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A comparison of five methods in landslide susceptibility assessment: a case study from the 330-kV transmission line in Gansu Region, China

  • Yunfeng Ge
  • Hongzhi Chen
  • Binbin Zhao
  • Huiming Tang
  • Zishan Lin
  • Zhiguo Xie
  • Le Lv
  • Peng Zhong
Original Article

Abstract

Landslides cause damages to land and infrastructure and pose serious threat to human survival. To prepare a landslide susceptibility map of the region in Longnan, City Gansu Province, with a 330-kV transmission line, 10 parameters were selected by correlation analysis and sensitivity analysis from initial 18 and five different methods were used, including analytical hierarchy process (AHP), information value (IV), fractal theory (FT), back propagation neural network (BPNN), support vector machine (SVM). The susceptibility maps were validated through receiver operating characteristic (ROC) and cumulative landslides percentage curves based on 77 existing landslide events. The results indicate that BPNN and SVM model are most accurate, time-saving and easily implemented. All of the five methods accurately predict the spatial distribution of landslides and can be well applied to landslide susceptibility mapping. What needs to be emphasized is that the machine learning methods have the advantages of high efficiency, accurate prediction, time-saving, convenient implementation, which are relatively new and better evaluation models of susceptibility.

Keywords

Landslide susceptibility GIS AHP model IV model FT model BPNN model SVM model 

Notes

Acknowledgements

This research was supported by the National Key R&D Program of China (No. 2017YFC1501303), the National Natural Science Foundation of China (No. 41602316), Science and Technology Project of State Grid Corporation of China (No. GCB17201700121), and Laboratory Research Funds of China University of Geosciences (Wuhan) (No. SJ-201812).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018
corrected ​publication ​[October 2018]

Authors and Affiliations

  • Yunfeng Ge
    • 1
  • Hongzhi Chen
    • 1
  • Binbin Zhao
    • 2
  • Huiming Tang
    • 1
  • Zishan Lin
    • 1
  • Zhiguo Xie
    • 1
  • Le Lv
    • 1
  • Peng Zhong
    • 1
  1. 1.Faculty of EngineeringChina University of GeosciencesWuhanChina
  2. 2.Research Institute of Transmission and Transformation Projects, China Electric Power Research Institute Co., LtdState Grid Corporation of ChinaBeijingChina

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