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IEEE/ICACT20210141 Slide.08        [Big Slide]	Oral Presentation
	Our prediction approach is a combined prediction method of 3DCNN, LSTM and attention mechanism£¬ which called 3DCNN-LSTM-AT. It not only takes into account the spatial correlation of SST field data in the same time, but also consider the time dependence among SST field data at different time, it also uses attention mechanism to weight the output of each step of LSTM to improve the prediction results. Thus, it can predict the medium-term and long-term SST changes more accurately. The framework of 3DCNN-LSTM-AT approach is shown as figure blow, which is mainly divided into four stages, that are long period time features extraction, spatial features extraction, temporal dependency learning, weighting and output results

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