A reconstruction method for dam monitoring data based on improved singular value decomposition

The existing reconstruction methods for dam monitoring data have the problems of being unable to reconstruct in the non-complete dataset and the reconstruction accuracy is not high enough. Therefore, this paper proposes the dam monitoring data reconstruction method (DSVD) to realize the accurate reconstruction of dam monitoring data in non-complete datasets. The method first adopts the sorting method, which is different from singular spectrum analysis, to construct the dam monitoring matrix. Then, the singular value decomposition is performed, and the hard singular value thresholding algorithm is used to select the singular values to form the dam monitoring approximation matrix. On this basis, an accurate reconstruction model is constructed, and the reconstructed values are used to replace the missing values to realize the accurate reconstruction of dam monitoring data. Finally, the reconstruction method of dam monitoring data is applied to monitoring system of Longgang Reservoir in Chongqing, and the results show that the method can realize the reconstruction of dam monitoring data in non-complete datasets, and the accuracy is higher than that of other models.