从使用深度学习与3D卷积的X波段海洋雷达图像进行显著波高预测
Ji-Woo Kwon1, Won-Du Chang1, Young Jun Yang2
1Department of Artificial Intelligence Convergence, Pukyong National University, Busan, Republic of Korea.
PloS one
|October 30, 2023
概括
这项研究使用深度学习与卷积神经网络 (CNN) 来估计海浪高度从雷达图像. 该方法准确地预测波浪高度,这对于海洋工程应用至关重要.
科学领域:
- 海洋学 海洋学 海洋学
- 海洋工程 海洋工程
- 人工智能的人工智能
背景情况:
- 准确的海浪高度估计对于海洋工程至关重要.
- 传统方法面临着实时数据和复杂条件的挑战.
- 深度学习为增强波浪监控提供了一个有前途的方法.
研究的目的:
- 开发一种深度学习回归模型,用于精确估计海洋波浪高度.
- 为了利用X波段雷达图像进行波高预测.
- 为了解决波浪高度数据集中的数据不平衡问题.
主要方法:
- 使用基于VGGNet架构的卷积神经网络 (CNN).
- 在72,180张雷达图像和浮标波高度的数据集上训练了模型.
- 从雷达图像序列中使用3D卷积来提取时间和空间特征.
- 应用分层随机抽样来缓解数据不平衡.
主要成果:
- 实现了0.3576m的根平均平方误差 (RMSE) 波浪高度估计.
- 在74天的时间里,在各种波浪高度条件下展示了准确的预测.
- 在雷达图像序列中成功捕获时间特征.
结论:
- 拟议的深度学习模型提供了可靠和准确的海洋波浪高度估计.
- 该方法在需要精确波浪数据的海洋工程应用中是有效的.
- 解决数据不平衡对于海洋学中强大的深度学习模型至关重要.
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