Deformation Prediction of Dam Based on Optimized Grey Verhulst Model

Huang, Changjun; Zhou, Lv; Liu, Fenliang; Cao, Yuzhen; Liu, Zhong; Xue, Yadong

doi:10.3390/math11071729

Cited by 4 publications

(5 citation statements)

References 32 publications

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“…For example, mathematical statistics, structural analysis and artificial intelligence algorithms have been utilized in the studies of variation law, early warning and risk analysis related to the deformation of dams for decades [18][19][20]. Recently, with the rapid development of artificial intelligence algorithms, artificial neural networks [21][22][23], grey system models [24][25][26], clustering algorithms [27][28][29] and intelligent optimization algorithms [30][31][32] have been widely applied in the deformation prediction of hydraulic structure engineering. These algorithms are able to overcome the shortcomings of traditional prediction models in terms of multidimensional input, model adaptive learning and overfitting.…”

Section: Introductionmentioning

confidence: 99%

Prediction for the Sluice Deformation Based on SOA-LSTM-Weighted Markov Model

Peng,

Xie,

et al. 2023

Water

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Increasingly, deformation prediction has become an essential research topic in sluice safety control, which requires significant attention. However, there is still a lack of practical and efficient prediction modeling for sluice deformation. In order to address the limitations in mining the deep features of long-time data series of the traditional statistical model, in this paper, an improved long short-term memory (LSTM) model and weighted Markov model are introduced to predict sluice deformation. In the method, the seagull optimization algorithm (SOA) is utilized to optimize the hyper-parameters of the neural network structure in LSTM primarily to improve the model. Subsequently, the relevant error sequences of the fitting results of SOA-LSTM model are classified and the Markovity of the state sequence is examined. Then, the autocorrelation coefficients and weights of each order are calculated and the weighted and maximum probability values are applied to predict the future random state of the sluice deformation. Afterwards, the prediction model of sluice deformation on the SOA-LSTM-weighted Markov model is proposed. Ultimately, the presented model is used to predict the settlement characteristics of an actual sluice project in China. The analysis results demonstrate that the proposed model possesses the highest values of R2 and the smallest values of RMSE and absolute relative errors for the monitoring data of four monitoring points. Consequently, it concluded that the proposed method shows better prediction ability and accuracy than the SOA-LSTM model and the stepwise regression model.

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Section: Introductionmentioning

confidence: 99%

Prediction for the Sluice Deformation Based on SOA-LSTM-Weighted Markov Model

Peng,

Xie,

et al. 2023

Water

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“…represents the sign function, η is the step coefficient, pBest is the current location. 5 The average relative error of the fractional Verhulst model is calculated when r pBest = .…”

Section: Setmentioning

confidence: 99%

“…Li et al [11] first combined the twin support vector regression and the Hausdorff derivative operator, then proposed a new grey prediction model, achieving excellent results in predicting the displacement of the Bazimen landslide in China's Three Gorges Reservoir area. Huang et al used the reciprocal sequence (RS) of the accumulative generation operation (AGO) to construct identification parameters for the grey Verhulst model [5]. This method effectively solves problems of initial value optimization and parameter misplacement replacement in the model.…”

Section: Introductionmentioning

confidence: 99%

“…The beetle antennae search algorithm precisely adjusts the order to optimize model accuracy. Comparative analysis with the traditional grey Verhulst model, GM (1,1) model, Cubic Exponential Smoothing algorithm model, DGM (2,1) model, and the Huang optimized grey Verhulst model [5] shows the optimized model has a high fitting and prediction effects on landslide data. This paper provides a reference for early warning and prevention of landslide disasters.…”

Section: Introductionmentioning

confidence: 99%

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Design of Fractional Verhulst Model for Displacement Prediction of Landslide Based on the Optimization of Beetle Antennae Algorithm

Yang,

Li,

Tan

et al. 2023

ITC

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Landslides significantly impact economic development and public safety. Aiming at the problem of insufficient prediction accuracy of the displacement data series of the traditional grey Verhulst model, this paper proposes a fractional Verhulst model optimized using the beetle tentacle search algorithm. First, based on the grey Verhulst model, a fractional order operator is introduced to accurately adjust the magnitude between cumulative values, constructing a fractional order-based grey Verhulst model. Expanding the accumulative order range improves prediction performance. Second, the fractional operator is optimized. The beetle antennae search algorithm finds the optimal fractional order between 0 and 1 in the grey Verhulst model, minimizing average relative error. Finally, using Heifangtai landslide group displacement data from Gansu Province, simulation experiments verified that the model has higher fitting accuracy and prediction effect than the traditional grey Verhulst model, Huang's improved Verhulst model, GM (1,1) model, cubic exponential smoothing model, and DGM (2,1) model. The average relative error is 2.949 %. Results show that the beetle antennae search algorithm optimized fractional order grey prediction model significantly improves fitting and prediction effect on data. The optimized fractional Verhulst model is more suitable for predicting landslide displacement deformation.

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“…Dams are subjected to a variety of factors such as the nature of the dam foundation engineering, temperature conditions, structural design, and their own loading during their complex and variable service, and their properties will gradually deteriorate with increasing service life [5][6][7]. This deterioration may lead to an increase in the probability of accidents in dams, which in turn may cause serious property damage and casualties in the surrounding towns [8][9][10].…”

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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Deformation Prediction of Dam Based on Optimized Grey Verhulst Model

Cited by 4 publications

References 32 publications

Prediction for the Sluice Deformation Based on SOA-LSTM-Weighted Markov Model

Prediction for the Sluice Deformation Based on SOA-LSTM-Weighted Markov Model

Design of Fractional Verhulst Model for Displacement Prediction of Landslide Based on the Optimization of Beetle Antennae Algorithm

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

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