基于残差U型卷积网络的海面风速多步时空预测
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作者:
作者单位:

1.武汉理工大学 自动化学院;2.武汉第二船舶设计研究所第五研究室

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中图分类号:

TP399

基金项目:

高技术船舶专项课题,工信部装函(2019)331 号


Multi-step sea surface wind speed spatio-temporal prediction based on residual UNet
Author:
Affiliation:

1.School of Automation, Wuhan University of Technology;2.The Fifth Research Office of Wuhan Second Ship Design Research Institute

Fund Project:

Special project of high-tech ships, Ministry of industry and information technology (2019) No. 331

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    摘要:

    准确的海面风速预测是保证远洋船舶航行安全和节能减排的重要条件. 针对远洋航行领域的海面风速预 测存在空间特征难以解析和多步预测精度偏低两个问题,设计了一种改进的多步时空预测方法. 在多步预测方 面, 使用超前时刻策略使单个模型学习并区分不同的预测时刻, 并将海面风向作为外生变量, 将月份、日期和时 刻作为协变量, 与历史风速数据结合以扩展样本空间. 在空间特征方面, 利用编码器-解码器结构的残差U型卷积 神经网络, 对多层级空间信息进行提取和解析, 并将超前时刻特征同时输入编码器和解码器, 强化了深层特征解 析为对应预测时刻的效果. 在全球原油运输路线上进行的12小时预测实验表明, 本文所提出的方法较其他6种预 测方法具有更低的预测误差.

    Abstract:

    Accurate sea surface wind speed prediction is an important condition to ensure the navigation safety and energy conservation of ocean ships. In terms of the two problems of difficult spatial feature decoding and low multi-step prediction accuracy of sea surface wind speed predicition in the field of ocean navigation, we design an improved multi-step spatio-temporal prediction method. For the problem of multi-step prediction, we use the lead time strategy to achieve that an individual model learns and distinguishs the data at multiple prediction time. We take the sea surface wind direction as an exogenous variable, take the month, date and time as covariant variables, and combine them with the historical wind speed data to expand sample space. For the problem of spatial features, the residual U-shaped convolutional network with encoder-decoder structure is used to extract and decode the multi-level spatial information, and the lead time features are input into the encoder and decoder at the same time, which strengthens the effect of decoding the deep features into the corresponding prediction time. The 12-hour prediction experiments on global major oil transportation routes show that the proposed method has lower prediction error than the other six prediction methods.

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  • 收稿日期:2022-01-05
  • 最后修改日期:2022-02-22
  • 录用日期:2022-02-25
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