Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Landslide displacement prediction based on multivariate chaotic model and extreme learning machine
https://orcid.org/0000-0002-5159-1635
AbstractThis 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.
HighlightsThe chaos characteristics of the displacement time series of reservoir landslides are identified.The main trigger factors of reservoir landslides are identified.Multivariate chaotic Extreme Learning Machine (ELM) model is used to predict the displacement of reservoir landslides.The displacements of two reservoir landslides in the Three Gorges Reservoir Area are predicted accurately.
Landslide displacement prediction based on multivariate chaotic model and extreme learning machine
https://orcid.org/0000-0002-5159-1635
AbstractThis 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.
HighlightsThe chaos characteristics of the displacement time series of reservoir landslides are identified.The main trigger factors of reservoir landslides are identified.Multivariate chaotic Extreme Learning Machine (ELM) model is used to predict the displacement of reservoir landslides.The displacements of two reservoir landslides in the Three Gorges Reservoir Area are predicted accurately.
Landslide displacement prediction based on multivariate chaotic model and extreme learning machine
https://orcid.org/0000-0002-5159-1635
AbstractThis 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.
HighlightsThe chaos characteristics of the displacement time series of reservoir landslides are identified.The main trigger factors of reservoir landslides are identified.Multivariate chaotic Extreme Learning Machine (ELM) model is used to predict the displacement of reservoir landslides.The displacements of two reservoir landslides in the Three Gorges Reservoir Area are predicted accurately.
Landslide displacement prediction based on multivariate chaotic model and extreme learning machine
Huang, Faming (Autor:in) / Huang, Jinsong (Autor:in) / Jiang, Shuihua (Autor:in) / Zhou, Chuangbing (Autor:in)
Engineering Geology ; 218 ; 173-186
14.01.2017
14 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Landslide displacement prediction based on multivariate chaotic model and extreme learning machine
| British Library Online Contents | 2017
Landslide displacement prediction based on multivariate chaotic model and extreme learning machine
| Online Contents | 2017
Landslide displacement prediction based on multivariate chaotic model and extreme learning machine
| Online Contents | 2017