Abstract
Accurate prediction of sea surface temperature (SST) is extremely important for forecasting oceanic environmental events and for ocean studies. However, the existing SST prediction methods do not consider the seasonal periodicity and abnormal fluctuation characteristics of SST or the importance of historical SST data from different times; thus, these methods suffer from low prediction accuracy. To solve this problem, we comprehensively consider the effects of seasonal periodicity and abnormal fluctuation characteristics of SST data, as well as the influence of historical data in different periods, on prediction accuracy. We propose a novel ensemble learning approach that combines the Predictive Recurrent Neural Network(PredRNN) network and an attention mechanism for effective SST field prediction. In this approach, the XGBoost model is used to learn the long-period fluctuation law of SST and to extract seasonal periodic features from SST data. The exponential smoothing method is used to mitigate the impact of severely abnormal SST fluctuations and extract the a priori features of SST data. The outputs of the two aforementioned models and the original SST data are stacked and used as inputs for the next model, the PredRNN network. PredRNN is the most recently developed spatiotemporal deep learning network, which simulates both spatial and temporal representations and is capable of transferring memory across layers and time steps. Therefore, we used it to extract the spatiotemporal correlations of SST data and predict future SSTs. Finally, an attention mechanism is added to capture the importance of different historical SST data, weigh the output of each step of the PredRNN network, and improve the prediction accuracy. The experimental results on two ocean datasets confirm that the proposed approach achieves higher training efficiency and prediction accuracy than the existing SST field prediction approaches do.
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Acknowledgements
This work was supported by the National Key R&D Program of China (2016YFC1401900) and the National Natural Science Foundation of China (Grant Nos. 61872072 and 61073063).
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Baiyou Qiao is an Associate Professor in Northeastern University, China. He is a member of China Computer Federation. His main research interests include cloud computing, big data mining and analysis, spatial temporal data management.
Zhongqiang Wu is a postgraduate student of Computer Technology in Northeastern University, China. His main research interests include data mining and time series analysis and prediction.
Ling Ma is a postgraduate student of Computer Science and Technology in Northeastern University, China. He received his BS degree from Northeast Normal University, China in June 2020. His main research interests are the ocean big data analysis and prediction.
Yicheng Zhou is a graduate student of computer and technology in Northeastern University, China. His main research interests include big data analysis and machine learning.
Yunjiao Sun is a professor in Northeastern University, China. He is a member of China Computer Federation. His main research interests include cloud computing, big data mining and analysis, machine learning.
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Qiao, B., Wu, Z., Ma, L. et al. Effective ensemble learning approach for SST field prediction using attention-based PredRNN. Front. Comput. Sci. 17, 171601 (2023). https://doi.org/10.1007/s11704-021-1080-7
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DOI: https://doi.org/10.1007/s11704-021-1080-7