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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
Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China
Tao Wen et al.
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Interactive discussionStatus: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version      Supplement - Supplement
 
SC1: 'Review for the manuscript', Yourong Liu, 22 May 2017 Printer-friendly Version 
AC2: 'Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China', HUIMING TNAG, 10 Jul 2017 Printer-friendly Version Supplement 
SC2: 'Short comment', Yourong Liu, 19 Jul 2017 Printer-friendly Version 
 
RC1: 'Review: Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China', Anonymous Referee #1, 01 Jun 2017 Printer-friendly Version Supplement 
AC1: 'Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China', HUIMING TNAG, 10 Jul 2017 Printer-friendly Version Supplement 
RC2: 'Review of : Landslide displacement prediction using GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China', Anonymous Referee #1, 21 Jul 2017 Printer-friendly Version 
 
RC3: 'Interactive comment on: Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China', Anonymous Referee #2, 28 Sep 2017 Printer-friendly Version Supplement 
AC3: 'Response to Reviewer’s Comments', HUIMING TNAG, 13 Oct 2017 Printer-friendly Version Supplement 
Peer review completion
AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (23 Oct 2017) by Thomas Glade  
AR by HUIMING TNAG on behalf of the Authors (28 Oct 2017)  Author's response  Manuscript
ED: Publish as is (02 Nov 2017) by Thomas Glade  
CC BY 4.0
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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,...
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