一种基于深度学习的海表温度混合预测方法

海表温度（SST）是海洋水文的重要参数，准确预测SST对海洋经济发展与极端天气的预防都有重大意义。首先，针对SST序列数据的多噪声特点，采用变分模态分解方法（VMD）预处理，以减少噪声对预测结果的影响。其次，将卷积神经网络（CNN）与长短时记忆网络（LSTM）结合，同时提取SST序列的空间与时间特征，以提高预测精度。最后，本文提出了一种基于深度学习并融合了去噪模块的SST预测模型，选取我国东海海域的SST进行实证研究。通过与基线模型、现有模型的对比，证明了本文模型不但在SST的预测精度方面提升明显，而且具有较好的鲁棒性。

A hybrid sea surface temperature predicting method based on deep learning

Sea surface temperature (SST) is an important parameter of ocean hydrology, and accurate prediction of SST is of great significance for ocean economic development and extreme weather prevention. For the characteristics of SST series with multiple noises, variational model decomposition (VMD) is used to pre-process the SST series and reduce the influence of noise on the prediction results. Furthermore, the convolutional neural network (CNN) is combined with the long short-term memory network (LSTM) extracting both spatial and temporal features in SST sequences to improve the prediction accuracy. Finally, a SST prediction model based on deep learning with incorporating the denoising module is proposed in this paper. The SST of China's East China Sea waters is selected for empirical study. Through comparison and analysis with the baseline models and existing models, it is proved that the model in this paper not only improves the SST prediction accuracy significantly, but also has better robustness.