Prediction of tidal currents using Bayesian machine learning
Prediction of tidal currents using Bayesian machine learning
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DOI：
10.1016/j.oceaneng.2018.03.007
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发表时间：
2018-06
影响因子：
5
通讯作者：
Dripta Sarkar;Michael A. Osborne;T. Adcock
中科院分区：
文献类型：
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作者：
Dripta Sarkar;Michael A. Osborne;T. Adcock
我们建议在贝叶斯框架中使用机器学习技术来预测潮流。基于经典谐波分析方法的计算机算法已经在潮汐预测中使用了几十年，但是该方法在处理噪声、表达不确定性、捕获非正弦、非谐波变化方面存在一些局限性。需要有原则的方法来处理不确定性并适应数据中的噪声。在这项工作中，我们使用高斯过程（一种贝叶斯非参数机器学习技术）来预测潮流。该方法的概率和非参数性质使其能够表示建模中的不确定性并处理问题的复杂性。该方法利用内核函数来捕获数据中的结构。总体目标是利用机器学习的最新进展来构建鲁棒的算法。使用多组现场数据，我们表明机器学习方法可以取得比传统方法更好的结果。
We propose the use of machine learning techniques in the Bayesian framework for the prediction of tidal currents. Computer algorithms based on the classical harmonic analysis approach have been used for several decades in tidal predictions, however the method has several limitations in terms of handling of noise, expressing uncertainty, capturing non-sinusoidal, non-harmonic variations. There is a need for principled approaches which can handle uncertainty and accommodate noise in the data. In this work, we use Gaussian processes, a Bayesian non-parametric machine learning technique, to predict tidal currents. The probabilistic and non-parametric nature of the approach enables it to represent uncertainties in modelling and deal with complexities of the problem. The method makes use of kernel functions to capture structures in the data. The overall objective is to take advantage of the recent progress in machine learning to construct a robust algorithm. Using several sets of field data, we show that the machine learning approach can achieve better results than the traditional approaches.