成都理工大学环境与土木工程学院,四川成都 610059

收稿日期:2018-05-03 修回日期:2018-11-30 出版日期:2019-02-26 发布日期:2019-02-28
通讯作者: 王涛
作者简介:王涛,男,博士,副研究员,1982年出生,地质工程专业,主要从事工程地质与地质灾害调查研究。Email:wangtaoig@cags.ac.cn。
栗泽桐,女,硕士研究生,1994年出生,地质工程专业,主要从事工程地质与地质灾害研究。Email:1185352118@qq.com。
基金资助:
国家自然科学基金项目(41572313);中国地质调查局项目(20160271);中国地质调查局项目(1212011220144)

Landslide Susceptibility Assessment Based on Information Value Model, Logistic Regression Model and Their Integrated Model: A Case in Shatang River Basin, Qinghai Province

LI Zetong¹^,²(), WANG Tao¹(), ZHOU Yang³, LIU Jiamei¹, XIN Peng¹

1. Institute of Geomechanics, Chinese Academy of Geological Sciences, Beijing 100081, China
2. China University of Geosciences, Beijing 100083, China
3. College of Environment and Civil Engineering, Chengdu University of Technology, Chengdu, Sichuan 610059, China

Received:2018-05-03 Revised:2018-11-30 Online:2019-02-26 Published:2019-02-28
Contact: WANG Tao

摘要/Abstract

摘要：

滑坡易发性定量评估是预测滑坡发生空间概率的重要手段,基于统计分析原理的评估方法目前在国内外应用最为广泛,且不同评估方法的对比研究逐渐成为热点。以青海沙塘川流域黄土梁峁区为例,剖析了信息量模型和逻辑回归模型在滑坡易发性评估中的优越性和局限性,并探索提出基于二者的耦合模型。考虑坡度、坡向、起伏度、岩性、与干流距离、与支流距离和植被指数等7个影响因素,对比分析了基于信息量、逻辑回归及二者耦合模型的滑坡易发性评估的技术流程及结果。3种模型的成功率分别为:耦合模型成功率(78.9%)>信息量模型成功率(71.8%)>逻辑回归模型成功率(70.8%)。在沙塘川流域黄土滑坡的易发性评估中,信息量和逻辑回归模型的表现基本相当,但信息量-逻辑回归耦合模型的成功率明显提升。该研究结果可为黄土高原区滑坡易发性定量评估提供借鉴。

关键词: 滑坡, 易发性评估, 信息量, 逻辑回归, 耦合模型

Abstract:

Quantitative landslide susceptibility assessment is important to predict the spatial probability of landslides. The assessment method based on statistical analysis principle is in present commonly adopted worldwide, and comparison of different assessment methods has become a research hot spot. The loess region of the Shatang River Basin in Qinghai Province was the focus of this study. Strengths and limitations of the information value and logistic regression models in landslide susceptibility assessment were analyzed and an integrated model was proposed. Seven influence factors including the slope, aspect, relief and lithology, distance from main/branch drainage, and the normalized difference vegetation index (NDVI) were analyzed and compared with the landslide susceptibility assessment results based on the three models. The results suggest that the successful rate decreases from the integrated model (78.9%), through the information value model (71.8%) to the logistic regression model (70.8%), which indicates that the performance of the latter two models are similar in the loess landslide susceptibility assessment in the Shatang River Basin, and the successful rate of the new method is obviously higher. This study provides a reference for the quantitative landslide susceptibility assessment in the loess plateaus.

Key words: landslide, susceptibility assessment, information value model, logistic regression model, integrated model

中图分类号:

栗泽桐, 王涛, 周杨, 刘甲美, 辛鹏. 基于信息量、逻辑回归及其耦合模型的滑坡易发性评估研究:以青海沙塘川流域为例[J]. 现代地质, 2019, 33(01): 235-245.

LI Zetong, WANG Tao, ZHOU Yang, LIU Jiamei, XIN Peng. Landslide Susceptibility Assessment Based on Information Value Model, Logistic Regression Model and Their Integrated Model: A Case in Shatang River Basin, Qinghai Province[J]. Geoscience, 2019, 33(01): 235-245.

