CSCD来源期刊

北大中文核心期刊

中国科技核心期刊

地学领域高质量科技期刊分级T2区（2023）

世界期刊影响力指数报告Q1区（2023）

Volume 39 Issue 2
Mar.  2020
Turn off MathJax
Article Contents
Article Navigation >  Bulletin of Geological Science and Technology  > 2020  >  39(2): 113-121
Hu Tao, Fan Xin, Wang Shuo, Guo Zizheng, Liu Aichang, Huang Faming. Landslide susceptibility evaluation of Sinan County using logistics regression model and 3S technology[J]. Bulletin of Geological Science and Technology, 2020, 39(2): 113-121. doi: 10.19509/j.cnki.dzkq.2020.0212
Citation: Hu Tao, Fan Xin, Wang Shuo, Guo Zizheng, Liu Aichang, Huang Faming. Landslide susceptibility evaluation of Sinan County using logistics regression model and 3S technology[J]. Bulletin of Geological Science and Technology, 2020, 39(2): 113-121. doi: 10.19509/j.cnki.dzkq.2020.0212
shu

Landslide susceptibility evaluation of Sinan County using logistics regression model and 3S technology

doi: 10.19509/j.cnki.dzkq.2020.0212
  • Received Date: 23 Sep 2018
    Abstract
  • The Sinan County of Guizhou Province, due to the specific and complex physical geography and geological conditions, is seriously affected by the landslide hazards. Hence, it is very necessary to conduct regional landslide susceptibility evaluation for landslide prediction and prevention in the area. This study uses 3S technology:remote sensing (RS), globe position system (GPS) and geographic information system (GIS), to evaluate landslide susceptibility based on the logistic regression (LR) model. The 3S technology is applied to obtain the landslide inventory, condition factors of landslides and other related basic data in Sinan County. About 308 landslides and ten affecting factors are acquired digital elevation model (DEM), slope, aspect, profile curvature, rock types, buffer of fracture lines, modified normalized difference water index (MNDWI), distance to river, normalized difference vegetation index (NDVI) and normalized difference building index (NDBI), using the 3S technology. Then based on the correlation analysis, LR model is used to calculate the landslide susceptibility indexes and map these indexes. Results show that, the area under the curve (AUC) of receiver operating characteristic curve (ROC) is 0.797 using LR model. The landslide distribution characteristics of Sinan County are accurately predicted by the LR model. In addition, the high and very high susceptible areas are mainly distributed in the areas where the DEM are higher than 600 m. In these areas, the slope are relatively great and the rocks are soft. The low and very low susceptible areas are mainly distributed in the areas where the DEM are high, the slopes are relatively low and the rocks are of hard rock class.

     

