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A non-homogeneous hidden Markov model for predicting the distribution of sea surface elevation

机译:非均匀隐马尔可夫模型预测海平面高程分布

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摘要

The prediction problem of sea state based on the field measurements of wave and meteorological factors is a topic of interest from the standpoints of navigation safety and fisheries. Various statistical methods have been considered for the prediction of the distribution of sea surface elevation. However, prediction of sea state in the transitional situation when waves are developing by blowing wind has been a difficult problem until now, because the statistical expression of the dynamic mechanism during this situation is very complicated. In this article, we consider this problem through the development of a statistical model. More precisely, we develop a model for the prediction of the time-varying distribution of sea surface elevation, taking into account a non-homogeneous hidden Markov model in which the time-varying structures are influenced by wind speed and wind direction. Our prediction experiments suggest the possibility that the proposed model contributes to an improvement of the prediction accuracy by using a homogenous hidden Markov model. Furthermore, we found that the prediction accuracy is influenced by the circular distribution of the circular hidden Markov model for the directional time series wind direction data.
机译:从航行安全和渔业的角度出发,基于波浪和气象因素的现场测量的海况预测问题是一个有趣的话题。已经考虑了各种统计方法来预测海平面高程的分布。然而,到目前为止,由于风在这种情况下动力机制的统计表达非常复杂,因此在海浪吹动风的过渡情况下预测海况一直是一个难题。在本文中,我们通过开发统计模型来考虑这个问题。更准确地说,我们考虑了一个非均质的隐马尔可夫模型,该模型预测了海平面高程的时变分布,其中时变结构受风速和风向的影响。我们的预测实验表明,通过使用同质的隐马尔可夫模型,提出的模型有助于提高预测精度。此外,我们发现对于方向时间序列风向数据,圆形隐马尔可夫模型的圆形分布会影响预测精度。

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