Future of Information and Communication Conference (FICC) 2025
28-29 April 2025
Publication Links
IJACSA
Special Issues
Future of Information and Communication Conference (FICC)
Computing Conference
Intelligent Systems Conference (IntelliSys)
Future Technologies Conference (FTC)
International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 5, 2024.
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.
Ping Jiang and Jiejie Chen, “Ensemble Empirical Mode Decomposition Based on Sparse Bayesian Learning with Mixed Kernel for Landslide Displacement Prediction” International Journal of Advanced Computer Science and Applications(IJACSA), 15(5), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150510
@article{Jiang2024,
title = {Ensemble Empirical Mode Decomposition Based on Sparse Bayesian Learning with Mixed Kernel for Landslide Displacement Prediction},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150510},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150510},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {5},
author = {Ping Jiang and Jiejie Chen}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.