Metabolic grey early warning model for dam deformation based on wavelet denoising

Wu, Yunxing; Gu, Yanchang

doi:10.1051/matecconf/201824602033

Cited by 2 publications

(3 citation statements)

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“…n i=k+1 (7) CMSE criterion is to find the kth IMF, so as to determine that the first k highfrequency IMFs are mainly noisy information. This method has the advantages of simple calculation and strong adaptive, and does not need to set the threshold manually.…”

Section: T (T)=mentioning

confidence: 99%

“…It has been widely used in data noise reduction, signal analysis, image processing and other fields [5,6]. Wu [7] added wavelet analysis to GM (1,1) model, and the results showed that wavelet threshold denoising can obviously remove the noise in original data. Li [8] also used wavelet analysis to denoise the dam deformation data, and then reconstructed the extracted comprehensive components to obtain a hybrid model to predict the dam deformation.…”

Section: Introductionmentioning

confidence: 99%

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Deformation Prediction of Gravity Dam Based on EMD-SARIMA Model

Bai

Zhang

et al. 2022

Advances in Transdisciplinary Engineering

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Hydraulic engineering plays an important role in energy construction in China. As the most important water retaining structure, the deformation trend and safety state of dam is undoubtedly the most concerned problem in engineering. Dam deformation monitoring data is the most critical information to understand dam deformation. So, the analysis and prediction of dam deformation monitoring data is an important measure to master dam safety state. However, the monitoring data of dam generally contains noise components. In order to reduce the noise influence and improve the stability and accuracy of dam monitoring data. EMD-SARIMA model was established in this paper. The monitoring data was decomposed into several Intrinsic Mode Function (IMF) from high to low frequency by using Empirical Mode Decomposition (EMD). Then, the data was reconstructed after eliminating the IMF mainly containing noise based on the Continuous Mean Square Error (CMSE) criterion. Finally, a Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model was established for the reconstructed data. The results show that EMD can effectively reduce the noise in dam monitoring data. The reconstructed data is more stable than the original data, and closer to the actual displacement process of the dam. Compared with SARIMA model, the prediction accuracy of EMD-SARIMA model meets the requirements, and is more accurate and less noise effect. It can be applied to denoise data and prediction analysis of gravity dam.

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Section: T (T)=mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deformation Prediction of Gravity Dam Based on EMD-SARIMA Model

Bai

Zhang

et al. 2022

Advances in Transdisciplinary Engineering

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“…The traditional signal decomposition techniques mainly include Fourier transform, Wavelet Decomposition (WD) and Empirical Mode Decomposition (EMD) which are frequently applied to decompose the dam deformation sequence [24]. Fourier transform can realize the mutual conversion from time domain to frequency domain, while its conditions are relatively harsh, and there is no Fourier transform for considerable useful signals; WD has a certain priority, while it is difficult to choose the basis function and decomposition scale.…”

Section: Introductionmentioning

confidence: 99%

Exploration of Multi-Scale Reconstruction Framework in Dam Deformation Prediction

Yuan

Cao

et al. 2021

Applied Sciences

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Affected by various complex factors, dam deformation monitoring data usually reflect volatility and non-linear characteristics, and traditional prediction models are difficult to accurately capture the complex laws of dam deformation. A multi-scale deformation prediction model based on Variational Modal Decomposition (VMD) signal decomposition technology is proposed in this study. The method first decomposes the original deformation sequence into a series of sub-sequences with different frequencies, then the decomposed sub-sequences are modeled and predicted by Long Short-Term Memory neural network (LSTM) and Random Forest (RF) according to different frequencies. Finally, the prediction results of all sub-sequences are reconstructed to obtain the final deformation prediction results. In this process, it is proposed to use the instantaneous frequency mean method to determine the decomposition modulus of VMD. The innovation of this paper is to decompose the monitoring data with high volatility, and use LSTM and RF prediction, respectively, according to the frequency of the monitoring data, so as to realize the more accurate capture of volatility data during the prediction process. The case analysis results show that the proposed model can effectively solve the negative impact of the original data volatility on the prediction results, and is superior to the traditional prediction models in terms of stability and generalization ability, which has an important reference value for accurately predicting dam deformation and has far-reaching engineering significance.

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Metabolic grey early warning model for dam deformation based on wavelet denoising

Cited by 2 publications

References 1 publication

Deformation Prediction of Gravity Dam Based on EMD-SARIMA Model

Deformation Prediction of Gravity Dam Based on EMD-SARIMA Model

Exploration of Multi-Scale Reconstruction Framework in Dam Deformation Prediction

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