Coupling VMD and MSSA denoising for dam deformation prediction

The measured data of dams in complex environments are influenced by factors such as external forcing and instrumentation errors and inevitably contain noise, which makes accurately predicting dam deformation challenging. Traditional denoising methods suffer from both excessive denoising and incomplete denoising, resulting in limited improvement in prediction accuracy. To better address these problems, this paper proposes a denoising method that combines adaptive variational modal decomposition (VMD) with improved multichannel singular spectrum analysis (MSSA). The algorithm denoises the decomposed subsequence while accounting for the covariance between the subsequence and extracts valid information from the residual sequences. A concrete panel rockfill dam is used as an example for validation. The results show that the proposed algorithm effectively preserves the intrinsic components and coupling relationships between the decomposed subsequence. Based on machine learning models with different sensitivities to noise, the prediction accuracy of the proposed denoising algorithm model is better than that of the traditional denoising algorithm model. It is more suitable for deformation prediction modelling in complex situations involving actual dams, which further enhances the generalization ability of the prediction model.