基于优化深度学习的有效波高双通道混合预测模型
作者单位:

南京信息工程大学

基金项目:

国家自然科学基金(62076136)


Significant wave height two-channel hybrid prediction model based on optimized deep learning
Author:
Affiliation:

Nanjing University of Information Science and Technology

Fund Project:

National Natural Science Foundation of China(62076136)

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    摘要:

    有效波高(Significant Wave Height, SWH)具有复杂的非线性动态特性,这使得对其精确预测成为一大挑战。时频分解技术是处理复杂非线性的有效手段,但现有方法未考虑SWH分解后分量的不同时频特性。因此,本文利用排列熵将集成经验模态分解(Ensemble Empirical Mode Decomposition, EEMD)后得到的SWH分量分为高、低频两类,根据其各自特性构建优化的长短时记忆网络-时间卷积网络(Long Short-Term Memory-Temporal Convolutional Network, LSTM-TCN)双通道时间特征提取模块。并考虑到不同分量预测值对最终SWH预测结果的影响不同,引入贝叶斯模型平均法(Bayesian Model Averaging, BMA)进行权重分配。最终,本文提出了基于优化深度学习的SWH双通道混合预测模型。实验结果表明,与现有最先进的模型相比,该模型在1、3、6、12小时的SWH预测中,评价指标RMSE、MAE和MAPE显著降低,具备较好的精度和稳定性。

    Abstract:

    Significant Wave Height (SWH) has complex nonlinear dynamic properties, which makes its accurate prediction a major challenge. Time-frequency decomposition technology is an effective means to deal with complex nonlinearities. However, existing methods do not consider the different time-frequency characteristics of the decomposed components of SWH. Therefore, this paper uses permutation entropy to divide the SWH components obtained after integrated empirical mode decomposition (EEMD) into high and low frequency categories, and constructs an optimized long short-term memory network - temporal convolutional network LSTM-TCN) based on their respective characteristics. Dual-channel temporal feature extraction module. Considering that different component prediction values have different impacts on the final SWH prediction result, the Bayesian average method (BMA) is introduced for weight allocation. Finally, this paper proposes a SWH dual-channel hybrid prediction model based on optimized deep learning. Experimental results show that compared with the existing state-of-the-art models, the evaluation indicators RMSE, MAE and MAPE of this model are significantly reduced in 1, 3, 6, and 12-hour SWH predictions, and it has better accuracy and stability.

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赵芮晗,闫加宁,韩莹.基于优化深度学习的有效波高双通道混合预测模型[J].南京信息工程大学学报,,():

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历史
  • 收稿日期:2024-10-18
  • 最后修改日期:2025-02-22
  • 录用日期:2025-02-26

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