PREDICTIVE MODELING OF LANDSLIDE DISPLACEMENT BY WAVELET ANALYSIS AND MULTIPLE EXTREME LEARNING MACHINES

The curve landslide cumulative displacement is usually nonlinear. Hence, it is challenging to build predictive models with less error. In this paper, we propose a new methodology of embedding wavelet analysis with basic extreme learning machine(ELM) and online sequential extreme learning machine(OS-ELM) to predict the cumulative displacement. Firstly, by wavelet transformation, the cumulative function of displacement is discretized into periodic displacement and trend displacement. Secondly, basic ELM and OS-ELM are selected to predict the periodic displacement and trend displacement. Lastly, the cumulative displacement function is computed by ensembling the predicted periodic and trend displacement values. For basic ELM,a sigmoid function is selected as the kernel function and a single hidden layer with 33 nodes performs best. For OS-ELM,the prediction error reaches its minimum with 100 hidden nodes when the RBF function is selected as the kernel function. RMSE for ELM is 0.1423 and for OS-ELM is 0.1315. This methodology with high predictive accuracy performs better in comparison with other methods.