Susceptibility assessment of small, shallow and clustered landslide

被引:10
|
作者
Liu, Xuemei [1 ,2 ,3 ]
Su, Pengcheng [1 ,2 ]
Li, Yong [1 ,2 ]
Zhang, Jun [1 ,2 ]
Yang, Taiqiang [1 ,2 ]
机构
[1] Chinese Acad Sci, Inst Mt Hazards & Environm, Key Lab Mt Hazards & Surface Proc, Chengdu 610041, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
[3] Sichuan Earthquake Adm, Chengdu 610041, Peoples R China
基金
中国国家自然科学基金;
关键词
Landslide susceptibility assessment; Slope unit; Grid cell; Information value; HAZARD ASSESSMENT; GIS;
D O I
10.1007/s12145-021-00687-2
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Susceptibility assessment of landslides over a large area depends on the basic spatial unit of mapping, usually by using grid cell or slope unit. Both units are used in this study for the assessment of small shallow and clustered landslides in vegetated slopes in Malipo, southwest China. Information value (IV) model was used to generate landslide susceptibility assessment map, while improved information value (IIV) model was used to determine whether the mapping unit is at risk of landslide. Seven factors, including slope angle, slope aspect, elevation, normalized difference vegetation Index (NDVI), Soil Moisture Content (SMC), distance to river and road were used as landslide influence factors. The Area under curve (AUC) values of the slope unit IIV, IV and grid cell were 0.814, 0.802 and 0.702 respectively for success rate. For prediction rate, the AUC values of the slope unit and grid cell were 0.803(IIV), 0.790(IV) and 0.699 respectively. Our results showed slope unit is more suitable than grid cell for assessing susceptibility of Small, Shallow and Cluster Landslide. Improved information value model increases the accuracy of susceptibility assessment model for this characteristic landslide.
引用
收藏
页码:2347 / 2356
页数:10
相关论文
共 50 条
  • [41] Abe Barek landslide and landslide susceptibility assessment in Badakhshan Province, Afghanistan
    Zhang, Jianqiang
    Gurung, Deo Raj
    Liu, Rongkun
    Murthy, Manchiraju Sri Ramachandra
    Su, Fenghuan
    LANDSLIDES, 2015, 12 (03) : 597 - 609
  • [42] Assessment of shallow landslide susceptibility using the transient infiltration flow model and GIS-based probabilistic approach
    Lee, Jung Hyun
    Park, Hyuck Jin
    LANDSLIDES, 2016, 13 (05) : 885 - 903
  • [43] Assessment of the regional landslide susceptibility based on GIS
    Sun, Ze
    Xie, Shijie
    Zhang, Kexin
    Zheng, Xinshen
    Zhu, Yunhai
    GEOINFORMATICS 2007: GEOSPATIAL INFORMATION TECHNOLOGY AND APPLICATIONS, PTS 1 AND 2, 2007, 6754
  • [44] Development of a landslide susceptibility assessment for a rail network
    Martinovic, Karlo
    Gavin, Kenneth
    Reale, Cormac
    ENGINEERING GEOLOGY, 2016, 215 : 1 - 9
  • [45] A NEW PERSPECTIVE FOR REGIONAL LANDSLIDE SUSCEPTIBILITY ASSESSMENT
    Titti G.
    Antelmi M.
    Fusco F.
    Longoni L.
    Borgatti L.
    Italian Journal of Engineering Geology and Environment, 2024, (Special Issue 1): : 275 - 283
  • [46] Assessment of landslide susceptibility and risk factors in China
    Di Wang
    Mengmeng Hao
    Shuai Chen
    Ze Meng
    Dong Jiang
    Fangyu Ding
    Natural Hazards, 2021, 108 : 3045 - 3059
  • [47] Landslide susceptibility assessment using fuzzy logic
    Wang, Zhiwang
    Li, Duanyou
    Cheng, Qiuming
    LANDSLIDES AND ENGINEERED SLOPES: FROM THE PAST TO THE FUTURE, VOLS 1 AND 2, 2008, : 1985 - +
  • [48] Landslide risk assessment based on susceptibility and vulnerability
    Mosaffaie, Jamal
    Jam, Amin Salehpour
    Sarfaraz, Faramarz
    ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY, 2024, 26 (04) : 9285 - 9303
  • [49] Transfer learning improves landslide susceptibility assessment
    Wang, Haojie
    Wang, Lin
    Zhang, Limin
    GONDWANA RESEARCH, 2023, 123 : 238 - 254
  • [50] Landslide susceptibility assessment of Kashmir Himalaya, India
    Sumira Nazir Zaz
    Shakil Ahmad Romshoo
    Arabian Journal of Geosciences, 2022, 15 (6)