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The prediction of sea surface temperature has always been the focus of ocean meteorological research. However, existing sea surface temperature prediction methods have limited ability in spatial information extraction of sea surface temperature and also perform poorly in long-term prediction. To address these problems, we propose a multi-point long-term prediction method for sea surface temperature, which is named graph multi-head self-attention neural network (GMSAN). Firstly, GMSAN constructs an adaptive graph neural network, which can adaptively extract the spatial information between points and realize multi-point prediction of sea surface temperature. GMSAN then uses the multi-head self-attention mechanism to extract global dependence point by point from the historical series of sea surface temperature to improve its long-term forecasting ability. We use 35 years of sea surface temperature data from Bohai Sea for the experiment and predict the sea surface temperature in three time windows of 120 days, 180 days and 240 days. The experimental results show that our model can meet the demand of multi-point long-term prediction of sea surface temperature. In addition, compared with the best-performing sea surface temperature prediction model, GMSAN improves the average prediction accuracy by 4.4% at 28 points and 3.3% at 112 points.

A graph multi-head self-attention neural network for the multi-point long-term prediction of sea surface temperature