Research on the Uplift Pressure Prediction of Concrete Dams Based on the CNN-GRU Model

Hua, Guowei; Wang, Shijie; Xiao, M.; Hu, Shaohua

doi:10.3390/w15020319

Cited by 15 publications

(4 citation statements)

References 28 publications

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“…The GRU model has proven to be more effective for short-term flood forecasts compared to LSTM [57]. While the CNN-GRU model has shown promise in flood prediction, enhancements in its performance for long-term forecasting are still necessary [60,61].…”

Section: The Flood Prediction Models and Lag Time Preprocessingmentioning

confidence: 99%

Flood Forecasting Using Hybrid LSTM and GRU Models with Lag Time Preprocessing

Zhang,

Zhou,

Van Griensven Thé

et al. 2023

Water

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Climate change and urbanization have increased the frequency of floods worldwide, resulting in substantial casualties and property loss. Accurate flood forecasting can offer governments early warnings about impending flood disasters, giving them a chance to evacuate and save lives. Deep learning is used in flood forecasting to improve the timeliness and accuracy of flood water level predictions. While various deep learning models similar to Long Short-Term Memory (LSTM) have achieved notable results, they have complex structures with low computational efficiency, and often lack generalizability and stability. This study applies a spatiotemporal Attention Gated Recurrent Unit (STA-GRU) model for flood prediction to increase the models’ computing efficiency. Another salient feature of our methodology is the incorporation of lag time during data preprocessing before the training of the model. Notably, for 12-h forecasting, the STA-GRU model’s R-squared (R2) value increased from 0.8125 to 0.9215. Concurrently, the model manifested reduced root mean squared error (RMSE) and mean absolute error (MAE) metrics. For a more extended 24-h forecasting, the R2 value of the STA-GRU model improved from 0.6181 to 0.7283, accompanied by diminishing RMSE and MAE values. Seven typical deep learning models—the LSTM, the Convolutional Neural Networks LSTM (CNNLSTM), the Convolutional LSTM (ConvLSTM), the spatiotemporal Attention Long Short-Term Memory (STA-LSTM), the GRU, the Convolutional Neural Networks GRU (CNNGRU), and the STA-GRU—are compared for water level prediction. Comparative analysis delineated that the use of the STA-GRU model and the application of the lag time pre-processing method significantly improved the reliability and accuracy of flood forecasting.

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Section: The Flood Prediction Models and Lag Time Preprocessingmentioning

confidence: 99%

Flood Forecasting Using Hybrid LSTM and GRU Models with Lag Time Preprocessing

Zhang,

Zhou,

Van Griensven Thé

et al. 2023

Water

View full text Add to dashboard Cite

show abstract

“…As one of the most important deep learning frameworks, a convolutional neural network has efficient feature extraction ability and a lower number of parameters [21], and can be divided into 3D-CNN, 2D-CNN and 1D-CNN according to the application object. 2D-CNN is commonly used for the spatial feature extraction of panel data, and 1D-CNN is commonly used for the temporal feature extraction of sequences.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Multi-Point Deformation Prediction Model for Concrete Dams Based on Spatial Feature Vector

Chen,

Liu

2023

Applied Sciences

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Deformation can effectively reflect the structural state of concrete dams and, thus, establishing na accurate concrete dam deformation prediction model is important for dam health monitoring and early warning strategies. To address the problem that the spatial coordinates introduced in the traditional multi-point deformation prediction model of dams not being able to accurately and efficiently reflect the spatial correlation of multiple-measuring points, a 2D-1D-CNN model is proposed which expresses the spatial correlation between each measuring point through spatial feature vectors, replacing the spatial coordinates in the traditional multi-point model. First, the spatial feature vector is extracted from the historical spatio-temporal panel series of deformation values of measuring points via a Two-Dimensional Convolutional Neural Network (2D-CNN); second, the vector is combined with the environmental impact factor of dam deformation to form the final input factor of fused spatial features; and, thirdly, this vector is combined with the environmental impact factors of dam deformation to form the final input factor of fused spatial features, and the non-linear linkage between the factors and the measured displacement values is constructed by the efficient feature processing capability of a One-Dimensional Convolutional Neural Network (1D-CNN) to obtain the prediction results. Finally, the actual monitoring data of a concrete dam in China are used as an example to verify the validity of the model. The results show that the proposed model outperforms the other models in most cases, respectively, which verifies the effectiveness of the proposed model in this paper.

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“…Given that environmental factors may exhibit a lag effect on dam deformation, overlooking time dependence can detrimentally impact prediction outcomes. To address these challenges, deep learning algorithms tailored for time series analysis have been increasingly utilized in the dam health monitoring field [15][16][17][18][19]. Their application has facilitated the realization of long-term predictions for deformation series.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Point Joint Prediction Model for High-Arch Dam Deformation Considering Spatial and Temporal Correlation

Cao,

Wen,

Feng

et al. 2024

Water

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Deformation monitoring for mass concrete structures such as high-arch dams is crucial to their safe operation. However, structure deformations are influenced by many complex factors, and deformations at different positions tend to have spatiotemporal correlation and variability, increasing the difficulty of deformation monitoring. A novel deep learning-based monitoring model for high-arch dams considering multifactor influences and spatiotemporal data correlations is proposed in this paper. First, the measurement points are clustered to capture the spatial relationship. Successive multivariate mode decomposition is applied to extract the common mode components among the correlated points as spatial influencing factors. Second, the relationship between various factors and deformation components is extracted using factor screening. Finally, a deep learning prediction model is constructed with stacked components to obtain the final prediction. The model is validated based on practical engineering. In nearly one year of high-arch dam deformation prediction, the root mean square error is 0.344 and the R2 is 0.998, showing that the modules within the framework positively contribute to enhancing prediction performance. The prediction results of different measurement points as well as the comparison results with benchmark models show its superiority and generality, providing an advancing and practical approach for engineering structural health monitoring, particularly for high-arch dams.

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Research on the Uplift Pressure Prediction of Concrete Dams Based on the CNN-GRU Model

Cited by 15 publications

References 28 publications

Flood Forecasting Using Hybrid LSTM and GRU Models with Lag Time Preprocessing

Flood Forecasting Using Hybrid LSTM and GRU Models with Lag Time Preprocessing

Multi-Point Deformation Prediction Model for Concrete Dams Based on Spatial Feature Vector

A Multi-Point Joint Prediction Model for High-Arch Dam Deformation Considering Spatial and Temporal Correlation

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