用数值和神经网络模型预测海浪

S. Mandal, N. Prabaharan
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引用次数: 32

摘要

本文综述了数值海浪预报模式的发展和近年来在海浪预报中应用的神经网络。数值波模型表达了现象的物理概念。数值波动模型的性能取决于如何将现象最好地表达为数值格式,从而可以估计更准确的波动参数。数值波模型仍有改进的余地。当同一现象的输入输出参数已知时,神经网络可以很好地定义该现象。利用神经网络进行海浪参数的预报显示了其潜在的实用性。结果表明,神经网络的短期波动预测结果与实际预测结果非常接近。研究还发现,神经网络不仅简化了复杂的现象,而且预测了相当精确的波参数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ocean Wave Prediction Using Numerical and Neural Network Models
This paper presents an overview of the development of the numerical wave prediction models and recently used neural networks for ocean wave hindcasting and forecasting. The numerical wave models express the physical concepts of the phenomena. The performance of the numerical wave model depends on how best the phenomena are expressed into the numerical schemes, so that more accurate wave parameters could be estimated. There are still scopes for improving the numerical wave models. When exact input-output parameters are known for the same phenomenon, it can be well de- fined by the neural network. Hindcasting of ocean wave parameters using neural networks shows its potential usefulness. It is observed that the short-term wave predictions using neural networks are very close to the actual ones. It is also ob- served that the neural network simplifies not only the complex phenomena, but also predicts fairly accurate wave parame- ters.
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