基于优化深度学习的有效波高双通道混合预测模型OA

Significant Wave Height(SWH)exhibits complex nonlinear dynamic properties,posing significant chal-lenges to its accurate prediction.Time-frequency decomposition is an effective approach to deal with such nonlinear-ities.However,existing methods fail to account for the distinct time-frequency characteristics of the SWH's decom-posed components.Here,we employ permutation entropy to categorize SWH components,which are obtained via En-semble Empirical Mode Decomposition(EEMD),into high-and low-frequency groups,and construct an optimized Long Short-Term Memory-Temporal Convolutional Network(LSTM-TCN)based on their respective characteristics,forming a dual-channel temporal feature extraction module.Furthermore,since different component predictions con-tribute unequally to the final SWH prediction result,the Bayesian Model Averaging(BMA)is introduced to assign adaptive weights.Finally,this paper proposes a dual-channel hybrid prediction model for SWH leveraging optimized deep learning.Experimental results show that,compared to state-of-the-art models,the proposed model achieves sig-nificant reductions in RMSE,MAE and MAPE across 1-,3-,6-,and 12-hour SWH predictions,with enhanced accu-racy and stability.