Spatial-temporal Offshore Current Field Forecasting Using Residual-learning Based Purely CNN Methodology with Attention Mechanism-Reference-Cited by-同舟云学术

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Spatial-temporal Offshore Current Field Forecasting Using Residual-learning Based Purely CNN Methodology with Attention Mechanism

Published:2024-03-04 Issue:1 Volume:38 Page:
ISSN:0883-9514
Container-title:Applied Artificial Intelligence
language:en
Short-container-title:Applied Artificial Intelligence

Author:

Zhang Zeguo¹,Yin Jianchuan¹

Affiliation:

1. Naval Architecture and Shipping College, Guangdong Ocean University, Zhanjiang, China

Funder

National Natural Science Foundation of China

the Special Projects of Key Areas for Colleges and Universities in Guangdong Province

the Natural Science Foundation of Guangdong Province of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/08839514.2024.2323827

Reference47 articles.

1. Ensemble robust local mean decomposition integrated with random forest for short-term significant wave height forecasting

2. Advanced extreme learning machines vs. deep learning models for peak wave energy period forecasting: A case study in Queensland, Australia

3. A novel approach for harmonic tidal currents constitutions forecasting using hybrid intelligent models based on clustering methodologies

4. Prediction of aerodynamic flow fields using convolutional neural networks

5. Bradbury J. S. Merity C. Xiong R. Soche. 2016. Quasi-recurrent neural networks. arXiv preprint arXiv:1611.01576.

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