Sea surface temperature forecasting with ensemble of stacked deep neural networks

Journal Publication ResearchOnline@JCU
Jahanbakht, Mohammad;Xiang, Wei;Rahimi Azghadi, Mostafa
Abstract

Oceanic temperature has a great impact on global climate and worldwide ecosystems, as its anomalies have been shown to have a direct impact on atmospheric anomalies. The major parameter for measuring the thermal energy of oceans is the sea surface temperature (SST). SST prediction plays an essential role in climatology and ocean-related studies. However, SST prediction is challenging due to the involvement of complex and nonlinear sea thermodynamic factors. To address this challenge, we design a novel ensemble of two stacked deep neural networks (DNNs) that uses air temperature, in addition to water temperature, to improve the SST prediction accuracy. To train our model and compare its accuracy with the state-of-the-art, we employ two well-known datasets from the national oceanic and atmospheric administration as well as the international Argo project. Using DNNs, our proposed method is capable of automatically extracting required features from the input time series and utilizing them internally to provide a highly accurate SST prediction that outperforms state-of-the-art models.

Journal

IEEE Geoscience and Remote Sensing Letters

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Volume

19

ISBN/ISSN

1558-0571

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Pages Count

5

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Publisher

IEEE

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DOI

10.1109/LGRS.2021.3098425