Prediction of Sea Surface Temperature Using Long Short-Term Memory 论文

详细信息

发表期刊/会议

IEEE Geoscience and Remote Sensing Letters

发表日期

2017-08-11

发表年份

2017

摘要

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.