Dam deformation prediction based on EMD and RBF neural network

doi:10.13474/j.cnki.11-2246.2019.0258

Abstract

Abstract: The use of long-term observation data combined with prediction models to estimate the deformation trend of dams is an essential part of dam structure safety monitoring. In this paper, the EMD and RBF neural networks are comprehensively used to study the intrinsic law of nonlinear periodic signal changes in the dam deformation time series. The 4000 data of Xilongchi L022 station is used as the training sample, and the subsequent 80 data is predicted and passed. The statistical analysis of the difference between the predicted result and the measured deformation is used to evaluate the prediction power of the method. The results show that the RMSE in the three directions of N, E, and U are 0.878 6, 0.360 4 and 2.235 mm, respectively. Compared with BP, RBF prediction is better, and it is less affected by data accuracy. MAE and RMSE can be increased by 63% and 57%, respectively, compared with BP. The method of this paper has high computational efficiency and strong generalization ability.

Key words: GNSS automatic monitoring system, empirical mode decomposition (EMD), RBF neural network, dam deformation prediction

CLC Number:

P258

LIU Simin, XU Jingtian, JU Boxiao. Dam deformation prediction based on EMD and RBF neural network[J]. 测绘通报, 2019, 0(8): 88-91,95.

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