Wave hindcasting by coupling numerical model and artificial neural networks
Wave hindcasting by coupling numerical model and artificial neural networks
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DOI：
10.1016/j.oceaneng.2007.09.003
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发表时间：
2008-03
影响因子：
5
通讯作者：
I. Malekmohamadi;R. Ghiassi;M. Yazdanpanah
中科院分区：
文献类型：
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作者：
I. Malekmohamadi;R. Ghiassi;M. Yazdanpanah
通过耦合数值波浪模型（NWM）和人工神经网络（ANN），提出了一种新的波浪预测程序。在许多情况下，出于经济考虑，数值波浪建模并不合理。尽管加入人工神经网络模型成本低廉，但这种模型需要长时间的波浪数据进行训练，这通常不方便实现。这两种方法的适当组合可以发挥两者的潜力。根据所提出的方法，波浪数据是由 NWM 通过在相关点假设的短时间风来生成的。然后，使用上述生成的风波数据设计和训练人工神经网络。该人工神经网络模型能够以低成本和可接受的精度将风速时间序列映射到波高和周期时间序列。该方法应用于两个不同地点的波浪后报；苏必利尔湖和太平洋。仿真结果表明了该方法的优越性。
By coupling numerical wave model (NWM) and artificial neural networks (ANNs), a new procedure for wave prediction is proposed. In many situations, numerical wave modeling is not justified due to economical consideration. Although incorporation of an ANN model is inexpensive, such a model needs a long time period of wave data for training, which is generally inconvenient to achieve. A proper combination of these two methods could carry the potentials of both. Based on the proposed approach, wave data are generated by a NWM by means of a short period of assumed winds at a concerned point. Then, an ANN is designed and trained using the above-mentioned generated wind-wave data. This ANN model is capable of mapping wind-velocity time series to wave height and period time series with low cost and acceptable accuracy. The method was applied for wave hindcasting to two different sites; Lake Superior and the Pacific Ocean. Simulation results show the superiority of the proposed approach.