海洋预测的深度学习:对方法,应用和数据集的全面审查
IEEE transactions on cybernetics
|April 1, 2025
概括
深度学习为海洋预测提供了一种强大的新方法,补充了传统的数值模型. 本综述探讨了基于深度学习的海洋预测 (DLOF) 模型,数据集和未来趋势.
科学领域:
- 海洋学 海洋学 海洋学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 准确的海洋预测是一个长期存在的科学挑战.
- 传统的数值海洋预测 (NOP) 在表示物理过程和同化数据方面面临局限性.
- 大规模的海洋数据流为先进的分析方法提供了机会.
研究的目的:
- 提供基于深度学习的海洋预测 (DLOF) 的全面审查.
- 探索DLOF模型架构,时空尺度和可解释性.
- 评估用于海洋预测的混合理论驱动和数据驱动模型的可行性.
主要方法:
- 对用于海洋预测的最先进的深度学习模型的审查.
- 使用数据集,基准和云计算资源对DLOF的评估.
- 混合架构的分析,结合数值和深度学习方法.
主要成果:
- 深度学习在彻底改变海洋预测方面取得了重大进展.
- DLOF模型提供了从时空海洋学数据中提取见解的新方法.
- 集成NOP和DLOF的混合架构证明了可行性.
结论:
- 基于深度学习的海洋预测 (DLOF) 是传统方法的有希望的补充.
- 对DLOF进行进一步的研究,包括可解释性和混合模型,至关重要.
- 该审查确定了DLOF研究当前的局限性和未来的趋势.
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