Journal cover Journal topic
Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
doi:10.5194/nhess-2017-87
© Author(s) 2017. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
29 Mar 2017
Review status
This discussion paper is under review for the journal Natural Hazards and Earth System Sciences (NHESS).
Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China
Tao Wen1, Huiming Tang1,2, Yankun Wang2, Chengyuan Lin1, and Chengren Xiong2 1Faculty of Engineering, China University of Geosciences, Wuhan 430074, Hubei, People's Republic of China
2Three Gorges Research Center for Geo-hazards of Ministry of Education, China University of Geosciences, Wuhan, Hubei 430074, People's Republic of China
Abstract. Predicting landslide displacement is challenging, but accurate predictions can prevent casualties and economic losses. Many factors can affect the deformation of a landslide, including the geological conditions, rainfall, and reservoir water level. Time series analysis was used to decompose the cumulative displacement of landslide into a trend component and a periodic component. Then the least squares support vector machine (LSSVM) model and genetic algorithm (GA) were used to predict landslide displacement, and we selected a representative landslide with step-like deformation as a case study. The trend component displacement, which is associated with the geological conditions, was predicted using a polynomial function, and the periodic component displacement which is associated with external environmental factors, was predicted using the GA-LSSVM model. Furthermore, based on a comparison of the results of the GA-LSSVM model and those of other models, the GA-LSSVM model was superior to other models in predicting landslide displacement, with the smallest root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results of the case study suggest that the model can provide good consistency between measured displacement and predicted displacement, and periodic displacement exhibited good agreement with trends in the major influencing factors.

Citation: Wen, T., Tang, H., Wang, Y., Lin, C., and Xiong, C.: Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China, Nat. Hazards Earth Syst. Sci. Discuss., doi:10.5194/nhess-2017-87, in review, 2017.
Tao Wen et al.
Tao Wen et al.
Tao Wen et al.

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Short summary
Landslide displacement prediction is one of the focuses of landslide research. In this paper, we use the deformation of a step-like landslide as an example. According to time series analysis, the trend displacement and periodic displacement are predicted using a polynomial function and the GA-LSSVM model. In conclusion, the GA-LSSVM model with time series analysis can be effectively used to predict landslide displacement.
Landslide displacement prediction is one of the focuses of landslide research. In this paper, we...
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