Prediction of sea surface temperature using a multiscale deep combination neural network

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.