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Susceptibility Assessment of Landslides in Alpine-Canyon Region Using Multiple GIS-Based Models

  • Engineering Technology
  • Published:
Wuhan University Journal of Natural Sciences

Abstract

This study explores a comparative study of three susceptibility assessment models based on remote sensing (RS) and geographic information system (GIS). The Lenggu region (China) was selected as a case study. At first, a landslide inventory map was compiled using data from existing geology reports, satellite imagery, and coupling with field observations. Subsequently, three models were built to map the landslide susceptibility using analytical hierarchy process (AHP), fuzzy logic (FL) and certainty factors (CF). The resulting models were validated and compared using areas under the curve (AUC). The AUC plot estimation results indicated that the three models are promising methods for landslide susceptibility mapping. Among the three methods, CF model has highest prediction accuracy than the other two models. Similarly, the outcome of this study reveals that streams, faults, slope and elevation are the main conditioning factors of landslides. Especially, the erosion of streams plays a key role of the landslide occurrence. These landslide susceptibility maps, to some extent, reflect spatial distribution characteristics of landslides in alpine- canyon region of southwest China, and can be used for land planning and hazard risk assessment.

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References

  1. Wu W, Sidle R C. A distributed slope stability model for steep forested basins [J]. Water Resources Research, 1995, 31(8): 2097–2110.

    Article  Google Scholar 

  2. Chung C J F, Fabbri A G, Van Westen C J. Geographical Information Systems in Assessing Natural Hazards [M]. Heidelberg: Springer-Verlag, 1995.

    Google Scholar 

  3. Guzzetti F. Landslide Hazard and Risk Assessment [D]. Bonn: University of Bonn, 2005.

    Google Scholar 

  4. Lan H X, Zhou C H, Wang L J, et al. Landslide hazard spatial analysis and prediction using GIS in the Xiaojiang Watershed, Yunnan, China [J]. Engineering Geology, 2004, 76(1-2): 109–128.

    Article  Google Scholar 

  5. Wang W D, Xie C M, Du X G. Landslides susceptibility mapping based on geographical information system, Guizhou, south-west China [J]. Environmental Geology, 2009, 58(1): 33–43.

    Article  Google Scholar 

  6. Guo C, Montgomery D R, Zhang Y, et al. Quantitative assessment of landslide susceptibility along the Xianshuihe fault zone, Tibetan Plateau, China [J]. Geomorphology, 2015, 248: 93–110.

    Article  Google Scholar 

  7. Cao C, Wang Q, Chen J, et al. Landslide susceptibility mapping in vertical distribution law of precipitation area: Case of the Xulong Hydropower Station Reservoir, Southwestern China [J]. Water, 2016, 8(7): 270–291.

    Article  Google Scholar 

  8. Thanh L N, Smedt F D. Application of an analytical hierarchical process approach for landslide susceptibility mapping in A Luoi district, Thua Thien Hue Province, Vietnam [J]. Environmental Earth Sciences, 2012, 66(7): 1739–1752.

    Article  Google Scholar 

  9. Jebur M N, Pradhan B, Tehrany M S. Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale [J]. Remote Sensing of Environment, 2014, 152: 150–165.

    Article  Google Scholar 

  10. Kayastha P, Dhital M R, De Smedt F. Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: A case study from the Tinau watershed, west Nepal [J]. Computers & Geosciences, 2013, 52: 398–408.

    Article  Google Scholar 

  11. Bui D T, Tuan T A, Klempe H, et al. Spatial prediction models for shallow landslide hazards: A comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree [J]. Landslides, 2016, 13(2): 361–378.

    Article  Google Scholar 

  12. Jia N, Mitani Y, Xie M, et al. Shallow landslide hazard assessment using a three-dimensional deterministic model in a mountainous area [J]. Computers and Geotechnics, 2012, 45: 1–10.

    Article  Google Scholar 

  13. Hasekioğulları G D, Ercanoglu M. A new approach to use AHP in landslide susceptibility mapping: A case study at Yenice (Karabuk, NW Turkey) [J]. Natural Hazards, 2012, 63(2): 1157–1179.