图/表 8

参考文献 32

[1]	王念秦, 张倬元. 黄土滑坡灾害研究[M]. 兰州: 兰州大学出版社, 2005: 1-10.
[2]	XU X Z, GUO W Z, LIU Y K, et al. Landslides on the loess plateau of China: a latest statistics together with a close look[J]. Natural Hazards, 2017,86:1393-1403.
[3]	黄润秋, 许强. 中国典型灾难性滑坡[M]. 北京: 科学出版社, 2008: 1-20.
[4]	FELL R, COROMINAS J, BONNARD C, et al. Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning[J]. Engineering Geology, 2008,102:99-111.
[5]	COROMINAS J, WESTEN C, FRATTINI P, et al. Recommendations for the quantitative analysis of landslide risk[J]. Bulletin of Engineering Geology and the Environment, 2013,73(2):209-263.
[6]	FEIZIZADEH B, BLASCHKE T. An uncertainty and sensitivity analysis approach for GIS-based multicriteria landslide susceptibility mapping[J]. Geographical Information Science, 2014,28(3):610-638.
[7]	MONDAL S, MAITI R. Landslide susceptibility analysis of Shiv-Khola Watershed, Darjiling: A remote sensing & GIS based analy-tical hierarchy process (AHP)[J]. Journal of the Indian Society of Remote Sensing, 2012,40(3):483-496.
[8]	MYRONIDIS D, PAPAGEORGIOU C, THEOPHANOUS S. Landslide susceptibility mapping based on landslide history and analytic hierarchy process(AHP)[J]. Natural Hazards, 2016,81:245-263.
[9]	ABEDINI M, GHASEMYAN B, REZAEI MOGADDAM M H. Landslide susceptibility mapping in Bijar city, Kurdistan Province, Iran: a comparative study by logistic regression and AHP models[J]. Environmental Earth Sciences, 2017,76:10-14.
[10]	SOETERS R, VAN WESTEN C J. Slope instability recognition, analysis, and zonation[J]. Transport Research Board Special Report, 1996,247:129-177.
[11]	许冲, 戴福初, 徐素宁, 等. 基于逻辑回归模型的汶川地震滑坡危险性评价与检验[J]. 水文地质工程地质, 2013,40(3):98-104.
[12]	BAI S B, WANG J, GUO N L, et al. GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China[J]. Geomorphology, 2010,115:23-31.
[13]	LEE S, JOONG S W, JEON S, et al. Spatial landslide hazard prediction using rainfall probability and a logistic regression model[J]. Math Geosciences, 2015,47:565-589.
[14]	SARKAR S, ARCHANA K, ROY, et al. Landslide susceptibility assessment using information value method in parts of the Darjeeling Himalayas[J]. Journal of Geological Society of India, 2013,82:253-262.
[15]	YILMAZ I. Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides(Tokat-Turkey)[J]. Computers & Geosciences, 2009,35(6):1125-1138.
[16]	YILMAZ I. Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine[J]. Environmental Earth Sciences, 2010,61(4):821-836.
[17]	LINA G F, CHANGA M J, HUANG Y C, et al. Assessment of susceptibility to rainfall-induced landslides using improved self-organizing linear output map, support vector machine, and logistic regression[J]. Engineering Geology, 2017,224:62-74.
[18]	王佳佳, 殷坤龙, 肖莉莉. 基于GIS和信息量的滑坡灾害易发性评价——以三峡库区万州区为例[J]. 岩石力学与工程学报, 2014,33(4):797-808.
[19]	张俊, 殷坤龙, 王佳佳, 等. 三峡库区万州区滑坡灾害易发性评价研究[J]. 岩石力学与工程学报, 2016,35(2):284-295.
[20]	沈玲玲, 刘连友, 许冲, 等. 基于多模型的滑坡易发性评价——以甘肃岷县地震滑坡为例[J]. 工程地质学报, 2016,24(1):19-28.
[21]	范林峰, 胡瑞林, 曾逢春, 等. 加权信息量模型在滑坡易发性评价中的应用——以湖北省恩施市为例[J]. 工程地质学报, 2012,20(4):508-513.
[22]	SHARMA L P, PATEL N, GHOSE M K, DEBNATH P. Development and application of shannon’s entropy integrated information value model for landslide susceptibility assessment and zonation in Sikkim Himalayas in India[J]. Natural Hazards, 2015,75(2):1555-1576.
[23]	丛威清, 潘懋, 李铁锋, 等. 基于GIS的滑坡、泥石流灾害危险性区划关键问题研究[J]. 地学前缘, 2006,13(1):185-90.
[24]	王进, 郭靖, 王卫东, 等. 权重线性组合与逻辑回归模型在滑坡易发性区划中的应用与比较[J]. 中南大学学报(自然科学版), 2012,43(5):1932-1939.
[25]	AYALEW L, YAMAGISHI H. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan[J]. Geomorphology, 2005,65:15-31.
[26]	DU G L, ZHANG Y S, IQBAL J, et al. Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed, Gansu Province, China[J]. Journal of Mountain Science, 2017,14(2):249-268.
[27]	WU C Y, PENG J B, WANG M. Landslide and slope aspect in the Three Gorges Reservior area based on GIS and information value model[J]. Natural Sciences, 2013,11(4):773-779.
[28]	MORELLO R, CAPUA C D, LUGARÀ M, et al. Risk model for landslide hazard assessment[J]. Measurement and Technology, 2014,8(3):129-134.
[29]	王涛, 胡秋韵, 张永双, 等. 汶川震区成兰铁路关键段多尺度滑坡危险性评估[J]. 地质力学学报, 2014,20(4):379-391.
[30]	杜国梁, 张永双, 高金川, 等. 基于GIS的白龙江流域甘肃段滑坡易发性评价[J]. 地质力学学报, 2016,22(1):1-11.
[31]	王珂, 郭长宝, 马施民, 等. 基于证据权模型的川西鲜水河断裂带滑坡易发性评价[J]. 现代地质, 2016,30(3):705-715.
[32]	刘筱怡, 杨志华, 郭长宝, 等. 基于SBAS-InSAR的鲜水河断裂带蠕滑型滑坡特征研究[J]. 现代地质, 2017,31(5):965-977.

