Integration of Analytical Hierarchy Process and Landslide Susceptibility Index Based Landslide Susceptibility Assessment of the Pearl River Delta Area, China

被引:19
|
作者
Zhang, Haoran [1 ]
Zhang, Guifang [1 ,2 ,3 ]
Jia, Qiwen [1 ]
机构
[1] Sun Yat Sen Univ, Sch Earth Sci & Engn, Guangzhou 510275, Guangdong, Peoples R China
[2] Sun Yat Sen Univ, Sch Earth Sci & Engn, Guangzhou Prov Key Lab Geodynam & Geohazards, Guangzhou 510075, Peoples R China
[3] Southern Marine Sci & Engn Lab, Zhuhai 519000, Peoples R China
基金
中国国家自然科学基金;
关键词
Terrain factors; Geology; Rivers; Roads; Earth; Remote sensing; Indexes; Analytical hierarchy process; GIS; landslide; landslide susceptibility index; Pearl River Delta; 3 GORGES AREA; DATA FUSION; LOGISTIC-REGRESSION; PROCESS AHP; GIS; HAZARD; BIVARIATE; MODEL; PROBABILITY; STATISTICS;
D O I
10.1109/JSTARS.2019.2938554
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Landslide is one of the most disastrous geological hazards in the Pearl River Delta area. In the article, landslide susceptibility index (LSI) and analytical hierarchy process (AHP) have been adopted to assess landslide susceptibility (LS) of the Pearl River Delta area. A total of 294 historical landslide sites were extracted from China Geological Survey Result CGS2015-008 and remote sensing images as landslide inventory. In each experiment, 198 landslide points were randomly selected as training samples and the remaining 96 as verification samples. Nine influencing factors, i.e., standard deviation (STD) of elevation, terrain roughness, curvature, lithology, fault, land use, water density, road density, and aspect, were obtained through ASTER GDEM, China Geological Survey Result CGS2015-008, Geographical Information Monitoring Cloud Platform, Open Street Map, and Google Earth. LSI was used to represent the contribution of each category within the influencing factors to the occurrence of landslides (LSIi), and AHP was used to calculate the weights between different influencing factors (Wi). Finally, the summation of the product of LSIi by Wi was used to represent the LS value for each pixel. Then, the study area was grouped into five susceptibility classes based on the landslide susceptibility. It is indicated that terrain factors have the greatest impact on landslides, followed by engineering geological lithology and land use types. The high and very high susceptibility classes were mainly distributed in the area of: 1) STD of elevation of 0.8-2.5, roughness of 1.004-1.030, and curvature 2.7-7.2; 2) sand-shale stone and coal, bedded clastic rock, and bedded epimetamorphic rock; and 3) woodland and the urban and rural land. The receiver operating characteristic curves of many experimental results prove that the model has fine prediction performance and good stability. This article could provide a basis for landslide prevention and land development in the Pearl River Delta area.
引用
收藏
页码:4239 / 4251
页数:13
相关论文
共 50 条
  • [1] Integration of the Statistical Index Method and the Analytic Hierarchy Process technique for the assessment of landslide susceptibility in Huizhou, China
    Zhang, Guifang
    Cai, Yixi
    Zheng, Zhuo
    Zhen, Junwei
    Liu, Yongli
    Huang, Kangyou
    CATENA, 2016, 142 : 233 - 244
  • [2] Landslide susceptibility mapping using the analytical hierarchy process and GIS
    Semlali, I
    Ouadif, L.
    Bahi, L.
    CURRENT SCIENCE, 2019, 116 (05): : 773 - 779
  • [3] Landslide susceptibility mapping based on landslide history and analytic hierarchy process (AHP)
    Dimitrios Myronidis
    Charalambos Papageorgiou
    Stavros Theophanous
    Natural Hazards, 2016, 81 : 245 - 263
  • [4] Landslide susceptibility mapping based on landslide history and analytic hierarchy process (AHP)
    Myronidis, Dimitrios
    Papageorgiou, Charalambos
    Theophanous, Stavros
    NATURAL HAZARDS, 2016, 81 (01) : 245 - 263
  • [5] Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances
    H. R. Pourghasemi
    H. R. Moradi
    S. M. Fatemi Aghda
    Natural Hazards, 2013, 69 : 749 - 779
  • [6] Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances
    Pourghasemi, H. R.
    Moradi, H. R.
    Aghda, S. M. Fatemi
    NATURAL HAZARDS, 2013, 69 (01) : 749 - 779
  • [7] GIS-based landslide susceptibility assessment using analytical hierarchy process in wenchuan earthquake region
    Xu, Chong
    Dai, Fuchu
    Yao, Xin
    Chen, Jian
    Tu, Xinbin
    Sun, Yu
    Wang, Zhiyi
    Yanshilixue Yu Gongcheng Xuebao/Chinese Journal of Rock Mechanics and Engineering, 2009, 28 (SUPPL. 2): : 3978 - 3985
  • [8] GIS-based Landslide Susceptibility Mapping Using Analytical Hierarchy Process in Wenchuan
    Gou, Changjiang
    Liu, Rui
    Feng, Ce
    2013 21ST INTERNATIONAL CONFERENCE ON GEOINFORMATICS (GEOINFORMATICS), 2013,
  • [9] A Comparative Analysis of Binary Logistic Regression and Analytical Hierarchy Process for Landslide Susceptibility Assessment in the Dobrovat River Basin, Romania
    Patriche, Cristian V.
    Pirnau, Radu
    Grozavu, Adrian
    Rosca, Bogdan
    PEDOSPHERE, 2016, 26 (03) : 335 - 350
  • [10] Landslide susceptibility zonation using the analytical hierarchy process (AHP) in the Great Xi'an Region, China
    Liu, Xiaokang
    Shao, Shuai
    Shao, Shengjun
    SCIENTIFIC REPORTS, 2024, 14 (01):