Abstract
This paper proposes a WD-GA-LSSVM model for predicting the displacement of a deepseated landslide triggered by seasonal rainfall, in which wavelet denoising (WD) is used in displacement time series of landslide to eliminate the GPS observation noise in the original data, and genetic algorithm (GA) is applied to obtain optimal parameters of least squares support vector machines (LSSVM) model. The model is first trained and then evaluated by using data from a gentle dipping (~2°-5°) landslide triggered by seasonal rainfall in the southwest of China. Performance comparisons of WD-GA-LSSVM model with Back Propagation Neural Network (BPNN) model and LSSVM are presented, individually. The results indicate that the adoption of WD-GA-LSSVM model significantly improves the robustness and accuracy of the displacement prediction and it provides a powerful technique for predicting the displacement of a rainfall-triggered landslide.
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Acknowledgments
This study was supported by the Chinese National Natural Science Foundation (Grant No. 41502293), the National Basic Research Program (973 Program) (Grant No. 2014CB744703), the Funds for Creative Research Groups of China (Grant No. 41521002). We would like to extend special thanks to Dr. T.W.J. van Asch, for all his valuable suggestions in greatly improving the quality of this paper.
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Zhu, X., Ma, Sq., Xu, Q. et al. A WD-GA-LSSVM model for rainfall-triggered landslide displacement prediction. J. Mt. Sci. 15, 156–166 (2018). https://doi.org/10.1007/s11629-016-4245-3
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DOI: https://doi.org/10.1007/s11629-016-4245-3