Flow prediction in the lower Yellow River based on CEEMDAN-BILSTM coupled model

Zhang, Xianqi; Qiao, Wenbao; Huang, Jiafeng; Shi, Jingwen; Zhang, Minghui

doi:10.2166/ws.2022.426

Cited by 8 publications

(2 citation statements)

References 10 publications

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“…The reason is that BiLSTM can simultaneously consider the preceding and succeeding information of sequential data, acquiring more comprehensive contextual information through forward and backward learning [14]. Currently, certain LSTM-based models are applied in fields such as landslide prediction [15], river flow forecasting [16], and inflow rate prediction [17], among others, becoming popular methods for handling time-series prediction.…”

Section: Introductionmentioning

confidence: 99%

Subway track foundation settlement deformation prediction based on the BiLSTM-AdaBoost model

Dang,

Yin,

Liu

et al. 2024

Eng. Res. Express

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The rapid economic expansion has spurred extensive construction near subway networks, im-pacting the stability of their track foundations. Consequently, it's crucial to monitor and predict settlement in subway track foundations. However, the dynamic deformation patterns often exhibit nonlinearity and non-stationarity, posing challenges for traditional linear regression models. To tackle this, our study integrates the BiLSTM (bi-directional long short-term memory) network with the AdaBoost ensemble learning algorithm. Using settlement data from Shanghai metro monitor-ing points, the model is trained and evaluated employing R2 (coefficient of determination), MAE (mean absolute error), and RMSE (root mean square error). Results show that our proposed model displays superior predictive accuracy compared to the LSTM and the BiLSTM, with an average training set R2 of 0.99, test set R2 of 0.78, average MAE of 0.32mm, and average RMSE of 0.4mm. Consequently, for forecasting subway track foundation deformations, employing our network model ensures highly accurate predictive capabilities.

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Section: Introductionmentioning

confidence: 99%

Subway track foundation settlement deformation prediction based on the BiLSTM-AdaBoost model

Dang,

Yin,

Liu

et al. 2024

Eng. Res. Express

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“…Li X et al proposed a CNN-LSTM river flow prediction model, which effectively solved the problem of diversity reduction in depth networks [32]. Zhang combined the advantages of complete ensemble empirical mode decomposition with adaptive noise and BiLSTM (CEEMDAN-BiLSTM) to predict flow in the middle and lower reaches of the Yellow River and verified the effectiveness of bidirectional recurrent neural networks in terms of traffic prediction [33]. Liang et al adopted the improved the BiGRU model with the introduction of attention mechanism to achieve the high-precision prediction of the discharge during the 36 h prediction period [34].…”

Section: Introductionmentioning

confidence: 99%

Overflow Capacity Prediction of Pumping Station Based on Data Drive

Guo¹,

Yan²,

Chen³

et al. 2023

Water

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In recent years, the information requirements of pumping stations have become higher and higher. The prediction of overflow capacity can provide important reference for flood carrying capacity, water resource scheduling and water safety. In order to improve the accuracy, stability and generalization ability of the model, a BiGRU–ARIMA data-driven method based on self-attention mechanism is proposed to predict the flow capacity of the pump station. Bidirectional gated recurrent unit (BiGRU), a variant of cyclic neural network (RNN), can not only deal with nonlinear components well, but also deal with the problem of insufficient dependence over long distances and has a simple structure. Autoregressive integrated moving average (ARIMA) has the advantage of being sensitive to linear components. Firstly, the characteristics of the pre-processed pump station data are selected and screened through Pearson correlation coefficient and a self-attention mechanism. Then, a bi-directional gated recurrent unit (BiGRU) is used to process the nonlinear components of the data, and a dropout layer is added to avoid overfitting phenomena. We extract the linear features of the obtained error terms using the ARIMA model and use them as correction items to correct the prediction results of the BiGRU model. Finally, we obtain the prediction results of the overflow and water level. The variation characteristics of overdischarge are analyzed by the relation of flow and water level. In this paper, the actual production data of a Grade 9 pumping station of Miyun Reservoir is taken as an example to verify the validity of the model. Model performance is evaluated according to mean absolute error (MAE), mean absolute percentage error (MAPE) and linear regression correlation coefficient (R2). The experimental results show that, compared with the single ARIMAX, BiGRU model and BP neural network, the SA–BiGRU–ARIMA hybrid prediction model has a better prediction effect than other data-driven models.

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Multi-phase hybrid bidirectional deep learning model integrated with Markov chain Monte Carlo bivariate copulas function for streamflow prediction

Iqbal,

Siddiqi

2023

Stoch Environ Res Risk Assess

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Flow prediction in the lower Yellow River based on CEEMDAN-BILSTM coupled model

Cited by 8 publications

References 10 publications

Subway track foundation settlement deformation prediction based on the BiLSTM-AdaBoost model

Subway track foundation settlement deformation prediction based on the BiLSTM-AdaBoost model

Overflow Capacity Prediction of Pumping Station Based on Data Drive

Multi-phase hybrid bidirectional deep learning model integrated with Markov chain Monte Carlo bivariate copulas function for streamflow prediction

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