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Prediction of Sea Surface Temperature Using Long Short-Term Memory
Open accessAbstract
This letter adopts long short-term memory (LSTM) to predict sea surface temperature (SST), and makes short-term prediction, including one day and three days, and long-term prediction, including weekly mean and monthly mean. The SST prediction problem is formulated as a time series regression problem. The proposed network architecture is composed of two kinds of layers: an LSTM layer and a full-connected dense layer. The LSTM layer is utilized to model the time series relationship. The full-connected layer is utilized to map the output of the LSTM layer to a final prediction. The optimal setting of this architecture is explored by experiments and the accuracy of coastal seas of China is reported to confirm the effectiveness of the proposed method. The prediction accuracy is also tested on the SST anomaly data. In addition, the model's online updated characteristics are presented.
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Temporal Convolutional Networks for the Advance Prediction of ENSO
Yan, J., Mu, L., Wang, L., Ranjan, R., & Zomaya, A. Y. (2020) · Scientific Reports · doi:10.1038/s41598-020-65070-5 · Cited by 298
Metadata
- Authors
- Zhang, Q., Wang, H., Dong, J., Zhong, G., & Sun, X.
- Venue
- IEEE Geoscience and Remote Sensing Letters
- Published
- 2017-10
- Volume
- 14
- Issue
- 10
- Pages
- 1745-1749
- ISSN
- 1545-598X (ISSN-L)
- 1558-0571
- OpenAlex
- W2619995677
- Cited by
- 482
- References
- 12
- FWCI
- 15.36
- Type
- journal article
- Language
- en
- Open access
- green
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