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Volume 17, issue 12 | Copyright
Nat. Hazards Earth Syst. Sci., 17, 2181-2198, 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.
Related subject area
Landslides and Debris Flows Hazards
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Cited articles
Abdi, M. J. and Giveki, D.: Automatic detection of erythemato-squamous diseases using PSO-SVM based on association rules, Eng. Appl. Artif. Intel., 26, 603–608,, 2013.
Ahmed, B.: Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh, Landslides, 6, 1077–1095,, 2013.
Ahmadi, M. A., Zendehboudi, S., Lohi, A., Elkamel, A., and Chatzis, I.: Reservoir permeability prediction by neural networks combined with hybrid genetic algorithm and particle swarm optimization, Geophys. Prosp., 61, 582–598,, 2013.
Altınel, B., Can Ganiz, M., and Diri, B.: A corpus-based semantic kernel for text classification by using meaning values of terms, Eng. Appl. Artif. Intel., 43, 54–66,, 2015.
Brockwell, P. J. and Davis, R. A.: Time series: theory and methods, Springer Science & Business Media, 340–341, 2013.
Publications Copernicus
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,...