Accurate prediction of sea surface temperature (SST) is of high importance in marine science, benefiting applications ranging from ecosystem protection to extreme weather forecasting and climate analysis. Wide-area SS...
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Accurate prediction of sea surface temperature (SST) is of high importance in marine science, benefiting applications ranging from ecosystem protection to extreme weather forecasting and climate analysis. Wide-area SST usually shows diverse SST patterns in different sea areas due to the changes of temperature zones and the dynamics of ocean currents. However, existing studies on SST prediction often focus on small-area predictions and lack the consideration of diverse SST patterns. Furthermore, SST shows an annual periodicity, but the periodicity is not strictly adherent to an annual cycle. Existing SST prediction methods struggle to adapt to this non-strict periodicity. To address these two issues, we proposed the Cross-Region Graph Convolutional Network with Periodicity Shift Adaptation (RGCN-PSA) model which is equipped with the Cross-Region Graph Convolutional Network module and the Periodicity Shift Adaption module. The Cross-Region Graph Convolutional Network module enhances wide-area SST prediction by learning and incorporating diverse SST patterns. Meanwhile, the periodicity Shift Adaptation module accounts for the annual periodicity and enable the model to adapt to the possible temporal shift automatically. We conduct experiments on two real-world SST datasets, and the results demonstrate that our RGCN-PSA model obviously outperforms baseline models in terms of prediction accuracy. The code of RGCN-PSA model is available at https://***/ADMIS-TONGJI/RGCN-PSA/.
In this paper, we develop spectral collocation method for a class of fractional diffusion differential equations. Since the solutions of these fractional differential equations usually exhibit singularities at the end...
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