Graph convolutional network-assisted SST and Chl-a prediction with multicharacteristics modeling of spatio-temporal evolution

Abstract

Changes in oceanic variables, such as sea surface temperature (SST) and chlorophyll-a (Chl-a), have important implications for marine ecosystems and global climate change. The deep learning (DL) methods relying on convolutional neural networks can be employed to extract the spatial correlation for the prediction of oceanic variables. However, these methods are inflexible in the cases where some regions, e.g., land and islands, are invalid for the prediction of oceanic variables. By contrast, the graph convolutional network (GCN) is capable of capturing the large-scale spatial dependency existing in the irregular data. Owing to this, in this article, we propose a GCN-based method for the prediction of oceanic variables, including SST and Chl-a, with high accuracy, which is referred to as OVPGCN. The proposed OVPGCN consists of three modules aiming to fully extract the spatial correlation and temporal dependency via modeling the multicharacteristics of the spatio-temporal dynamic evolution. In particular, three modules are implemented to extract the stationary and nonstationary variations in the recent spatio-temporal sequences, the spatial differences between different sites, and the periodic features in historical data, respectively. The well-designed OVPGCN is applied to the monthly SST and Chl-a prediction in the Bohai Sea and the Northern South China Sea (NSCS). The performance demonstrates that the proposed OVPGCN is highly effective and enables to achieve much higher prediction accuracy than the state-of-the-art methods.

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Identifiers

ORCID for Lie-Liang Yang: orcid.org/0000-0002-2032-9327

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