Landslide Proneness Evaluation Based on GIS Platform in Urban Area of Yichang City, Hubei Province

SUN Xiaofan; ZHANG Peng; DANG Chao

doi:10.13961/j.cnki.stbctb.2018.06.046

您当前的位置：

首页 >

文章列表页 >

Landslide Proneness Evaluation Based on GIS Platform in Urban Area of Yichang City, Hubei Province

- Landslide Proneness Evaluation Based on GIS Platform in Urban Area of Yichang City, Hubei Province
- Bulletin of Soiland Water Conservation Vol. 38, Issue 6, Pages: 304-309(2018)
- 作者机构：
  
  1. 三峡库区地质灾害教育部重点实验室,湖北,宜昌,443002
  2. 三峡大学土木与建筑学院,湖北,宜昌,443002
- 作者简介：
- 基金信息：
- DOI：10.13961/j.cnki.stbctb.2018.06.046
  CLC：
- Published：2018
- 稿件说明：
移动端阅览
SUN Xiaofan, ZHANG Peng, DANG Chao. Landslide Proneness Evaluation Based on GIS Platform in Urban Area of Yichang City, Hubei Province[J]. Bulletin of Soiland Water Conservation, 2018, 38(6): 304-309.
DOI：

SUN Xiaofan, ZHANG Peng, DANG Chao. Landslide Proneness Evaluation Based on GIS Platform in Urban Area of Yichang City, Hubei Province[J]. Bulletin of Soiland Water Conservation, 2018, 38(6): 304-309. DOI： 10.13961/j.cnki.stbctb.2018.06.046.

摘要

[目的] 对城市滑坡灾害进行易发性分区评价，为城市规划与防灾减灾工程提供理论依据。[方法] 以湖北省宜昌市城区为研究区，通过GIS平台选取高程、坡度、地层岩性、归一化植被指数（NDVI）、与水系的距离和道路密度等6个评价因子，采用似然比方法分析评价因子和滑坡发育的关系，并以归一化似然比值将评价因子参数分类量化；以量化值作为Logistic回归模型的自变量，抽取样本数据建立滑坡易发性评价回归模型。[结果] 评价因子结果显著，模型的整体准确率达到79.2%，ROC曲线下面积达0.871；极低易发区和低易发区占全区面积的61.59%，包含滑坡灾害的11.29%；高易发区和极高易发区虽仅占全区面积的17.88%，却发育了68.55%的滑坡灾害，结果与滑坡灾害分布特征相符合。[结论] 对宜昌市城区的滑坡易发性进行了等级划分。采用GIS和Logistic回归相结合的滑坡易发性评价方法，结果准确可靠。

Abstract

[Objective] In order to provide a theoretical basis for urban planning and disaster prevention and mitigation engineering

the zoning evaluation on urban landslide proneness was conducted.[Methods] The evaluation was conducted at urban area of Yichang City

Hubei Province. Evaluation indicators

i.e. elevation

slope gradient

lithology

normalized difference vegetation index(NDVI)

distance to watercourse and roading density

were identified by GIS platform; the relations between landslide proneness and evaluation indicators were analyzed based on likelihood ratio method

and the evaluation indicators could be quantified using the generalized likelihood ratio. As the independent variable in Logistic regression model

the regression model of landslide proneness evaluation was established based on sample datum.[Results] The significance of evaluation indicators was tested notable. The overall accuracy and the area under the ROC curve of evaluation model reached to 79.2% and 0.871 respectively. The extremely low proneness zone and the low proneness zone covered 61.59% of the total area

where landslide contributed 11.29% of the total landslides. Landslides in the high proneness zone and the extremely high proneness zone accounted for 68.55%

although it covered only 17.88% of the total area. The evaluation outcomes were conincided with the distribution of historical landslides by and large.[Conclusion] The landslide proneness of urban area of Yichang City is classified. The result of landslide proneness evaluation based on GIS and Logistic regression model is accurate and reliable.

关键词

Keywords

references

殷坤龙,朱良峰.滑坡灾害空间区划及GIS应用研究[J].地学前缘,2001,8(2):279-284.

丛威青,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):185-190.

许冲,戴福初,姚鑫,等.GIS支持下基于层次分析法的汶川地震区滑坡易发性评价[J].岩石力学与工程学报,2009,28(S2):3978-3985.

