Time space hybrid network XLTNET: A high-precision current velocity prediction model targeting local sea areas in the South Pacific

Hu Nanxing; Yuan Hongchun

doi:10.12284/hyxb20250105

Volume 47 Issue 12

Dec. 2025

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Hu Nanxing，Yuan Hongchun. Time space hybrid network XLTNET: A high-precision current velocity prediction model targeting local sea areas in the South Pacific[J]. Haiyang Xuebao，2025, 47(12)：165–184 doi: 10.12284/hyxb20250105

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Time space hybrid network XLTNET: A high-precision current velocity prediction model targeting local sea areas in the South Pacific

doi: 10.12284/hyxb20250105

Hu Nanxing^1
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Yuan Hongchun^{1
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College of Information Technology, Shanghai Ocean University, Shanghai 201306, China

Received Date: 2025-06-25
Rev Recd Date: 2025-10-29
Publish Date: 2025-12-31

Abstract

Abstract

Accurate long-term forecasting of ocean current fluid is crucial for marine science research, yet existing deep learning models generally suffer from error accumulation and insufficient long-term stability when processing high-dimensional spatiotemporal sequences. To address this challenge, this study proposes an innovative spatiotemporal fusion network, XLTNET. The model is based on an encoder-decoder architecture, with its core lying in the efficient fusion of two key modules: an improved Swin Transformer that adopts the K-Nearest Neighbors (KNN) sparse self-attention mechanism for precisely capturing multi-scale spatial dynamics, and an extended Long Short-Term Memory network (xLSTM) for enhancing the modeling of long-range temporal dependencies. Experiments were conducted based on the reanalysis dataset from the Copernicus Marine Service, utilizing five ocean elements including ocean current fluid (U and V components), temperature, salinity, and height. The results demonstrate that XLTNET exhibits superior performance and stability in long-term forecasting tasks. In the 15-day forecast, XLTNET was the only model to maintain an R-value above 0.7 in both flow directions. Its U-direction R-value improved by 7.3%, 18.0%, and 20.7% compared to ASTMEN, ConvLSTM, and LSTM, respectively, while its V-direction R-value showed improvements of 8.7%, 15.6%, and 17.4%. Furthermore, ablation studies confirmed the necessity of each model component and the deep fusion strategy. This research provides a high-performance solution for high-precision, long-term ocean current fluid forecasting.
- South Pacific,
- ocean current fluid prediction,
- Swin Transformer,
- spatiotemporal fusion,
- deep learning,
- xLSTM

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References

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