Wavelet support vector machine-based prediction model of dam deformation

• Published in: Mechanical systems and signal processing Vol. 110; pp. 412 - 427
• Main Authors: Su, Huaizhi, Li, Xing, Yang, Beibei, Wen, Zhiping
• Format: Journal Article
• Language: English
• Published: Berlin Elsevier Ltd 15.09.2018; Elsevier BV
• Subjects: 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
• DOI: 10.1016/j.ymssp.2018.03.022

•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.