Landslide displacement prediction using discrete wavelet transform and extreme learning machine based on chaos theory

Landslide displacement system is generally characterized by non-stationary and nonlinear characteristics. Traditionally, many artificial neural network (ANN) models have been proposed to forecast landslide displacement. However, the underlying non-stationary characteristics in the landslide displacement are not captured, and the input–output variables of the ANN models are not selected nonlinearly. To overcome these drawbacks, this paper proposes the chaos theory-based discrete wavelet transform (DWT)–extreme learning machine (ELM) model to predict landslide displacement. The DWT method is adopted to decompose the landslide displacement into several low- and high-frequency components to address the non-stationary characteristics. And chaos theory is used to determine the input–output variables of the ELM model. The cumulative displacement time series of the Baishuihe and Baijiabao landslides in the Three Gorges Reservoir Area, China, are used as data sets. The results show that the chaotic DWT-ELM model accurately predicts landslide displacement. The chaotic DWT–support vector machine (SVM), chaotic DWT–back-propagation neural network (BPNN) and single chaotic ELM models are used for comparisons. The comparison results show that the chaotic DWT-ELM model achieves higher prediction accuracy than do the chaotic DWT-SVM, chaotic DWT-BPNN and the single chaotic ELM models.