    Article  Google Scholar 

  14. Yilmaz I. Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: Conditional probability, logistic regression, artificial neural networks, and support vector machine [J]. Environmental Earth Sciences, 2010, 61(4): 821–836.

    Article  CAS  Google Scholar 

  15. Xu C, Xu X, Dai F, et al. Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China [J]. Computers & Geosciences, 2012, 46: 317–329.

    Article  CAS  Google Scholar 

  16. Devkota K C, Regmi A D, Pourghasemi H R, et al. Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling-Narayanghat road section in Nepal Himalaya [J]. Natural Hazards, 2013, 65(1): 135–165.

    Article  Google Scholar 

  17. Pradhan B, Buchroithner M F. Comparison and validation of landslide susceptibility maps using an artificial neural network model for three test areas in Malaysia [J]. Environmental & Engineering Geoscience, 2010, 16(2): 107–126.

    Article  Google Scholar 

  18. Park S, Choi C, Kim B, et al. Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea [J]. Environmental Earth Sciences, 2013, 68(5): 1443–1464.

    Article  Google Scholar 

  19. Pradhan B. 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.

    Article  Google Scholar 

  20. Atkinson P M, Massari R. Generalised linear modelling of susceptibility to landsliding in the central Apennines, Italy [J]. Computers & Geosciences, 1998, 24(4): 373–385.

    Article  Google Scholar 

  21. Reger J P. Discriminant analysis as a possible tool in landslide investigations [J]. Earth Surface Processes and Landforms, 1979, 4(3): 267–273.

    Article  Google Scholar 

  22. Pourghasemi H R, Pradhan B, Gokceoglu C. Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran[J]. Natural Hazards, 2012, 63(2): 965–996.

    Article  Google Scholar 

  23. Ermini L, Catani F, Casagli N. Artificial neural networks applied to landslide susceptibility assessment [J]. Geomorphology, 2005, 66(1): 327–343.

    Article  Google Scholar 

  24. Yao X, Tham L G, Dai F C. Landslide susceptibility mapping based on support vector machine: A case study on natural slopes of Hong Kong, China [J]. Geomorphology, 2008, 101(4): 572–582.

    Article  Google Scholar 

  25. Glade T. Linking debris-flow hazard assessments with geomorphology [J]. Geomorphology, 2005, 66(1): 189–213.

    Article  Google Scholar 

  26. Qi S, Wu F, Yan F, et al. Mechanism of deep cracks in the left bank slope of Jinping First Stage Hydropower Station [J]. Engineering Geology, 2004, 73(1): 129–144.

    Article  Google Scholar 

  27. Liu H Q, Hu R, Tan R, et al. Huashiban loose deposit landslide, Tiger-Leaping-Gorge, China: Analysis and prediction [J]. Bulletin of Engineering Geology and the Environment, 2007, 66(2): 197–202.

    Article  CAS  Google Scholar 

  28. Wang Z L. Research on Failure Mode and Stability Analysis of the Slope at Lenggu Hydropower Station on Yalong River [D]. Chengdu: Chengdu University of Technology, 2016 (Ch).

    Google Scholar 

  29. Saaty T L. Axiomatic foundation of the analytic hierarchy process [J]. Management Science, 1986, 32(7): 841–855.

    Article  Google Scholar 

  30. Shortliffe E H, Buchanan B G. A model of inexact reasoning in medicine [J]. Mathematical Biosciences, 1975, 23(3-4): 351–379.

    Article  Google Scholar 

  31. Binaghi E, Luzi L, Madella P, et al. Slope instability zonation: A comparison between certainty factor and fuzzy Dempster–Shafer approaches [J]. Natural Hazards, 1998, 17(1): 77–97.

    Article  Google Scholar 

  32. Pourghasemi H R, Pradhan B, Gokceoglu C, et al. Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran [J]. Arabian Journal of Geosciences, 2013, 6(7): 2351–2365.

    Article  Google Scholar 

  33. Zadeh L A. Fuzzy sets [J]. Information and Control, 1965, 8(3): 338–353.

    Article  Google Scholar 

  34. Bonham-Carter G F. Geographic Information Systems for Geoscientists: Modeling with GIS [M]. Oxford: Pergamon Press, 1994.

