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IEEE/ICACT20210141 Question.1 Questioner: kobiljonikromjanov@gmail.com 2021-02-09 오후 8:00:22	IEEE/ICACT20210141 Answer.1 Answer by Auhor qiaobaiyou@mail.neu.edu.cn 2021-02-09 오후 8:00:22
Thank you very much for submitting your research article and welcome to IEEE/ ICACT2021. Can you please explain your contribution in this article?	Our contribution can be summarized as follows: 1.We regarded SST field prediction as a spatiotemporal sequence forecasting problem and solved it using a CNN3D-LSTM model for the first time. 2. Based on the seasonal characteristics of the SST data, we proposed an ensemble learning prediction method, which use machine learning models to learn seasonal features, spatiotemporal model to learn the spatiotemporal features. 3. For the long-term SST prediction task, we introduce a custom attention layer behind the LSTM to adjust the finally lstm output and obtain better prediction results. 4. We have evaluated the proposed learning method on SST data and demonstrated its effectiveness.

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