Wind-wave variability in a shallow tidal sea—Spectral modelling combined with neural network methods
Wind-wave variability in a shallow tidal sea—Spectral modelling combined with neural network methods
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DOI：
10.1016/j.coastaleng.2009.02.007
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发表时间：
2009-07
影响因子：
4.4
通讯作者：
A. Herman;R. Kaiser;H. D. Niemeyer
中科院分区：
文献类型：
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作者：
A. Herman;R. Kaiser;H. D. Niemeyer
本文用数值和神经网络相结合的方法模拟了德国瓦登海潮汐盆地的风浪变率。首先，利用最先进的第三代频谱波浪模型SWAN模拟了研究区内的波浪传播和变形。通过对四个测站的模拟和实测平均波浪参数的比较，显示了SWAN在风、水位和海流高度变化的非平稳条件下准确再现感兴趣的现象的能力。然后使用SWAN结果的主成分分析来揭示数据中的主要空间模式并降低它们的维度，从而使得能够对整个研究区域的平均波参数进行高效和相对直观的神经网络建模。结果表明，用本文提出的方法产生的数据具有统计特性(平均波参数的离散概率分布等)。与SWAN资料的性质非常接近，从而证明该方法可以作为沿海地区风浪模拟的可靠工具，补充(通常需要大量计算的)谱波浪模型。
In this paper the wind-wave variability in the tidal basins of the German Wadden Sea is modelled with combined numerical and neural-network (NN) methods. First, the wave propagation and transformation in the study area are modelled with the state-of-the-art third-generation spectral wave model SWAN. The ability of SWAN to accurately reproduce the phenomena of interest in nonstationary conditions governed by highly variable winds, water levels and currents is shown by comparisons of the modelled and measured mean wave parameters at four stations. The principal component analysis of the SWAN results is then used to reveal the dominating spatial patterns in the data and to reduce their dimensionality, thus enabling an efficient and relatively straightforward NN modelling of mean wave parameters in the whole study area. It is shown that the data produced with the approach developed in this work have statistical properties (discrete probability distributions of the mean wave parameters etc.) very close to the properties of the data obtained with SWAN, thus proving that this approach can be used as a reliable tool for wind wave simulation in coastal areas, complementary to (often computationally demanding) spectral wave models.