Research on Dam Deformation Prediction Model Based on Optimized SVM

Xing, Yin; Chen, Yang; Huang, Saipeng; Wang, Peng; Xiang, Yang

doi:10.3390/pr10091842

Cited by 9 publications

(6 citation statements)

References 33 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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“…With the rapid advancement of computer technology, sophisticated machine algorithms, including artificial neural networks, support vector machines, random forests, multilayer feedforward neural networks, genetic algorithms, and other advanced techniques, have increasingly taken a leading role in developing models for analyzing and predicting dam deformation. These algorithms offer robust technical support for further improving dam safety monitoring and early warning systems [12,13]. Hipni et al [14] successfully predicted the daily water level of a sluice gate through multiple input schemes and effective utilization of SVM algorithm, successfully predicted the daily water level of a sluice gate; Wang et al [15] introduced a hybrid model, combining backpropagation with a genetic algorithm (GA-BP) and multiple population genetic algorithm (MPGA), building upon the BP model.…”

Section: Introductionmentioning

confidence: 99%

Gravity Dam Deformation Prediction Model Based on I-KShape and ZOA-BiLSTM

Jiedeerbieke,

Li,

Chao

et al. 2024

IEEE Access

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This research proposes a dam deformation prediction model based on clustering partitioning and Bidirectional Long Short-Term Memory (BiLSTM) networks to address the limitations of traditional monitoring models in characterizing the distribution characteristics of deformation zones in concrete gravity dams. The model takes into account the intrinsic correlations among monitoring points and achieves more comprehensive deformation monitoring by integrating multiple feature information. Firstly, the improved K-Shape algorithm, which takes into account the time series features and spatial coordinate relationships, is used to cluster and partition the spatial measurement points to better capture the spatial distribution characteristics of the deformation region. Following that, the model hyperparameters undergo iterative optimization using the ZOA optimization algorithm to enhance overall model performance. Finally, a ZOA-BiLSTM modelling process incorporating the correlation characteristics of multiple measurement points is proposed. After validation by engineering examples, the clustering results coincide with the spatial distribution characteristics of dam deformation. Meanwhile, the prediction model has high accuracy and robustness, and predicts the dam deformation from the multi-measurement point correlation dimension, which provides a new and effective method to monitor the overall safety state of the dam.

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“…Therefore, more advanced machine learning techniques should be used for optimization [3]. In recent years, with the rapid development of artificial intelligence technology, a large number of machine learning algorithms such as support vector machines (SVM) [4][5][6][7], artificial neural networks (ANN) [8], extreme learning machines (ELM) [9][10][11], recurrent neural networks (RNN) [12][13][14][15][16], random forest (RF) [17,18] and other technologies have been recognized for their powerful data-driven modeling capabilities and processing capabilities for complex nonlinear systems related to dam deformation prediction. These methods improve the accuracy and robustness of the prediction model by dealing with the deep nonlinear dependence between the dam influence factor and the deformation.…”

Section: Introductionmentioning

confidence: 99%

Dam Deformation Prediction Model Based on Multi-Scale Adaptive Kernel Ensemble

Zhou,

Wang,

et al. 2024

Water

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Aiming at the noise and nonlinear characteristics existing in the deformation monitoring data of concrete dams, this paper proposes a dam deformation prediction model based on a multi-scale adaptive kernel ensemble. The model incorporates Gaussian white noise as a random factor and uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method to decompose the data set finely. Each modal component is evaluated by sample entropy (SE) analysis so that the data set can be reconstructed according to the sample entropy value to retain key information. In addition, the model uses partial autocorrelation function (PACF) to determine the correlation between intrinsic modal function (IMF) and historical data. Then, the global search whale optimization algorithm (GSWOA) is used to accurately determine the parameters of kernel extreme learning machine (KELM), which forms the basis of the dam deformation prediction model based on multi-scale adaptive kernel function. The case analysis shows that CEEMDAN-SE-PACF can effectively extract signal features and identify significant components and trends so as to better understand the internal deformation trend of the dam. In terms of algorithm optimization, compared with the WOA algorithm and other algorithms, the results of the GSWOA algorithm are significantly better than other algorithms and have the optimal convergence. In terms of prediction performance, CEEMDAN-SE-PACF-GSWOA-KELM is superior to the CEEMDAN-WOA-KELM, GSWOA-KELM, CEEMDAN-KELM, and KELM models, showing higher accuracy and stronger stability. This improvement is manifested in the decrease of root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) and the improvement of the R square (R2) value close to 1. These research results provide a new method for dam safety monitoring and evaluation.

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Research on Dam Deformation Prediction Model Based on Optimized SVM

Cited by 9 publications

References 33 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

Gravity Dam Deformation Prediction Model Based on I-KShape and ZOA-BiLSTM

Dam Deformation Prediction Model Based on Multi-Scale Adaptive Kernel Ensemble

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