基于调和分析及VMD-LSTM混合模型的甬江河口水位预报方法  

Water level forecasting method for the Yongjiang River Estuary based on harmonic analysis and VMD-LSTM hybrid model

作　　者：陈永平[1,2] 韩韬 邱超 甘敏 谭亚[1,2] 王瑾琪 CHEN Yongping;HAN Tao;QIU Chao;GAN Min;TAN Ya;WANG Jinqi(The National Key Laboratory of Water Disaster Prevention,Hohai University,Nanjing 210098,China;College of Harbour,Coastal and Offshore Engineering,Hohai University,Nanjing 210098,China;Hydrological Management Center of Zhejiang Province,Hangzhou 310009,China;State Key Laboratory of Lake Science and Environment,Nanjing Institute of Geography and Limnology,Chinese Academy of Sciences,Nanjing 210008,China)

机构地区：[1]河海大学水灾害防御全国重点实验室,江苏南京210098 [2]河海大学港口海岸与近海工程学院,江苏南京210098 [3]浙江省水文管理中心,浙江杭州310009 [4]中国科学院南京地理与湖泊研究所,江苏南京210008

出　　处：《河海大学学报(自然科学版)》2025年第2期1-10,共10页Journal of Hohai University(Natural Sciences)

基　　金：国家重点研发计划项目(2023YFC3008100);浙江省水利科技重大项目(RA2202)。

摘　　要：为解决甬江感潮河段潮位预报总体精度偏低的问题,构建了一种基于经典调和分析(T_TIDE)、变分模态分解(VMD)和长短时记忆神经网络(LSTM)的混合模型(VMD-LSTM混合模型)。VMD-LSTM混合模型采用T_TIDE程序包对甬江河口逐时水位数据进行回报(即潮位),用实测水位减去潮位得到相应余水位,并采用VMD模型将余水位分解为13个本征模函数(IMF),依次对应D0~D12潮族,采用LSTM模型分别训练余水位的各个IMF分量和潮位并分别向后预报12~48h,各个IMF分量和潮位的预报值之和即为河口水位的预测值。结果表明:VMD模型可对甬江河口余水位中D0~D12潮族波动进行完全分离;VMD-LSTM混合模型12、24、36、48h短期水位预报的均方根误差(RMSE)比LSTM模型最多分别降低了0.15、0.13、0.16、0.16m;VMD-LSTM混合模型在D0、D2潮族频带的误差修正最明显,相比LSTM模型,可分别将D0、D2潮族的谱峰预报误差最多降低0.05、0.04m·d^(0.5)。Abstract:To address the challenge of achieving high accuracy in tidal level forecasting along the tidal reach of the Yongjiang River Estuary,this study introduces a hybrid model(VMD-LSTM hybrid model)that integrates classical harmonic analysis(T_TIDE),variational mode decomposition(VMD),and long short-term memory network(LSTM).The VMD-LSTM hybrid model utilizes the T_TIDE package to obtain the hourly water level data of the estuary.The tide-simulated water levels are calculated,and the corresponding residual water levels are derived by subtracting tidal levels from the measured data at each station.The VMD model is employed to decompose the residual water levels into 13 Intrinsic Mode Functions(IMFs),specifically IMF0 through IMF12,which correspond to the D0 to D12 tidal species in sequence.LSTM-based regression is performed on each IMF component and tide level of the residual water levels for step-by-step prediction over a forecast horizon ranging from 12 to 48 hours.The sum of the predicted values of each IMF component and tide level is the predicted value of the estuarine water level.The results showed that:the VMD model can completely separate the D0 to D12 tidal constituent fluctuations in the residual water level for the Yongjiang River Estuary;the root mean square error(RMSE)of the VMD-LSTM hybrid model for short-term water level forecasting at 12 hours,24 hours,36 hours,and 48 hours was reduced by at most 0.15,0.13,0.16,and 0.16 m,respectively,compared to the LSTM model.In addition,the hybrid model of VMD-LSTM demonstrates the most significant capability for error correction in the D0 and D2 tidal bands.Compared to the LSTM model,this approach can reduce the spectral peak prediction errors of these tidal bands by up to 0.05,0.04 m·d^(0.5),respectively.

关 键 词：甬江口 河口潮汐 变分模态分解 LSTM模型 调和分析 水位预报 

分 类 号：P338[天文地球—水文科学]