基于MASTGCN的AIS信息船舶SO2排放预测模型OA北大核心

Sulfur dioxide(SO2)emissions from ships are a major contributor to air pollution and ocean acidification,exhibiting significant spatial and temporal heterogeneity.Current prediction models for shipborne pollutants have limitations in modeling spatiotemporal dependencies,making it difficult to effectively capture the complex spatio-temporal correlation characteristics in SO2 emissions.To address this issue,based on automatic identification system(AIS)data and Chinese ship registry data,a dynamics-based method combined with emission factor approaches is used to quantify shipborne SO2 emissions during navigation,thereby providing a solid data foundation for subse-quent prediction.In terms of model construction,a multi-head attention spatial-temporal graph convolutional net-work(MASTGCN)is proposed.Based on the spatial-temporal graph convolutional network(STGCN)architecture,MASTGCN incorporates multi-head self-attention mechanisms in both spatial and temporal dimensions.By dynami-cally allocating weights,it enhances the modeling capability to learn spatial dependencies across different regions and temporal dependencies across time intervals,thus improving the accuracy of spatiotemporal predictions for ship-borne SO2 emissions.Experimental results show that when the number of attention heads is set to five,the model achieves a mean absolute error(MAE)of 0.057 5,mean squared error(MSE)of 0.120 6,root mean squared error(RMSE)of 0.347 3,and floating point operations(FLOPs)of 3 030 M.These results demonstrate superior overall performance in both accuracy and efficiency compared to other configurations and the baseline STGCN model.Spe-cifically,MASTGCN with five attention heads outperforms STGCN by improving MAE by 27.6%,MSE by 6.0%,and RMSE by 1.3%.The findings indicate that the incorporation of multi-head attention mechanisms enables the model to effectively capture the spatial characteristics of SO2 emissions through dynamic weighting.The five-head MASTGCN model achieves excellent predictive accuracy while maintaining a relatively reasonable computational complexity.