Journal
JOURNAL OF MOUNTAIN SCIENCE
Volume 15, Issue 1, Pages 156-166Publisher
SCIENCE PRESS
DOI: 10.1007/s11629-016-4245-3
Keywords
WD-GA-LSSVM; Landslide; Rainfall; Displacement prediction; Wavelet denoising
Categories
Funding
- Chinese National Natural Science Foundation [41502293]
- National Basic Research Program (973 Program) [2014CB744703]
- Funds for Creative Research Groups of China [41521002]
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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 (similar to 2A degrees-5A degrees) 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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