Ensemble Empirical Mode Decomposition Based on Sparse Bayesian Learning with Mixed Kernel for Landslide Displacement Prediction

Abstract

Inspired by the principles of decomposition and ensemble, we introduce an Ensemble Empirical Mode Decomposition (EEMD) method that incorporates Sparse Bayesian Learning (SBL) with Mixed Kernel, referred to as EEMD-SBLMK, specifically tailored for landslide displacement prediction. EEMD and Mutual Information (MI) techniques were jointly employed to identify potential input variables for our forecast model. Additionally, each selected component was trained using distinct kernel functions. By minimizing the number of Relevance Vector Machine (RVM) rules computed, we achieved an optimal balance between kernel functions and selected parameters. The EEMD-SBLMK approach generated final results by summing the prediction values of each subsequence along with the residual function associated with the corresponding kernel function. To validate the performance of our EEMD-SBLMK model, we conducted a real-world case study on the Liangshuijing (LSJ) landslide in China. Furthermore, in comparison to RVM-Cubic and RVM-Bubble, EEMD-SBLMK emerged as the most effective method, delivering superior results in the same measurement metrics.