Sequence prediction of missing data of spatio-temporal series of marine environmental elements based on ST-ResNet model


  • Yuanfeng Zhong



Marine environmental elements, Deep learning, ST-ResNet model


Spatial data of marine environmental elements are of great significance to the marine industry and marine safety, and they are also affected by external factors. This results in a new method for spatial and temporal sequence prediction of marine environmental elements based on the ST-ResNet model. Specifically, the temporal distance, period, and trend of marine regions are modelled by residual networks, and different residual sub-networks are used for each characteristic, in each sub-network, different weights are assigned to different branches and regions by simulating the changes of marine data, and the output is finally fitted to obtain the output. The ST-ResNet model was validated on the ERA5 dataset as an example, and compared with other prediction models, the ST-ResNet model achieved better results, which verified the effectiveness of the method in predicting the temporal and spatial sequences of marine environmental elements.


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How to Cite

Zhong, Y. (2024). Sequence prediction of missing data of spatio-temporal series of marine environmental elements based on ST-ResNet model. Journal of Computing and Electronic Information Management, 12(2), 76-80.

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