陈志高, 吴子豪, 班亚, 陈志平. 基于调和分析及VMD-BP神经网络的感潮河段流量预报[J]. 武汉大学学报 ( 信息科学版), 2023, 48(8): 1389-1397. DOI: 10.13203/j.whugis20220012
引用本文: 陈志高, 吴子豪, 班亚, 陈志平. 基于调和分析及VMD-BP神经网络的感潮河段流量预报[J]. 武汉大学学报 ( 信息科学版), 2023, 48(8): 1389-1397. DOI: 10.13203/j.whugis20220012
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CHEN Zhigao, WU Zihao, BAN Ya, CHEN Zhiping. Discharge Prediction in Tidal Reach Using Harmonic Analysis and VMD-BP Neural Network[J]. Geomatics and Information Science of Wuhan University, 2023, 48(8): 1389-1397. DOI: 10.13203/j.whugis20220012
Citation: CHEN Zhigao, WU Zihao, BAN Ya, CHEN Zhiping. Discharge Prediction in Tidal Reach Using Harmonic Analysis and VMD-BP Neural Network[J]. Geomatics and Information Science of Wuhan University, 2023, 48(8): 1389-1397. DOI: 10.13203/j.whugis20220012
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基于调和分析及VMD-BP神经网络的感潮河段流量预报

Discharge Prediction in Tidal Reach Using Harmonic Analysis and VMD-BP Neural Network

    摘要
  • 摘要: 针对感潮河段受径流和潮汐共同影响导致流量预报精度较低的问题，结合变分模态分解（variational mode decomposition，VMD）处理非平稳信号的能力与反向(back propagation，BP)神经网络处理非线性问题的优势，提出了基于潮流调和分析及VMD-BP神经网络组合模型的感潮河段流量预报方法。首先，采用潮流调和分析方法对原始流量进行潮流、余流分离；然后，根据误差逆向传播算法构建BP神经网络，并对潮流数据和经VMD处理后的余流数据进行仿真训练；最后，将仿真训练输出的潮流和余流分量叠加重构进而得到最终的流量预报结果。在长江口徐六泾断面开展流量预报实验，结果表明，单独采用BP神经网络方法相对于传统潮流调和分析方法的流量预报精度提高了约3 400 m3/s，相对精度提高了约6%；所提组合模型方法的流量预报精度相对于传统潮流调和分析方法提高了约5 000 m3/s，相对精度提高了约9%。基于调和分析及VMD-BP神经网络的组合模型可以有效提高感潮河段流量预报精度，同时也为流况多变水域的流量预报提供新思路。

     

    Abstract:
      Objectives  In view of the low accuracy of flow forecast in tidal reach due to the combined influence of runoff and tide, combining the ability of variational mode decomposition (VMD) to process non-stationary signals and the advantages of back propagation (BP) neural network to deal with nonlinear problems, this paper proposes a new method for discharge prediction in tidal reach based on the combination model of tidal current harmonic analysis and VMD-BP neural network.
      Methods  First, the harmonic analysis method is used to separate the tidal discharge and residual discharge from the original discharge. Second, a BP neural network is constructed based on error backpropagation algorithm. Then, the BP neural network is used to simulate and train the power flow data and residual current data processed by VMD. Finally, the output tidal discharge and residual discharge components are superimposed and reconstructed to obtain the final discharge prediction results.
      Results  Experiments performed at the Xuliujing section of the Yangtze Estuary show that the discharge prediction accuracy of BP neural network method is better than that of traditional harmonic analysis method, with the root mean square error (RMSE) is decreased by about 3 400 m3/s, and the relative standard accuracy (RSD) is increased by about 6%. In addition, compared with the traditional method, RMSE of the proposed method in this paper is decreased by about 5 000 m3/s, and the RSD is increased by about 9%.
      Conclusions  The combined model based on harmonic analysis and VMD-BP neural network can effectively improve the prediction accuracy of discharge in tidal reach. At the same time, it also provides a new idea for the discharge prediction in the waters with variable flow conditions.

     

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