Landslide Susceptibility Mapping and Interpretation in the Upper Minjiang River Basin

被引:1
|
作者
Wang, Xin [1 ]
Bai, Shibiao [1 ,2 ]
机构
[1] Nanjing Normal Univ, Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Sch Marine Sci & Engn, Nanjing 210023, Peoples R China
[2] Chinese Acad Sci, Inst Mt Hazards & Environm, China Pakistan Joint Res Ctr Earth Sci, Chengdu 610041, Peoples R China
基金
中国国家自然科学基金;
关键词
background factors; landslide susceptibility; WOE-RF; rainfall landslide; seismic landslide; space comparison; MULTI-HAZARD ASSESSMENT; RISK; GIS; REGRESSION; EARTHQUAKE;
D O I
10.3390/rs15204947
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
To enable the accurate assessment of landslide susceptibility in the upper reaches of the Minjiang River Basin, this research intends to spatially compare landslide susceptibility maps obtained from unclassified landslides directly and the spatial superposition of different types of landslide susceptibility map, and explore interpretability using cartographic principles of the two methods of map-making. This research using the catalogs of rainfall and seismic landslides selected nine background factors those affect the occurrence of landslides through correlation analysis finally, including lithology, NDVI, elevation, slope, aspect, profile curve, curvature, land use, and distance to faults, to assess rainfall and seismic landslide susceptibility, respectively, by using a WOE-RF coupling model. Then, an evaluation of landslide susceptibility was conducted by merging rainfall and seismic landslides into a dataset that does not distinguish types of landslides; a comparison was also made between the landslide susceptibility maps obtained through the superposition of rainfall and seismic landslide susceptibility maps and unclassified landslides. Finally, confusion matrix and ROC curve were used to verify the accuracy of the model. It was found that the accuracy of the training set, testing set, and the entire data set based on the WOE-RF model for predicting rainfall landslides were 0.9248, 0.8317, and 0.9347, and the AUC area were 1, 0.949, and 0.955; the accuracy of the training set, testing set, and the entire data set for seismic landslides prediction were 0.9498, 0.9067, and 0.8329, and the AUC area were 1, 0.981, and 0.921; the accuracy of the training set, testing set, and the entire data set for unclassified landslides prediction were 0.9446, 0.9080, and 0.8352, and the AUC area were 0.9997, 0.9822, and 0.9207. Both of the confusion matrix and the ROC curve indicated that the accuracy of the coupling model is high. The southeast of the line from Mount Xuebaoding to Lixian County is a high landslide prone area, and through the maps, it was found that the extremely high susceptibility area of seismic landslides is located at a higher elevation than rainfall landslides by extracting the extremely high susceptibility zones of both. It was also found that the results of the two methods of evaluating landslide susceptibility were significantly different. As for a same background factor, the distribution of the areas occupied by the same landslide occurrence class was not the same according to the two methods, which indicates the necessity of conducting relevant research on distinguishing landslide types.
引用
收藏
页数:28
相关论文
共 50 条
  • [2] A regional level preliminary landslide susceptibility study of the upper Indus river basin
    Ahmed, M. Farooq
    Rogers, J. David
    Ismail, Elamin H.
    [J]. EUROPEAN JOURNAL OF REMOTE SENSING, 2014, 47 : 343 - 373
  • [3] Landslide susceptibility mapping of the Sera River Basin using logistic regression model
    Raja, Nussaibah B.
    Cicek, Ihsan
    Turkoglu, Necla
    Aydin, Olgu
    Kawasaki, Akiyuki
    [J]. NATURAL HAZARDS, 2017, 85 (03) : 1323 - 1346
  • [4] Landslide susceptibility mapping of the Sera River Basin using logistic regression model
    Nussaïbah B. Raja
    Ihsan Çiçek
    Necla Türkoğlu
    Olgu Aydin
    Akiyuki Kawasaki
    [J]. Natural Hazards, 2017, 85 : 1323 - 1346
  • [5] Effect of scale and mapping unit on landslide susceptibility mapping of Mandakini River Basin, Uttarakhand, India
    Sharad Kumar Gupta
    Dericks P. Shukla
    [J]. Environmental Earth Sciences, 2022, 81
  • [6] Effect of scale and mapping unit on landslide susceptibility mapping of Mandakini River Basin, Uttarakhand, India
    Gupta, Sharad Kumar
    Shukla, Dericks P.
    [J]. ENVIRONMENTAL EARTH SCIENCES, 2022, 81 (14)
  • [7] GIS-Based Spatial Analysis and Modeling for Landslide Hazard Assessment:A Case Study in Upper Minjiang River Basin
    FENG Wenlan 1
    2. Graduate University of Chinese Academy of Sciences
    3. Department of Envioronmental Engineering
    [J]. Wuhan University Journal of Natural Sciences, 2006, (04) : 847 - 852
  • [8] MULTI-CRITERIA ANALYSIS FOR LANDSLIDE SUSCEPTIBILITY MAPPING IN PAQUEQUER RIVER BASIN, RJ
    Meirelles, Evelyn de Oliveira
    Dourado, Francisco
    da Costa, Vivian Castilho
    [J]. GEO UERJ, 2018, (33):
  • [9] Correction to: Landslide susceptibility mapping of the Sera River Basin using logistic regression model
    Nussaïbah B. Raja
    Ihsan Çiçek
    Necla Türkoğlu
    Olgu Aydin
    Akiyuki Kawasaki
    [J]. Natural Hazards, 2018, 91 : 1423 - 1423
  • [10] Application of the PRMS model in the Zhenjiangguan watershed in the Upper Minjiang River basin
    Fang, Longzhang
    Liu, Chao
    Qin, Guanghua
    Zhang, Bin
    Liu, Tiegang
    [J]. REMOTE SENSING AND GIS FOR HYDROLOGY AND WATER RESOURCES, 2015, 368 : 209 - 214