STDNet: Spatio-Temporal Decompose Network for Predicting Arctic Sea Ice Concentration - Xu Zhu; Jing Wang; Guojun Wang; Yangming Jiang; Yi Sun; Huihui Zhao (Remote Sensing)
In the context of global warming, the accurate prediction of Arctic Sea Ice Concentration (SIC) is crucial for the development of Arctic shipping routes. We have therefore constructed a lightweight, non-recursive spatio-temporal prediction model, the Spatio-Temporal Decomposition Network (STDNet), to predict the daily SIC in the Arctic. The model is based on the Seasonal and Trend decomposition using Loess (STL) decomposition idea to decompose the model into trend and seasonal components. In addition, we have designed the Global Sparse Attention Module (GSAM) to help the model extract global information. STDNet not only extracts seasonal signals and trend information with periodical correspondence from the data but also obtains the spatio-temporal dependence features in the data. The experimental methodology involves predicting the next 10 days based on the first 10 days of data. The prediction results provided the following metrics for the 10-day forecast of STDNet: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and coefficient of determination of 1.988%, 3.541%, 5.843%, and 0.979, respectively. The average Binary Accuracy (BACC) at the beginning of September for the period 2018–2022 reached 93.85%. The proposed STDNet model outperforms and is lighter than existing deep-learning-based SIC prediction models.