The study of sea surface temperature (SST) in coastal water is of great significance for navigation, aquaculture and military. Numerous studies have been conducted to predict this parameter in recent years. The fluctuation of SST is periodic, and it shows different changing patterns over different timescales. At present, most investigations on SST ignore the influence of multiscale features on the prediction, which may limit the accuracy of the final prediction. To fully exploit the features of SST data, we propose a multi-long short-term memory convolution neural network (M-LCNN) prediction model. In this model, we use the wavelet transform to decompose and reconstruct the time series, we then predict the variation of SST sequences at multiple scales, and finally complete the prediction process. We conduct experiments in the Yellow Sea and the Bohai Sea in China, and the results indicate that our method is significantly better than traditional approaches.

对沿海水域海表温度（SST）的研究对航海，水产养殖和军事意义重大。近年来，已经进行了许多研究来预测该参数。SST的波动是周期性的，并且在不同的时间范围内显示出不同的变化模式。目前，大多数关于SST的研究都忽略了多尺度特征对预测的影响，这可能会限制最终预测的准确性。为了充分利用SST数据的特征，我们提出了一种多长短期记忆卷积神经网络（M-LCNN）预测模型。在该模型中，我们使用小波变换分解和重建时间序列，然后在多个尺度上预测SST序列的变化，最终完成预测过程。