Wavelet support vector machine-based prediction model of dam deformation

•SVM and other methods are combined to build prediction model of dam deformation.•Three key problems on SVM are investigated.•The proposed approach can reduce the human impact on modeling process. Considering the strong nonlinear dynamic characteristics of dam deformation, the prediction model of da...

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Vydáno v:Mechanical systems and signal processing Ročník 110; s. 412 - 427
Hlavní autoři: Su, Huaizhi, Li, Xing, Yang, Beibei, Wen, Zhiping
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin Elsevier Ltd 15.09.2018
Elsevier BV
Témata:
Basis functions
Characteristics identification
Dam deformation
Dams
Deformation
Design engineering
Dynamic characteristics
Improved particle swarm optimization algorithm
Kernel functions
Mathematical models
Model accuracy
Morlet wavelet
Particle swarm optimization
Prediction model
Reconstruction
Support vector machines
Wavelet analysis
Wavelet support vector machine
Wavelet transforms
Dam deformation
Improved particle swarm optimization algorithm
Characteristics identification
Prediction model
Wavelet support vector machine
ISSN:0888-3270, 1096-1216
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Shrnutí:•SVM and other methods are combined to build prediction model of dam deformation.•Three key problems on SVM are investigated.•The proposed approach can reduce the human impact on modeling process. Considering the strong nonlinear dynamic characteristics of dam deformation, the prediction model of dam deformation is investigated. Support vector machine (SVM) is combined with other methods, such as phase space reconstruction, wavelet analysis and particle swarm optimization (PSO), to build the prediction model of dam deformation. Firstly, the chaotic characteristics and the predictable time scale of dam deformation are identified by implementing the phase space reconstruction of observation data series on dam deformation. Secondly, a SVM-based prediction model of dam deformation is proposed. The reconstructed phase space of observed deformation and the Morlet wavelet basis function are selected as the input vector and the kernel function of SVM. Thirdly, the PSO algorithm is improved to implement the parameter optimization of SVM-based prediction model of dam deformation. Finally, the displacement of one actual dam is taken as an example. The results demonstrate the modeling efficiency and forecasting accuracy can be improved.
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ISSN:0888-3270
1096-1216
DOI:10.1016/j.ymssp.2018.03.022