影响因素		因素区间范围	信息量	影响因素		因素区间范围	信息量
地形因素	坡度/(°)	0~4.37	-3.079	地层因素	岩性	N₁x	0.569
		4.37~9.58	-0.191			E₃m	0.487
		9.58~13.54	0.088			E₂h	0.243
		13.54~16.87	0.290	植被因素	NDVI	0.41~0.53	-1.510
		16.87~20.00	0.370			0.53~0.60	0.028
		20.00~23.12	0.357			0.60~0.64	0.113
		23.12~26.45	0.335			0.64~0.68	-0.057
		26.45~30.20	0.300			0.68~0.72	0.060
		30.20~35.20	0.202			0.72~0.76	0.095
		35.20~53.32	0.234			0.76~0.80	0.073
	坡向	平地	-2.924			0.80~0.84	-0.096
		N	0.097	0.84~0.90			-0.067
		NE	0.244	0.90~0.98			-0.765
		E	-0.027	河流侵蚀因素	距主流距离 /m	0~300	-1.252
		SE	-0.383			300~600	-0.136
		S	0.100			600~900	0.253
		SW	0.093			900~1 200	0.366
		W	-0.039			1 200~1 500	0.314
		NW	-0.077			1 500~1 800	-0.178
	起伏度 /m	3.03~52.69	-9.625			1 800~2 100	-0.123
		52.69~112.63	-4.648	2 100~2 400			0.145
		112.63~158.87	-1.474	2 400~2 700			0.257
		158.87~189.70	-0.414	>2 700			0.710
		189.70~215.39	0.093	距支流距离 /m		0~102.85	0.095
		215.39~239.37	0.241			102.85~219.42		0.042
		239.37~265.06	0.444		219.42~335.98		-0.024
		265.06~294.17	0.725		335.98~452.55		-0.024
		294.17~335.27	0.667		452.55~569.12		-0.085
		335.27~441.46	0.849		569.12~692.54		-0.130
地层因素	岩性	Q₄	-2.348		692.54~829.68	-0.135
		Q₂	-0.485	829.68~1 001.10	-0.053
		N₁xn	0.501	1 001.10~1 234.23	0.067
		N₁c	-0.346	1 234.23~1 741.64	0.299

