Articles | Volume 17, issue 12
https://doi.org/10.5194/nhess-17-2181-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/nhess-17-2181-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China
Tao Wen
Faculty of Engineering, China University of Geosciences, Wuhan 430074, Hubei, People's Republic of China
Huiming Tang
CORRESPONDING AUTHOR
Faculty of Engineering, China University of Geosciences, Wuhan 430074, Hubei, People's Republic of China
Three Gorges Research Center for Geohazards of Ministry of Education, China University of Geosciences, Wuhan 430074, Hubei, People's Republic of China
Yankun Wang
Faculty of Engineering, China University of Geosciences, Wuhan 430074, Hubei, People's Republic of China
Three Gorges Research Center for Geohazards of Ministry of Education, China University of Geosciences, Wuhan 430074, Hubei, People's Republic of China
Chengyuan Lin
Faculty of Engineering, China University of Geosciences, Wuhan 430074, Hubei, People's Republic of China
Chengren Xiong
Three Gorges Research Center for Geohazards of Ministry of Education, China University of Geosciences, Wuhan 430074, Hubei, People's Republic of China
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- Point and Interval Predictions for Tanjiahe Landslide Displacement in the Three Gorges Reservoir Area, China Y. Wang et al. 10.1155/2019/8985325
- A Novel Decomposition-Ensemble Learning Model Based on Ensemble Empirical Mode Decomposition and Recurrent Neural Network for Landslide Displacement Prediction X. Niu et al. 10.3390/app11104684
- Machine-learning-based nowcasting of the Vögelsberg deep-seated landslide: why predicting slow deformation is not so easy A. van Natijne et al. 10.5194/nhess-23-3723-2023
- Prediction model oriented for landslide displacement with step-like curve by applying ensemble empirical mode decomposition and the PSO-ELM method H. Du et al. 10.1016/j.jclepro.2020.122248
- Prediction of China’s Energy Consumption Based on Robust Principal Component Analysis and PSO-LSSVM Optimized by the Tabu Search Algorithm L. Zhang et al. 10.3390/en12010196
- Prediction on landslide displacement using a new combination model: a case study of Qinglong landslide in China W. Wang et al. 10.1007/s11069-019-03595-3
- Landslide displacement forecasting using deep learning and monitoring data across selected sites L. Nava et al. 10.1007/s10346-023-02104-9
- Displacement characteristics and prediction of Baishuihe landslide in the Three Gorges Reservoir D. Li et al. 10.1007/s11629-019-5470-3
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- Relationship between river bank stability and hydrological processes using in situ measurement data G. Mentes 10.1556/24.62.2019.01
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- Using Complementary Ensemble Empirical Mode Decomposition and Gated Recurrent Unit to Predict Landslide Displacements in Dam Reservoir B. Yang et al. 10.3390/s22041320
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- Using a kernel extreme learning machine with grey wolf optimization to predict the displacement of step-like landslide K. Liao et al. 10.1007/s10064-019-01598-9
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- Hydrodynamic landslide displacement prediction using combined extreme learning machine and random search support vector regression model R. Wang et al. 10.1080/19648189.2020.1754298
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Latest update: 28 Mar 2024
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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