A Spatial-Reduction Attention-Based BiGRU Network for Water Level Prediction

Bao, Kexin; Bi, Jinqiang; Ma, Ruixin; Sun, Yue; Zhang, Wenjia; Wang, Yongchao

doi:10.3390/w15071306

Cited by 5 publications

(1 citation statement)

References 30 publications

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“…where W→ In order to effectively mine the laws of forward and backward information in time series data, the BiGRU [33][34][35] model employs a structure consisting of a bi-directional recurrent neural network with forward and backward propagation. The structure is shown in Figure 4.…”

Section: Bi-directional Gated Recurrent Unitmentioning

confidence: 99%

Short-Term Wind Power Forecasting Based on VMD and a Hybrid SSA-TCN-BiGRU Network

Zhang,

Sun

et al. 2023

Applied Sciences

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Wind power generation is a renewable energy source, and its power output is influenced by multiple factors such as wind speed, direction, meteorological conditions, and the characteristics of wind turbines. Therefore, accurately predicting wind power is crucial for the grid operation and maintenance management of wind power plants. This paper proposes a hybrid model to improve the accuracy of wind power prediction. Accurate wind power forecasting is critical for the safe operation of power systems. To improve the accuracy of wind power prediction, this paper proposes a hybrid model incorporating variational modal decomposition (VMD), a Sparrow Search Algorithm (SSA), and a temporal-convolutional-network-based bi-directional gated recurrent unit (TCN-BiGRU). The model first uses VMD to break down the raw power data into several modal components, and then it builds an SSA-TCN-BIGRU model for each component for prediction, and finally, it accumulates all the predicted components to obtain the wind power prediction results. The proposed short-term wind power prediction model was validated using measured data from a wind farm in China. The proposed VMD-SSA-TCN-BiGRU forecasting framework is compared with benchmark models to verify its practicability and reliability. Compared with the TCN-BiGRU, the symmetric mean absolute percentage error, the mean absolute error, and the root mean square error of the VMD-SSA-TCN-BiGRU model reduced by 34.36%, 49.14%, and 55.94%.

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Section: Bi-directional Gated Recurrent Unitmentioning

confidence: 99%

Short-Term Wind Power Forecasting Based on VMD and a Hybrid SSA-TCN-BiGRU Network

Zhang,

Sun

et al. 2023

Applied Sciences

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A Hybrid Data-Driven Deep Learning Prediction Framework for Lake Water Level Based on Fusion of Meteorological and Hydrological Multi-source Data

Yao,

Wang,

et al. 2023

Nat Resour Res

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Temporal Fusion Transformer-Gaussian Process for Multi-Horizon River Level Prediction and Uncertainty Quantification

Wang

Tang

2023

J CIRCUIT SYST COMP

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Accurate river level prediction is vital in water resource management and flood mitigation. Recent advances in data-driven modeling, especially in deep learning, provide profound insights into predicting river levels when mechanistic hydrological knowledge is absent. However, they often do not capture predictive uncertainty well, making them less robust in the hydrological prediction as they overconfidently extrapolate. Moreover, they are not flexible in handling hydrological variables with heterogeneous characteristics. In this work, we present the Temporal Fusion Transformer-Gaussian Process (TFT-GP), a novel model for multi-horizon probabilistic river level prediction. We show how TFT-GP inherits the nice properties of the Gaussian process and deep neural networks, giving it excellent representative power and uncertainty quantification ability. The performance of TFT-GP is thoroughly compared with existing well-known deep learning models in three real-world hydrological datasets, and the results showed that TFT-GP is not only more accurate in point prediction but also more reasonable in uncertainty quantification.

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A Spatial-Reduction Attention-Based BiGRU Network for Water Level Prediction

Cited by 5 publications

References 30 publications

Short-Term Wind Power Forecasting Based on VMD and a Hybrid SSA-TCN-BiGRU Network

Short-Term Wind Power Forecasting Based on VMD and a Hybrid SSA-TCN-BiGRU Network

A Hybrid Data-Driven Deep Learning Prediction Framework for Lake Water Level Based on Fusion of Meteorological and Hydrological Multi-source Data

Temporal Fusion Transformer-Gaussian Process for Multi-Horizon River Level Prediction and Uncertainty Quantification

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