STCANet: Spatiotemporal Coupled Attention Network for Ocean Surface Current Prediction

Xie, Chengzhi; Chen, Ping; Man, Tenghao; Dong, Junyu

doi:10.1007/s11802-023-5269-2

Cited by 7 publications

(2 citation statements)

References 17 publications

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“…Deep neural network models can automatically learn and extract feature relationships in complex data. Xie extracted the spatiotemporal coupled features of ocean currents, captured correlations and dependencies between adjacent sea areas using the Spatial Channel Attention Module (SCAM), and used the Gated Recurrent Unit (GRU) to model the temporal relationships of ocean currents [50]. They developed a deep network model called Spatiotemporal Coupled Attention Network (STCANet), which outperforms traditional models such as History Average (HA) and Autoregressive Integrated Moving Average (ARIMA).…”

Section: Dynamic Environmental Factorsmentioning

confidence: 99%

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A Review on Motion Prediction for Intelligent Ship Navigation

Zhang,

Chu,

Liu

et al. 2024

JMSE

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In recent years, as intelligent ship-navigation technology has advanced, the challenge of accurately modeling and predicting the dynamic environment and motion status of ships has emerged as a prominent area of research. In response to the diverse time scales required for the prediction of ship motion, various methods for modeling ship navigation environments, ship motion, and ship traffic flow have been explored and analyzed. Additionally, these motion-prediction methods are applied for motion control, collision-avoidance planning, and route optimization. Key issues are summarized regarding ship-motion prediction, including online modeling of motion models, real ship validation, and consistency in modeling, optimization, and control. Future technology trends are predicted in mechanism-data fusion modeling, large-scale model, multi-objective motion prediction, etc.

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Section: Dynamic Environmental Factorsmentioning

confidence: 99%

“…STCANet [50,56] STCANet, through the integration of spatial and temporal attention mechanisms, excels in capturing the interactions between variables such as wind, waves, currents, and tides. This results in higher predictive accuracy compared to traditional models.…”

Section: Dynamic Environmental Factorsmentioning

confidence: 99%