Analysis of Dam Deformation Using Artificial Neural Networks Methods and Singular Spectrum Analysis

Gourine, Bachir; Khelifa, Sofiane

doi:10.1007/978-3-319-70548-4_255

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…In recent years, a variety of machine learning architectures have been used in the field of dam safety monitoring, such as the autoregressive integrated moving average (ARIMA) algorithm [8], the support vector machine (SVM) algorithm [9], the artificial neural network (ANN) algorithm [10][11][12], and the random forest (RF) algorithm [13,14], etc. These algorithms can predict dam displacement with reasonable accuracy; among them, the ANN algorithm illustrates superior performance in dealing with nonlinear problems [15,16]. Liu et al [17] used the long short-term memory (LSTM) model to predict the displacement of the arch dam.…”

Section: Introductionmentioning

confidence: 99%

Variation Trend Prediction of Dam Displacement in the Short-Term Using a Hybrid Model Based on Clustering Methods

Lin,

Zou,

Lai

et al. 2023

Applied Sciences

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The deformation behavior of a dam can comprehensively reflect its structural state. By comparing the actual response with model predictions, dam deformation prediction models can detect anomalies for effective advance warning. Most existing dam deformation prediction models are implemented within a single-step prediction framework; the single-time-step output of these models cannot represent the variation trend in the dam deformation, which may contain important information on dam evolution during the prediction period. Compared with the single value prediction, predicting the tendency of dam deformation in the short term can better interpret the dam’s structural health status. Aiming to capture the short-term variation trends of dam deformation, a multi-step displacement prediction model of concrete dams is proposed by combining the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm, the k-harmonic means (KHM) algorithm, and the error minimized extreme learning machine (EM-ELM) algorithm. The model can be divided into three stages: (1) The CEEMDAN algorithm is adopted to decompose dam displacement series into different signals according to their timing characteristics. Moreover, the sample entropy (SE) method is used to remove the noise contained in the decomposed signals. (2) The KHM clustering algorithm is employed to cluster the denoised data with similar characteristics. Furthermore, the sparrow search algorithm (SSA) is utilized to optimize the KHM algorithm to avoid the local optimal problem. (3) A multi-step prediction model to capture the short-term variation of dam displacement is established based on the clustered data. Engineering examples show that the model has good prediction performance and strong robustness, demonstrating the feasibility of applying the proposed model to the multi-step forecasting of dam displacement.

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Section: Introductionmentioning

confidence: 99%

Variation Trend Prediction of Dam Displacement in the Short-Term Using a Hybrid Model Based on Clustering Methods

Lin,

Zou,

Lai

et al. 2023

Applied Sciences

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show abstract

“…This can reduce the influence of artificial parameters on model accuracy while ensuring the timeliness of parameter selection. Zhu et al [28] used adaptive theory to optimize the artificial bee colony algorithm and combined it with a back propagation (BP) neural network model to accurately predict the deformation sequences of high arch dams and provide a safety state analysis of dam structures with Gourine's [29] combined singular spectrum analysis and ANNs to study the influencing factors of dam displacement, and they achieved good results. Wei et al [30] considered the influence that complex nonlinearities in the residual sequence would have on the prediction accuracy in the modeling process of deformation prediction, and they proposed a combined prediction model, which integrated wavelet decomposition, neural networks, and integrated moving average autonomy on the basis of traditional statistical models.…”

Section: Introductionmentioning

confidence: 99%

MHA-ConvLSTM Dam Deformation Prediction Model Considering Environmental Volume Lag Effect

Liu

Ding

2023

Applied Sciences

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The construction of a reasonable and reliable deformation prediction model is of great practical significance for dam safety assessment and risk decision-making. Traditional dam deformation prediction models are susceptible to interference from redundant features, weak generalization ability, and a lack of model interpretation. Based on this, a deformation prediction model that considers the lag effect of environmental quantities is proposed. The model first constructs a new deformation lag influence factor based on the plain HST model through the lag quantization algorithm. Secondly, the attention and memory capacity of the model is improved by introducing a multi-head attention mechanism to the features of the long-time domain deformation influence factor, and finally, the extracted dynamic features are transferred to the ConvLSTM model for learning, training, and prediction. The results of the simulation tests based on the measured deformation data of an active dam show that the introduction of the deformation lag factor not only improves the interpretation of the prediction model for deformation but also makes the prediction of deformation more accurate, and it can improve the evaluation indexes such as RMSE by 50%, the nMAPE by 40%, and R2 by 10% compared with the traditional prediction model. The combined prediction model is more capable of mining the hidden features of the data and has a deeper picture of the overall peak and local extremes of the deformation data, which provides a new way of thinking for the dam deformation prediction model.

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Segmented modeling method of dam displacement based on BEAST time series decomposition

Yang

et al. 2022

Measurement

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Analysis of Dam Deformation Using Artificial Neural Networks Methods and Singular Spectrum Analysis

Cited by 3 publications

References 4 publications

Variation Trend Prediction of Dam Displacement in the Short-Term Using a Hybrid Model Based on Clustering Methods

Variation Trend Prediction of Dam Displacement in the Short-Term Using a Hybrid Model Based on Clustering Methods

MHA-ConvLSTM Dam Deformation Prediction Model Considering Environmental Volume Lag Effect

Segmented modeling method of dam displacement based on BEAST time series decomposition

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