Abstract
Accurately predicting the surface displacement of the landslide is important and necessary. However, most of the existing research has ignored the frequency component of inducing factors and how it affects the landslide deformation. Therefore, a hybrid displacement prediction model based on time series theory and various intelligent algorithms was proposed in this paper to study the effect of frequency components. Firstly, the monitoring displacement of landslide from the Three Gorges Reservoir area (TGRA) was decomposed into the trend and periodic components by complete ensemble empirical mode decomposition (CEEMD). The trend component can be predicted by the least square method. Then, time series of inducing factors like rainfall and reservoir level was reconstructed into high frequency components and low frequency components with CEEMD and t-test, respectively. The dominant factors were selected by the method of dynamic time warping (DTW) from the frequency components and other common factors (e.g., current monthly rainfall). Finally, the ant colony optimization-based support vector machine regression (ACO-SVR) is utilized for prediction purposes in the TGRA. The results demonstrate that after considering the frequency components of landslide-induced factors, the accuracy of the displacement prediction model based on ACO-SVR is better than that of other models based on SVR and GA-SVR.
Highlights
As one of the most widely distributed and frequently occurring geological disasters in nature, landslides pose a greater threat to the environment, natural resources, hydraulic engineering, etc. [1].A landslide can be regarded as a nonlinear dynamic system, which is affected by external factors such as rainfall, reservoir water level, and groundwater [2]
By considering the frequency components of inducing factors, optimized parameter selection methods and application of ant colony optimization (ACO) optimization algorithm, this study presents a hybrid prediction method consisting of the complete ensemble empirical mode decomposition (CEEMD), the Dynamic time warping (DTW), and the ant colony optimization-based support vector machine regression (ACO-support vector regression (SVR)) model to improve the accuracy of the SVR-based prediction model
The ACO-based SVR model has a better generalization performance and can increase the predictive accuracy of landslide displacement by determining the optimal parameters of SVR automatically
Summary
A landslide can be regarded as a nonlinear dynamic system, which is affected by external factors such as rainfall, reservoir water level, and groundwater [2]. It is reported that plenty of old landslides were reactivated by the periodic reservoir level fluctuation and rainfall since the first impound of the Three Gorges Reservoir (TGR) in 2003 [3,4]. The surface displacement of the landslide is one of the important information generated during the landslide deformation process and is of great significance to predict the evolution law and development trend of landslides according to the analysis of it [5,6,7]. Predicting the surface displacement of the landslide is important and necessary for mastering the evolution stages of landslide and realizing the accurate early warning [8,9].
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