影响因素		因素区间范围	信息量	影响因素		因素区间范围	信息量
地形因素	坡度/(°)	0~4.37	-3.079	地层因素	岩性	N₁x	0.569
		4.37~9.58	-0.191			E₃m	0.487
		9.58~13.54	0.088			E₂h	0.243
		13.54~16.87	0.290	植被因素	NDVI	0.41~0.53	-1.510
		16.87~20.00	0.370			0.53~0.60	0.028
		20.00~23.12	0.357			0.60~0.64	0.113
		23.12~26.45	0.335			0.64~0.68	-0.057
		26.45~30.20	0.300			0.68~0.72	0.060
		30.20~35.20	0.202			0.72~0.76	0.095
		35.20~53.32	0.234			0.76~0.80	0.073
	坡向	平地	-2.924			0.80~0.84	-0.096
		N	0.097	0.84~0.90			-0.067
		NE	0.244	0.90~0.98			-0.765
		E	-0.027	河流侵蚀因素	距主流距离 /m	0~300	-1.252
		SE	-0.383			300~600	-0.136
		S	0.100			600~900	0.253
		SW	0.093			900~1 200	0.366
		W	-0.039			1 200~1 500	0.314
		NW	-0.077			1 500~1 800	-0.178
	起伏度 /m	3.03~52.69	-9.625			1 800~2 100	-0.123
		52.69~112.63	-4.648	2 100~2 400			0.145
		112.63~158.87	-1.474	2 400~2 700			0.257
		158.87~189.70	-0.414	>2 700			0.710
		189.70~215.39	0.093	距支流距离 /m		0~102.85	0.095
		215.39~239.37	0.241			102.85~219.42		0.042
		239.37~265.06	0.444		219.42~335.98		-0.024
		265.06~294.17	0.725		335.98~452.55		-0.024
		294.17~335.27	0.667		452.55~569.12		-0.085
		335.27~441.46	0.849		569.12~692.54		-0.130
地层因素	岩性	Q₄	-2.348		692.54~829.68	-0.135
		Q₂	-0.485	829.68~1 001.10	-0.053
		N₁xn	0.501	1 001.10~1 234.23	0.067
		N₁c	-0.346	1 234.23~1 741.64	0.299

系数	常数项b₀	坡度b₁	坡向b₂	起伏度b₃	与干流距离b₄	与支流距离b₅	岩性b₆	NDVI b₇
系数值	-4.336	0.809	0.446	2.087	0.824	0.765	1.355	0.657
S_ig	0	0	0.043	0	0	0	0	0.001

系数	常数项b₀	坡度b₁	坡向b₂	起伏度b₃	与干流距离b₄	与支流距离b₅	岩性b₆	NDVI b₇
系数值	-4.336	0.809	0.446	2.087	0.824	0.765	1.355	0.657
S_ig	0	0	0.043	0	0	0	0	0.001

系数	常数项c₀	坡度c₁	坡向c₂	起伏度c₃	与干流距离c₄	与支流距离c₅	岩性c₆	NDVIc₇
系数值	-1.184	0.051	0.298	0.481	0.179	0.069	0.627	0.299
S_ig	0.000	0.000	0.044	0.000	0.009	0.015	0.000	0.001

基于信息量、逻辑回归及其耦合模型的滑坡易发性评估研究:以青海沙塘川流域为例

Landslide Susceptibility Assessment Based on Information Value Model, Logistic Regression Model and Their Integrated Model: A Case in Shatang River Basin, Qinghai Province

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