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Spatial-temporal attention network for multistep-ahead forecasting of chlorophyll

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Abstract

The multistep-ahead prediction of chlorophyll provides an effective means for early warning of red tide. However, since multistep-ahead forecasting presents challenges, such as vague interactive relationships among ocean factors, long-term dependence modeling, and accumulative errors, existing methods mostly concentrate on the current time or one-step-ahead forecasting. In this paper, a hierarchical multistep-ahead forecasting model spatial-temporal attention network(STAN), which integrates the spatial context extractor network(SCE-net), long short-term memory network(LSTM), and the temporal attention mechanism, is proposed for the prediction of chlorophyll. In STAN, the input layer utilizes SCE-net to excavate relationships among ocean factors and generate high-level semantic via embedding factors into a continuous low-dimensional space. The middle layer applies LSTM to build the long-term dependencies of corresponding semantic representations. The output layer uses another LSTM with temporal attention to reduce accumulative errors and maintain temporal continuity. The attention can assign different weights to the middle layer’s hidden state and generate a context vector. Then the context vector and the final predicted value are considered as the current input for better forecasting. The buoy observation data of the Xiamen coastal area monitored in 2009–2011 is used to verify the efficiency of STAN. Experimental results prove that STAN outperforms the state-of-the-art methods of multistep-ahead prediction. When using 7 observation steps to forecast 15 steps, the MAPE of STAN is 0.3209, and the MAE is 0.1 lower than the values of the baselines approaches.

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Acknowledgments

This work is supported by National Program on Key Research Project of China (2016YFC1401900) and Open fund of the Key Laboratory of Digital Ocean, State Oceanic Administration, China (B201801030).

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Authors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China

    Xiaoyu He, Xiulin Geng & Lingyu Xu

  2. Key Laboratory of Digital Ocean, National Marine Data and Information Service, Tianjin, 300171, China

    Suixiang Shi

  3. School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, Inner Mongolia, China

    Xiaolin Zhang

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  1. Xiaoyu He
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  3. Xiulin Geng
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  4. Lingyu Xu
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Correspondence to Lingyu Xu.

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He, X., Shi, S., Geng, X. et al. Spatial-temporal attention network for multistep-ahead forecasting of chlorophyll. Appl Intell 51, 4381–4393 (2021). https://doi.org/10.1007/s10489-020-02143-y

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