从浮标测量结果预测异常波的情况
Thomas Breunung1, Balakumar Balachandran2
1Department of Mechanical Engineering, University of Maryland, College Park, MD, 20742, USA. thomasbr@umd.edu.
Scientific reports
|July 18, 2024
概括
这项研究表明,神经网络可以使用浮标数据预测流波. 人工智能模型成功地预测到5分钟的预警,提高海上安全.
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:
- 怪异或流的波浪对海上运营和基础设施构成重大风险.
- 目前的方法缺乏对这些极端波浪事件的可靠预测能力.
研究的目的:
- 开发和演示使用现场测量预测流波的方法.
- 通过可靠的预测,减轻与流波相关的风险.
主要方法:
- 利用了一个广泛的浮标数据集,包括数十亿波.
- 参数化和训练的神经网络,以区分前流波条件.
- 验证了网络将预测推断到新位置的能力.
主要成果:
- 成功预测了四个流波中的三个,提前1分钟.
- 在10个流波中,预测准确度达到7个,警告时间为5分钟.
- 在现实的场景中表现出可靠的性能,罕见的流波发生.
结论:
- 神经网络提供了一种可行的方法,可以从现场数据中预测流波.
- 开发的预测方法通过提供预警,提高了海上安全.
- 该模型的推断能力表明,它对于流波预测具有广泛的适用性.
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