SICFormer: A 3D-Swin Transformer for Sea Ice Concentration Prediction

• Published in: Journal of Marine Science and Engineering
• Main Authors: Zhuoqing Jiang, Bing Guo, Huihui Zhao, Yangming Jiang, Yi Sun
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2024
• Subjects: spatiotemporal prediction; 3D-Swin Transformer; sea ice concentration; attention mechanisms; Naval architecture. Shipbuilding. Marine engineering; VM1-989; Oceanography; GC1-1581; National Snow and Ice Data Center; Sea ice
• Online Access: https://doi.org/10.3390/jmse12081424; https://doaj.org/article/8335f7acb28e4b0087f63131e39ec9fe

Sea ice concentration (SIC) is an important dimension for characterising the geographical features of the pan-Arctic region. Trends in SIC bring new opportunities for human activities in the Arctic region. In this paper, we propose a deep learning technology-based sea ice concentration prediction model, SICFormer, which can realise end-to-end daily sea ice concentration prediction. Specifically, the model uses a 3D-Swin Transformer as an encoder and designs a decoder to reconstruct the predicted image based on PixelShuffle. This is a new model architecture that we have proposed. Single-day SIC data from the National Snow and Ice Data Center (NSIDC) for the years 2006 to 2022 are utilised. The results of 8-day short-term prediction experiments show that the average Mean Absolute Error (MAE) of the SICFormer model on the test set over the 5 years is 1.89%, the Root Mean Squared Error (RMSE) is 5.99%, the Mean Absolute Percentage Error (MAPE) is 4.32%, and the Nash–Sutcliffe Efficiency (NSE) is 0.98. Furthermore, the current popular deep learning models for spatio-temporal prediction are employed as a point of comparison given their proven efficacy on numerous public datasets. The comparison experiments show that the SICFormer model achieves the best overall performance.