Lee S,Min K. Statistical analysis of landslide susceptibility at Yongin,Korea[J]. Environmental Geology,2001,40(9):1095-1113.

许湘华.用Logistic回归模型编制滑坡灾害敏感性区划图的方法研究[J].铁道科学与工程学报,2010,7(5):87-91.

曾忠平,汪华斌,张志,等.地理信息系统/遥感技术支持下三峡库区青干河流域滑坡危险性评价[J].岩石力学与工程学报,2006,25(S1):2777-2784.

司康平,田原,汪大明,等.滑坡灾害危险性评价的3种统计方法比较:以深圳市为例[J].北京大学学报:自然科学版,2008,45(4):19-26.

阮沈勇,黄润秋.基于GIS的信息量法模型在地质灾害危险性区划中的应用[J].成都理工学院学报,2001,28(1):89-92.

高克昌,崔鹏,赵纯勇,等.基于地理信息系统和信息量模型的滑坡危险性评价:以重庆万州为例[J].岩石力学与工程学报,2006,25(5):991-996.

宁娜,马金珠,张鹏,等.基于GIS和信息量法的甘肃南部白龙江流域泥石流灾害危险性评价[J].资源科学,2013,35(4):892-899.

王佳佳,殷坤龙,肖莉丽.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.

Pradhan B J, Lee S. Utilization of optical remote sensing data and GIS tools for regional landslide hazard analysis using an artificial neural network model[J]. Earth Science Frontiers, 2007,14(6):143-152.

赵向辉,付忠良,谢会云,等.神经网络和集成学习在地质灾害危险度区划中的应用研究[J].工程科学与技术,2010,42(S1):50-55.

孙正超.基于GIS和Logistic回归模型的区域泥石流危险性评价[D].四川成都:西南石油大学,2016.

王济川.Logistic回归模型[M].北京:高等教育出版社,2001.

刘斌.基于WEBGIS的滑坡灾害空间预测与系统开发研究:以三峡坝区至巴东段为例[D].湖北武汉:中国地质大学,2009.

王卫东,陈燕平,钟晟.应用CF和Logistic回归模型编制滑坡危险性区划图[J].中南大学学报:自然科学版,2009,40(4):1127-1133.

兰恒星,伍法权,周成虎,等.基于GIS的云南小江流域滑坡因子敏感性分析[J].岩石力学与工程学报,2002,21(10):1500-1506.

胡爱军,李宁,吴吉东,等.基于经验似然比函数模型的降水型滑坡灾害概率风险分析与预测[J].灾害学,2009,24(3):1-6.

王卫东,钟晟.基于GIS的Logistic回归模型在地质灾害危险性区划中的应用[J].工程勘察,2009(11):5-10.

Lee S. Application of likelihood ration and logistic regression models to landslide susceptibility mapping using GIS[J]. Environmental Management, 2005,34(2):223-232.

李雪平.基于GIS的区域斜坡稳定性评价Logistic回归模型研究[D].湖北武汉:中国地质大学,2005.

李春林,刘淼,胡远满,等.基于增强回归树和Logistic回归的城市扩展驱动力分析[J].生态学报,2014,34(3):727-737.

Can T,Nefeslioglu H A,Gokceoglu C,et al. Susceptibility assessments of shallow earthflows triggered by heavy rainfall at three catchments by logistic regression analyses[J]. Geomorphology, 2005,72(1):250-271.

Views

1721

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Using TM to Monitor the Desertification in West China

Dynamic Changes of Sandy Desertification Land in North of Otindag Sandy Land

Spatial and Temporal Variability of Landuse and Soil Erosion at Hailun County

GIS-Based Quantitative Study of Soil Erosion in Taiyuan City

An Analysis of Spatiotemporal Land Use Patterns in Yulin City Based on GIS and RS Techniques

Related Author

WANG Yi-mou

WANG Li-wen

WEI Ya-xing

Guo Jian

Chen Guanging

FENG Jianmin

WANG Tao

MA Shaoxiu

Related Institution

Resource Environment and City Science Department, Xianyang Normal University

Key Laboratory of Desert and Desertification, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences

Heilongjiang Institute of Soil and Water Conservation, Binxian

Heilongjiang University

Graduate University of Chinese Accdemy of Science

⁰