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 de⁃composition,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3400 m^(3)/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5000 m^(3)/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。
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 3400 m^(3)/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 5000 m^(3)/s,and the RSD is in⁃creased 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.
作者
陈志高
吴子豪
班亚
陈志平
CHEN Zhigao;WU Zihao;BAN Ya;CHEN Zhiping(Key Laboratory of Mine Environmental Monitoring and Improving Around Poyang Lake of Ministry of Natural Resources,East China University of Technology,Nanchang 330013,China;School of Surveying and Geoinformation Engineering,East China University of Technology,Nanchang 330013,China;Chongqing Academy of Metrology and Quality Inspection,Chongqing 401100,China)
出处
《武汉大学学报（信息科学版）》
EI
CAS
CSCD
北大核心
2023年第8期1389-1397,共9页
Geomatics and Information Science of Wuhan University
基金
国家自然科学基金(42266006,41806114,41904031)
江西省自然科学基金(20202ACBL214019,20181BAB216031,20202BABL213033)。
关键词
流量预报
调和分析
变分模态分解
BP神经网络
感潮河段
长江口
discharge prediction
harmonic analysis
variational model decomposition
BP neural net⁃work
tidal reach
the Yangtze Estuary
