Abstract
Existing component separation methods fail to consider the complex nonlinear relationship between dam effect quantities and environmental variables. In this study, a novel nonlinear component separation method for the effect quantities is proposed by combining kernel partial least squares (KPLS) and pseudosamples. By this method, a nonlinear monitoring model is established based on KPLS, and the complicated nonlinear relationship between the effect quantities and environmental variables can be determined accurately through the model. Furthermore, special pseudosamples are constructed to separate independent components and coupling influence components of environmental factors from the KPLS model. These methods have been applied into a super‐high arch dam, and the separated displacement components conform to the general deformation law. The presented results indicate that it is more reliable than traditional multiple linear regression models.
Highlights
Dams provide substantial comprehensive benefits, including power generation, flood control, and irrigation
(2) e independent component of the hydrostatic pressure is relatively smaller in the kernel partial least squares (KPLS) model than in the multiple linear regression (MLR) model, and the difference between the two models is evident when the water storage is close to the normal water storage level
A nonlinear method for the component separation of dam effect quantities is proposed by combining KPLS and pseudosamples. e method initially uses KPLS to establish a nonlinear analysis model for dam safety monitoring. e model can determine the complex nonlinear relationship between dam effect quantities and environmental variables, avoid the influence of noise and environmental factor multicollinearities, and fully utilize the correlation information between multiple effect quantities to eliminate noise effectively. e model has high fitting and prediction accuracy, providing a strong guarantee for subsequent component separation
Summary
Dams provide substantial comprehensive benefits, including power generation, flood control, and irrigation. The multicollinearity may exist between the reservoir level and temperature given seasonal operations of the reservoir, among different terms of the polynomial function to describe the effect of hydrostatic pressure [12] or among influence factors and their lagged variables when considering delayed effects. In KPLS, the original input data are nonlinearly transformed into a high-dimensional space via a kernel function, and a linear PLS model is created in the high-dimensional space. A nonlinear component separation method for dam effect quantities is proposed by combining KPLS and pseudosamples.
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