Researchers have developed a novel method for forecasting sea surface temperature (SST) by leveraging large language models (LLMs) combined with spatial graphs. This approach integrates historical SST data, environmental records, and ocean knowledge into a textual context, while dynamic and static spatial graphs capture recent correlations and geographical relationships. The system demonstrated superior performance in forecasting SST in the South China Sea, achieving the best Mean Absolute Error (MAE) and R-squared ($\Rtwo$) across ten forecast steps. Additionally, it provides source-linked contextual explanations for its predictions by matching trends with knowledge entries. AI
IMPACT This research demonstrates a novel application of LLMs and graph neural networks for complex environmental forecasting, potentially improving climate modeling and prediction accuracy.
RANK_REASON The item is an academic paper detailing a new methodology for a scientific forecasting task. [lever_c_demoted from research: ic=1 ai=1.0]
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