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Prediction of Dam Deformation Using SSA-LSTM Model Based on Empirical Mode Decomposition Method and Wavelet Threshold Noise Reduction
The deformation monitoring information of concrete dams contains some high-frequency components, and the high-frequency components are strongly nonlinear, which reduces the accuracy of dam deformation prediction. In order to solve such problems, this paper proposes a concrete dam deformation monitoring model based on empirical mode decomposition (EMD) combined with wavelet threshold noise reduction and sparrow search algorithm (SSA) optimization of long short-term memory network (LSTM). The model uses EMD combined with wavelet threshold to decompose and denoise the measured deformation data. On this basis, the LSTM model based on SSA optimization is used to mine the nonlinear function relationship between the reconstructed monitoring data and various influencing factors. The engineering example is analyzed and compared with the prediction results of LSTM model and PSO-SVM model. The results show that the mean absolute error (MAE) and root mean square error (RMSE) of the model are 0.05345 and 0.06358, with the complex correlation coefficient R2 of 0.9533 being closer to 1 and a better fit than the other two models. This can effectively mine the relationship in the measured deformation data, and reduce the influence of high-frequency components on the dam prediction accuracy.
Prediction of Dam Deformation Using SSA-LSTM Model Based on Empirical Mode Decomposition Method and Wavelet Threshold Noise Reduction
The deformation monitoring information of concrete dams contains some high-frequency components, and the high-frequency components are strongly nonlinear, which reduces the accuracy of dam deformation prediction. In order to solve such problems, this paper proposes a concrete dam deformation monitoring model based on empirical mode decomposition (EMD) combined with wavelet threshold noise reduction and sparrow search algorithm (SSA) optimization of long short-term memory network (LSTM). The model uses EMD combined with wavelet threshold to decompose and denoise the measured deformation data. On this basis, the LSTM model based on SSA optimization is used to mine the nonlinear function relationship between the reconstructed monitoring data and various influencing factors. The engineering example is analyzed and compared with the prediction results of LSTM model and PSO-SVM model. The results show that the mean absolute error (MAE) and root mean square error (RMSE) of the model are 0.05345 and 0.06358, with the complex correlation coefficient R2 of 0.9533 being closer to 1 and a better fit than the other two models. This can effectively mine the relationship in the measured deformation data, and reduce the influence of high-frequency components on the dam prediction accuracy.
Prediction of Dam Deformation Using SSA-LSTM Model Based on Empirical Mode Decomposition Method and Wavelet Threshold Noise Reduction
Caiyi Zhang (author) / Shuyan Fu (author) / Bin Ou (author) / Zhenyu Liu (author) / Mengfan Hu (author)
2022
Article (Journal)
Electronic Resource
Unknown
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