Journal metrics

Journal metrics

  • IF value: 2.281 IF 2.281
  • IF 5-year value: 2.693 IF 5-year 2.693
  • CiteScore value: 2.43 CiteScore 2.43
  • SNIP value: 1.193 SNIP 1.193
  • SJR value: 0.965 SJR 0.965
  • IPP value: 2.31 IPP 2.31
  • h5-index value: 40 h5-index 40
  • Scimago H index value: 73 Scimago H index 73
Volume 17, issue 12 | Copyright
Nat. Hazards Earth Syst. Sci., 17, 2181-2198, 2017
https://doi.org/10.5194/nhess-17-2181-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 07 Dec 2017

Research article | 07 Dec 2017

Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China

Tao Wen et al.
Data sets

Shuping landslide-Rainfall and Reservoir water level T. Wen, H. Tang, Y. Wang, C. Lin, and C.Xiong https://doi.org/10.13140/RG.2.2.27329.04964

Publications Copernicus
Download
Short summary
Landslide displacement prediction is one of the focuses of landslide research. In this paper, time series analysis was used to decompose the cumulative displacement of landslide into a trend component and a periodic component. Then LSSVM model and GA were used to predict landslide displacement. The results show that the GA-LSSVM model can be effectively used to predict landslide displacement and reflect the corresponding relationships between the major influencing factors and the displacement.
Landslide displacement prediction is one of the focuses of landslide research. In this paper,...
Citation
Share