从单色波浪到现实的潮:深度学习用于沿海海洋短期预测
Irem Yildiz1, Emil V Stanev2,3, Joanna Staneva2
1Institute of Coastal Systems - Analysis and Modeling, Helmholtz-Zentrum Hereon, Geesthacht, Germany. irem.yildiz@hereon.de.
Scientific reports
|December 21, 2025
概括
这项研究引入了一种深度学习模型,用于预测德国海湾海平面变化. 混合框架准确地预测海平面,即使是从有限的沿海数据,补充传统的数值模型.
科学领域:
- 海洋学 海洋学 海洋学
- 人工智能的人工智能
- 沿海工程 沿海工程
背景情况:
- 准确的海平面预测对于沿海管理和运营海洋预测至关重要.
- 传统的数值模型在复杂的沿海环境中面临着挑战.
研究的目的:
- 开发和评估一种混合深度学习模型,用于预测海平面变化.
- 从有限的沿海数据来评估该模型能够重建盆地范围的海平面的能力.
- 调查深度学习的潜力,以补充数值模型.
主要方法:
- 一种混合架构,将卷积神经网络 (CNN) 结合起来,用于空间重建和长短期记忆 (LSTM) 网络用于时间预测.
- 从学术和现实的场景中应用到海平面数据,包括单色波和真实的潮成分 (M2,M4).
- 实验包括不同的数据可用性来模仿数据同化.
主要成果:
- 混合模型以最小的错误实现了准确的短期海平面预测.
- 对于潮组成部分,强大的性能被证实为高达48小时的交付时间.
- 深度学习成功地从有限数量的沿海标尺中重建了盆地范围内的海平面.
- 结合沿海观测数据显著提高了预测准确度.
结论:
- 深度学习模型可以有效地预测海平面变化,并重建空间海平面数据.
- 混合深度学习框架显示了在运营预测中补充传统数值模型的巨大潜力.
- 这种方法在数据丰富的沿海地区对于基于证据的管理尤其有价值.
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