    Google Scholar 

  35. Wieczorek G F. Preparing a detailed landslide-inventory map for hazard evaluation and reduction [J]. Bull Assoc Eng Geol, 1984, 21(3): 337–342.

    Google Scholar 

  36. Soeters R, van Westen C J. Slope instability recognition, analysis, and zonation [J]. Transportation Research Board Special Report, 1996, (247): 129–177.

    Google Scholar 

  37. Van Westen C J, Castellanos E, Kuriakose S L. Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview[J]. Engineering Geology, 2008, 102(3): 112–131.

    Article  Google Scholar 

  38. Kritikos T, Davies T. Assessment of rainfall-generated shallow landslide/debris-flow susceptibility and runout using a GIS-based approach: Application to western Southern Alps of New Zealand [J]. Landslides, 2015, 12(6): 1051–1075.

    Article  Google Scholar 

  39. Ercanoglu M, Gokceoglu C, Van Asch T W J. Landslide susceptibility zoning north of Yenice (NW Turkey) by multivariate statistical techniques [J]. Natural Hazards, 2004, 32(1): 1–23.

    Article  Google Scholar 

  40. Dai F C, Lee C F. Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong [J]. Geomorphology, 2002, 42(3): 213–228.

    Article  Google Scholar 

  41. Donati L, Turrini M C. An objective method to rank the importance of the factors predisposing to landslides with the GIS methodology: application to an area of the Apennines (Valnerina; Perugia, Italy) [J]. Engineering Geology, 2002, 63(3): 277–289.

    Article  Google Scholar 

  42. Yalcin A, Bulut F. Landslide susceptibility mapping using GIS and digital photogrammetric techniques: A case study from Ardesen (NE-Turkey) [J]. Natural Hazards, 2007, 41(1): 201–226.

    Article  Google Scholar 

  43. Korup O. Geomorphic implications of fault zone weakening: Slope instability along the Alpine Fault, South Westland to Fiordland[J]. New Zealand Journal of Geology and Geophysics, 2004, 47(2): 257–267.

    Article  Google Scholar 

  44. Warr L N, Cox S. Clay mineral transformations and weakening mechanisms along the Alpine Fault, New Zealand [J]. Geological Society, London, Special Publications, 2001, 186(1): 85–101.

    Article  Google Scholar 

  45. Larsen I J, Montgomery D R. Landslide erosion coupled to tectonics and river incision [J]. Nature Geoscience, 2012, 5(7): 468–473.

    Article  CAS  Google Scholar 

  46. Nefeslioglu H A, Gokceoglu C, Sonmez H. An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps [J]. Engineering Geology, 2008, 97(3): 171–191.

    Article  Google Scholar 

  47. Hall F G, Townshend J R, Engman E T. Status of remote sensing algorithms for estimation of land surface state parameters [J]. Remote Sensing of Environment, 1995, 51(1): 138–156.

    Article  Google Scholar 

  48. Chen W, Li W P, Chai H C, et al. GIS-based landslide susceptibility mapping using analytical hierarchy process (AHP) and certainty factor (CF) models for the Baozhong region of Baoji City, China [J]. Environmental Earth Sciences, 2016, 75(1): 63.

    Article  Google Scholar 

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Authors and Affiliations

  1. College of Engineering and Technology, Southwest University, Chongqing, 400715, China

    Man Hu & Pengyu Liu

  2. Consultative Centre for Geo-Hazard Emergency, Ministry of Land and Resources of China, Beijing, 100081, China

    Qiuqiang Liu

Authors
  1. Man Hu
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  2. Qiuqiang Liu
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  3. Pengyu Liu
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Correspondence to Man Hu.

Additional information

Foundation item: Supported by the National Natural Science Foundation of China (41602354), the Chongqing Research Program of Basic Research and Frontier Technology (2017jcyjAX0300), and the Fundamental Research Funds for the Central Universities (XDJK2016B027)

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Hu, M., Liu, Q. & Liu, P. Susceptibility Assessment of Landslides in Alpine-Canyon Region Using Multiple GIS-Based Models. Wuhan Univ. J. Nat. Sci. 24, 257–270 (2019). https://doi.org/10.1007/s11859-019-1395-5

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  • DOI: https://doi.org/10.1007/s11859-019-1395-5

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