Sea surface temperature forecasting with ensemble of stacked deep neural networks

Jahanbakht, Mohammad, Xiang, Wei, and Rahimi Azghadi, Mostafa (2021) Sea surface temperature forecasting with ensemble of stacked deep neural networks. IEEE Geoscience and Remote Sensing Letters. (In Press)

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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.

Item ID: 68935
Item Type: Article (Research - C1)
ISSN: 1558-0571
Keywords: Ocean temperature; Atmospheric modeling; Time series analysis; Temperature distribution; Predictive models; Forecasting; Numerical models
Copyright Information: © 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
Funders: Australian Government (AG)
Projects and Grants: AG Research Training Program (RTP) Scholarship
Date Deposited: 18 Aug 2021 01:29
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning @ 20%
41 ENVIRONMENTAL SCIENCES > 4104 Environmental management > 410402 Environmental assessment and monitoring @ 80%
SEO Codes: 19 ENVIRONMENTAL POLICY, CLIMATE CHANGE AND NATURAL HAZARDS > 1901 Adaptation to climate change > 190101 Climate change adaptation measures (excl. ecosystem) @ 100%
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