A cuboid attention-based IOD prediction model

Zheng Mengke; Fang Wei

doi:10.12284/hyxb20250127

Volume 47 Issue 12

Dec. 2025

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Zheng Mengke，Fang Wei. A cuboid attention-based IOD prediction model[J]. Haiyang Xuebao，2025, 47(12)：126–135 doi: 10.12284/hyxb20250127

Citation:

Zheng Mengke，Fang Wei. A cuboid attention-based IOD prediction model[J]. Haiyang Xuebao，2025, 47(12)：126–135 doi: 10.12284/hyxb20250127

Citation:

Zheng Mengke，Fang Wei. A cuboid attention-based IOD prediction model[J]. Haiyang Xuebao，2025, 47(12)：126–135 doi: 10.12284/hyxb20250127

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A cuboid attention-based IOD prediction model

doi: 10.12284/hyxb20250127

Zheng Mengke^{1, 2
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Fang Wei^{1, 3, 4
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,}

1.
School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.
School of Electronic and Information Engineering, Anhui Jianzhu University, Hefei 230601, China
3.
Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
4.
Department of Informatics, King’s college London, WC2B 4BG, UK

Received Date: 2025-09-13
Rev Recd Date: 2025-12-12

Available Online: 2025-08-21

Publish Date: 2025-12-31

Abstract

Abstract

The Indian Ocean Dipole (IOD) is the dominant climate mode in the tropical Indian Ocean, characterized by an east–west dipole pattern in sea-surface temperature anomalies that exerts substantial influence on regional and global climate variability. Current IOD forecasting methods predominantly rely on multivariate coupled or traditional statistical models, which pose significant challenges such as high computational complexity and multivariate noise interference. To address these challenges, this study proposes a cuboid attention-based IOD prediction model(CAIPM). The model takes sea surface temperature anomalies as the sole input variable, incorporates a Spatio-Temporal Gradient Enhancement Module (STGEM), which integrates sliding windows, temporal difference, and spatial convolution operations to enhance spatiotemporal feature extraction. By effectively capturing spatiotemporal dependencies within the Sea Surface Temperature Anomaly (SSTA) field through a cuboid attention mechanism, it directly outputs future spatiotemporal SSTA predictions, from which the IOD index is subsequently calculated. Experimental results demonstrate that the CAIPM significantly outperforms traditional statistical methods and current mainstream deep learning models in predicting the IOD index. Specifically, for a 12-month lead prediction, the Pearson correlation coefficient (PCC) of CAIPM is 32%, 22%, 16%, and 6% higher than that of the CNN, CNN-LSTM, TCN, and ConvLSTM models, respectively.
- climate change,
- IOD prediction,
- DMI index,
- spatio-temporal sequence prediction,
- cuboid attention mechanism

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References(30)

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