Application of the chaotic sequence WA-ELM coupling model in landslide displacement prediction

To address the chaotic characteristics of landslide displacement sequence and to overcome the deficiency of the traditional time series forecasting models, a WA-ELM prediction model of landslide displacement is proposed based on chaotic time series. The chaotic characteristics of the landslide displacement sequence is analyzed, in which the wavelet analysis(WA) is employed to decompose the displacement sequence into characteristic components with different frequencies. The characteristic components are reconstructed in the phase space and predicted using the extreme learning machine (ELM). Finally, the characteristic components are superposed to obtain the prediction values. A comparative study of Bazimen landslide in Three Gorges Reservoir area is made using WA-SVM and ELM models, respectively. The results show that the predictions of the WA-ELM model based on chaotic time series has higher accuracy and better versatility and stability.