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In this phase, 3D CNN model is used to extract the spatial features among the SST field data at the same time through two convolution and pooling operations. Look at this formula. The input of the 3D CNN is a 5-deminsional tensor expressed as 𝑩Ąż𝑭Ąż𝑳Ąż𝑾Ąż𝑪, where B is the number of training samples in a batch, F is the length of SST field series data. L and W are the length and width of the SST field. C is the channel information of the model, the value of C here is 1. In the following experiments, both length and width of SST field are set to 8, that means values of L and W are set to 8, and the numbers of features on past time is obtained by sliding the window. For example, if the SST is predicted by past 4 days SST data, then the sequence length is 4, in addition, 4 long-period time features extracted by XGBoost, the length of the time series is 8, i.e. the value of F. Finally, the data from each window are combined to obtain a 5-dimensional tensor as an input to the next model 3D CNN. The whole process is shown as figure blow. The output of 3D CNN model is the input of the next model LSTM.
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IEEE/ICACT20210141 Slide.10
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