Landslide displacement prediction based on multivariate chaotic model and extreme learning machine

Title: Landslide displacement prediction based on multivariate chaotic model and extreme learning machine
Creator: Huang, Faming; Huang, Jinsong; Jiang, Shuihua; Zhou, Chuangbing
Relation: Engineering Geology Vol. 218, p. 173-186
Publisher Link: http://dx.doi.org/10.1016/j.enggeo.2017.01.016
Publisher: Elsevier
Resource Type: journal article
Date: 2017
Description: This paper proposes a multivariate chaotic Extreme Learning Machine (ELM) model for the prediction of the displacement of reservoir landslides. The displacement time series of the Baishuihe and Bazimen landslides in the Three Gorges Reservoir Area in China are used as examples. The results show that there are evidences of chaos in the displacement time series. The univariate chaotic ELM model and the multivariate chaotic model based on Particle Swarm Optimization and Support Vector Machine (PSO-SVM) model are also applied for the purpose of comparison. The comparisons show that the multivariate chaotic ELM model achieves higher prediction accuracy than the univariate chaotic ELM model and the multivariate chaotic PSO-SVM model.
Subject: reservoir landslides; displacement prediction; multivariate phase space reconstruction; extreme learning machine; double exponential smoothing
Identifier: http://hdl.handle.net/1959.13/1355684
Identifier: uon:31509
Identifier: ISSN:0013-7952
Language: eng
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