    • FullText(HTML)
  • loading
    • References(30)
  • [1]
    Wang Yang, Liu Jizhixian, Yan Shuangjie, et al.Estimation of probability distribution of shear strength of slip zone soils in Middle Jurassic red beds in Wanzhou of China[J].Landslides, 2017, 14(6):2165-2174. doi: 10.1007/s10346-017-0890-z
    [2]
    Jiang S H, Huang J, Huang F, et al.Modelling of spatial variability of soil undrained shear strength by conditional random fields for slope reliability analysis[J].Applied Mathematical Modelling, 2018, 63:374-389. doi: 10.1016/j.apm.2018.06.030
    [3]
    刘磊, 殷坤龙, 王佳佳, 等.降雨影响下的区域滑坡危险性动态评价研究:以三峡库区万州主城区为例[J].岩石力学与工程学报, 2016, 35(3):558-569. http://www.cnki.com.cn/Article/CJFDTotal-YSLX201603013.htm
    [4]
    王芳, 殷坤龙, 桂蕾, 等.不同日降雨工况下万州区滑坡灾害危险性分析[J].地质科技情报, 2018, 37(1):190-195, 203. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dzkjqb201801026
    [5]
    黄发明, 殷坤龙, 何涛, 等.库岸滑坡地下水位时间序列混沌特征识别与PSO-LSSVM模型预测[J].地质科技情报, 2015, 34(6):186-192. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dzkjqb201506027
    [6]
    Huang F M, Yin K L, Huang J S, et al.Landslide susceptibility mapping based on self-organizing-map network and extreme learning machine[J].Engineering Geology, 2017, 223:11-22. doi: 10.1016/j.enggeo.2017.04.013
    [7]
    Huang F M, Yao C, Liu W P, et al.Landslide susceptibility assessment in the Nantian area of China: A comparison of frequency ratio model and support vector machine[J].Geomatics, Natural Hazards and Risk, 2018, 9(1):919-938. doi: 10.1080/19475705.2018.1482963
    [8]
    Chang Z L, Du Z, Zhang F, et al.Landslide susceptibility prediction based on remote sensing images and GIS:Comparisons of supervised and unsupervised machine learning models[J].Remote Sensing, 2020, 12(3):502. doi: 10.3390/rs12030502
    [9]
    Huang F M, Chen L X, Yin K L, et al.Object-oriented change detection and damage assessment using high-resolution remote sensing images, Tangjiao Landslide, Three Gorges Reservoir, China[J].Environmental Earth Sciences, 2018, 77(5):183. doi: 10.1007/s12665-018-7334-5
    [10]
    Li Y Y, Huang J S, Jiang S H, et al.A web-based GPS system for displacement monitoring and failure mechanism analysis of reservoir landslide[J].Scientific Reports, 2017, 7(1):17171. doi: 10.1038/s41598-017-17507-7
    [11]
    Huang F M, Wu P, Ziggah Y Y.GPS monitoring landslide deformation signal processing using time-series model[J].International Journal of Signal Processing, Image Processing and Pattern Recognition, 2016, 9(3):321-332. doi: 10.14257/ijsip.2016.9.3.28
    [12]
    肖鹏, 李安龙.基于GIS的黄河水下三角洲海底滑坡稳定性预测[J].地质科技情报, 2016, 35(3):221-226. http://www.cqvip.com/QK/93477A/201603/669167100.html
    [13]
    管全中, 董大忠, 王玉满, 等.层次分析法在四川盆地页岩气勘探区评价中的应用[J].地质科技情报, 2015, 34(5):91-97. http://www.cnki.com.cn/Article/CJFDTotal-DZKQ201505015.htm
    [14]
    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):284-296.
    [15]
    郭天颂, 张菊清, 韩煜, 等.基于粒子群优化支持向量机的延长县滑坡易发性评价[J].地质科技情报, 2019, 38(3):236-243. http://www.cqvip.com/QK/93477A/20193/7002215554.html
    [16]
    Aditian A, Kubota T, Shinohara Y.Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia[J].Geomorphology, 2018, 318:101-111. doi: 10.1016/j.geomorph.2018.06.006
    [17]
    Huang F M, Yin K L, Zhang G R, et al.Landslide displacement prediction using discrete wavelet transform and extreme learning machine based on chaos theory[J].Environmental Earth Sciences, 2016, 75(20):1-18. doi: 10.1007/s12665-016-6133-0
    [18]
    Zhou Chao, Yin Kunlong, Huan Faming.Application of the chaotic sequence WA-ELM coupling model in landslide displacement prediction[J].Rock and Soil Mechanics, 2015, 36(9):2674-2680. http://www.cqvip.com/QK/94551X/201509/666084759.html
    [19]
    Huang F M, Zhang J, Zhou C B, et al.A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction[J].Landslides, 2020, 17(1):217-229. doi: 10.1007/s10346-019-01274-9
    [20]
    Li D Y, Huang F M, Yan L X, et al.Landslide susceptibility prediction using particle-swarm-optimized multilayer perceptron: Comparisons with multilayer-perceptron-only, BP Neural Network, and Information Value Models[J].Applied Sciences, 2019, 9(18):3664. doi: 10.3390/app9183664
    [21]
    Biswajeet Pradhan.A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS[J].Computers & Geosciences, 2013, 51:350-365.
    [22]
    杨永刚, 殷坤龙, 赵海燕, 等.基于C5.0决策树-快速聚类模型的万州区库岸段乡镇滑坡易发性区划[J].地质科技情报, 2019, 38(6):189-197. http://www.cnki.com.cn/Article/CJFDTotal-DZKQ201906023.htm
    [23]
    王锐, 张泰丽.基于GIS和逻辑回归法的滑坡易发性评价[J].低温建筑技术, 2018, 40(6):95-97. http://cdmd.cnki.com.cn/Article/CDMD-10335-1018244309.htm
    [24]
    方教勇, 杨根云.基于GIS与逻辑回归的清平滑坡易发性评价[J].人民珠江, 2018, 39(4):66-70. http://www.cnki.com.cn/Article/CJFDTotal-RMZJ201804015.htm
    [25]
    Akcay O.Landslide fissure inference assessment by ANFIS and logistic regression using UAS-based photogrammetry[J].ISPRS International Journal of Geo-Information, 2015, 4(4):2131-2158. doi: 10.3390/ijgi4042131
    [26]
    黄发明, 殷坤龙, 蒋水华, 等.基于聚类分析和支持向量机的滑坡易发性评价[J].岩石力学与工程学报, 2018, 37(1):156-167. doi: 10.13722/j.cnki.jrme.2017.0824
    [27]
    Li Zhu, Huang L H, Fan L Y, et al.Landslide susceptibility prediction modeling based on remote sensing and a novel deep learning algorithm of a cascade-parallel recurrent neural network[J].Sensors, 2020, 20(6):1576. doi: 10.3390/s20061576
    [28]
    Huang F M, Luo X Y, Liu W P.Stability analysis of hydrodynamic pressure landslides with different permeability coefficients affected by reservoir water level fluctuations and rainstorms[J].Water, 2017, 9(7):450. doi: 10.3390/w9070450
    [29]
    黄发明, 殷坤龙, 张桂荣, 等.基于相空间重构和小波分析-粒子群向量机的滑坡地下水位预测[J].地球科学:中国地质大学学报, 2015, 40(7):1254-1265. http://d.wanfangdata.com.cn/Periodical/dqkx201507013
    [30]
    Chen W, Wang J L, Xie X S, et al.Spatial prediction of landslide susceptibility using integrated frequency ratio with entropy and support vector machines by different kernel functions[J].Environmental Earth Sciences, 2016, 75(20):1344. doi: 10.1007/s12665-016-6162-8
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML

    Article Metrics

    Article Views(499) PDF Downloads(757) Cited by()
    Proportional views
    Related

    Copyright © 2010Editorial Department of Bulletin of Geological Science and Technology

    Supported by: Beijing Renhe Information Technology Co., Ltd.    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return