Journal cover Journal topic
Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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 Wen1, Huiming Tang1,2, Yankun Wang1,2, 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 Geohazards of Ministry of Education, China University of Geosciences, Wuhan 430074, Hubei, 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 episodic movement 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) of 62.4146 mm, mean absolute error (MAE) of 53.0048 mm and mean absolute percentage error (MAPE) of 1.492 % at monitoring station ZG85, while these three values are 87.7215 mm, 74.0601 mm and 1.703 % at ZG86 and 49.0485 mm, 48.5392 mm and 3.131 % at ZG87. 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., 17, 2181-2198, https://doi.org/10.5194/nhess-17-2181-2017, 2017.
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,...
Share