摘要
为进一步提升潮汐预测精度,提高预测模型的多适应性,针对低频潮汐分潮智能化自适应提取困难、动态化处理分潮信息能力弱、单一预测模型对潮汐整体预测的局限性等问题,提出了一种基于能量熵的自适应最优变分模态分解VMD与门控循环单元神经网络GRU相结合的潮汐预测提升方法。首先,将潮汐数据归一化预处理,通过VMD对潮汐数据完成自适应变分模态分解,并根据不同分解层模态分量的能量熵判定最优分解层数,最后将最优分量标准化后经GRU单独预测合成,通过反归一化形成最终预测数据。经验证分析,在潮汐预测方面,GRU模型比LSTM、BiLSTM模型性能更优,均方根误差分别提升了53%和96.8%,而本文方法与单一GRU模型相比,均方根误差再次提升了81.3%,预测精度提升效果更加明显,对于潮汐分析与预测具有较高的推广应用价值。
To improve the accuracy of tidal prediction further enhance the adaptability of the prediction model,and address a series of problems,including the difficulties of intelligent and adaptive extraction of low-frequency tidal components,weak ability to dynamically process tidal information,limitations of a single prediction model for overall tidal prediction,this paper proposes an improving tidal prediction model based on adaptive optimal variational modal decomposition of energy entropy and GRU recurrent neural networks.Firstly,the tidal data are normalized,and the VMD method is utilized for adaptive variational modal decomposition.Then,the optimal decomposition level is confirmed based on the energy entropy of the components.Finally,each component of the optimal decomposition is standardized and separately predicted and synthesized by GRU.The final prediction data are formed through reverse normalization.Through verification and analysis,compared with LSTM and BiLSTM models,the GRU model has better performance in terms of tidal prediction.The RMSE values are increased by 53%and 96.8%,respectively.However,compared with a single GRU model,the proposed prediction model has RMSE increase 81.3%again,and the accuracy improvement effect is more obvious.The method in this paper has high promotion and application value for tidal analysis and prediction.
作者
赵杰
解则晓
刘世萱
Zhao Jie;Xie Zexiao;Liu Shixuan(College of Engineering,Ocean University of China,Qingdao 266404,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2023年第12期79-87,共9页
Chinese Journal of Scientific Instrument
基金
国家重点研发计划(2022YFC3104200)项目资助。
