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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >An Adaptive Scale Sea Surface Temperature Predicting Method Based on Deep Learning With Attention Mechanism
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An Adaptive Scale Sea Surface Temperature Predicting Method Based on Deep Learning With Attention Mechanism

机译：基于深度学习的自适应鳞片表面温度预测方法

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摘要

Sea surface temperature (SST) prediction plays an important role in ocean-related fields. It is challenging due to the nonlinear temporal dynamics with changing complex factors and the inherent difficulties in long-scale predictions. Conventional models often lack efficient information extraction and cannot meet the requirements of long-scale predictions. Therefore, the gate recurrent unit (GRU) encoder-decoder with SST codes and dynamic influence link (DIL), GRU encoder-decoder (GED), which considered both the static and dynamic influence, is proposed in this letter. Each SST code, capturing the static information more effectively, was computed by all hidden states of the encoder and was individually associated with each predicted SST. The DIL, capturing the dynamic influence, connected the SST code with the early predicted future SST for solving the long-scale dependence problem. GED was tested on the Bohai Sea SST data sets and South China Sea SST data sets and compared with full-connected long-short term memory (FC-LSTM) and support vector regression. The results demonstrated that GED outperformed others on different prediction scales and different prediction terms (daily, weekly, and monthly), especially in terms of long-scale and long-term predictions. In addition, attention relationships between historical and future SSTs were further explored, and there was a meaningful finding that each future daily mean SST of Bohai Sea most strongly correlated with the past 27th to 29th historical values.
机译：海表面温度（SST）预测在海洋相关领域发挥着重要作用。由于非线性时间动态与改变复杂因素的非线性时间动态以及长尺预测中固有的困难是挑战。常规模型通常缺乏有效的信息提取，无法满足长期预测的要求。因此，在这封信中提出了具有SST代码和动态影响链路（DIM），GRU编码器解码器（GED）的栅极复制单元（GRU）编码器解码器，其考虑静态和动态影响。每个SST代码更有效地捕获静态信息，由编码器的所有隐藏状态计算，并且与每个预测的SST单独关联。 DIL，捕获动态影响力，与早期预测的未来SST连接了SST代码，以解决长尺寸依赖性问题。 GED在渤海SST数据集和南海SST数据集上进行了测试，并与全连接的长期内存（FC-LSTM）进行比较并支持向量回归。结果表明，GED在不同的预测尺度和不同预测术语（每日，每周和每月）上表现出其他的其他人，特别是在长期和长期预测方面。此外，历史和未来SST之间的关注关系得到了进一步探索的，并且有一个有意义的发现，每个未来每天都意味着渤海的SST与过去27日至第29届历史价值最强烈相关。

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