标題: A Deep U-Net-ConvLSTM Framework with Hydrodynamic Model for Basin-Scale Hydrodynamic Prediction

作者: Li, A (Li, Ao); Zhang, WS (Zhang, Wanshun); Zhang, X (Zhang, Xiao); Chen, G (Chen, Gang); Liu, X (Liu, Xin); Jiang, AN (Jiang, Anna); Zhou, F (Zhou, Feng); Peng, H (Peng, Hong)

來源出版物: WATER  卷: 16  期: 5  文獻号: 625  DOI: 10.3390/w16050625  Published Date: 2024 MAR  

摘要: Traditional hydrodynamic models face the significant challenge of balancing the demands of long prediction spans and precise boundary conditions, large computational areas, and low computational costs when attempting to rapidly and accurately predict the nonlinear spatial and temporal characteristics of fluids at the basin scale. To tackle this obstacle, this study constructed a novel deep learning framework with a hydrodynamic model for the rapid spatiotemporal prediction of hydrodynamics at the basin scale, named U-Net-ConvLSTM. A validated high-fidelity hydrodynamic mechanistic model was utilized to build a 20-year hydrodynamic indicator dataset of the middle and lower reaches of the Han River for the training and validation of U-Net-ConvLSTM. The findings indicate that the R2 value of the model surpassed 0.99 when comparing the single-step prediction results with the target values. Additionally, the required computing time fell by 62.08% compared with the hydrodynamic model. The ablation tests demonstrate that the U-Net-ConvLSTM framework outperforms other frameworks in terms of accuracy for basin-scale hydrodynamic prediction. In the multi-step-ahead prediction scenarios, the prediction interval increased from 1 day to 5 days, while consistently maintaining an R2 value above 0.7, which demonstrates the effectiveness of the model in the missing boundary conditions scenario. In summary, the U-Net-ConvLSTM framework is capable of making precise spatiotemporal predictions in hydrodynamics, which may be considered a high-performance computational solution for predicting hydrodynamics at the basin scale.

作者關鍵詞: deep learning; U-Net; ConvLSTM; hydrodynamic prediction

地址: [Li, Ao; Zhang, Wanshun; Zhang, Xiao; Chen, Gang; Liu, Xin; Jiang, Anna; Zhou, Feng] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

[Zhang, Wanshun] Wuhan Univ, China Inst Dev Strategy & Planning, Wuhan 430079, Peoples R China.

[Zhang, Wanshun] Wuhan Univ, Sch Water Resources & Hydropower Engn, State Key Lab Water Resources & Hydropower Engn Sc, Wuhan 430072, Peoples R China.

[Peng, Hong] Wuhan Univ, Sch Water Resources & Hydropower Engn, Wuhan 430072, Peoples R China.

通訊作者地址: Zhang, WS (通訊作者)，Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

Zhang, WS (通訊作者)，Wuhan Univ, China Inst Dev Strategy & Planning, Wuhan 430079, Peoples R China.

Zhang, WS (通訊作者)，Wuhan Univ, Sch Water Resources & Hydropower Engn, State Key Lab Water Resources & Hydropower Engn Sc, Wuhan 430072, Peoples R China.

電子郵件地址: liao_la@whu.edu.cn; wszhang@whu.edu.cn

影響因子：3.4