A Machine Learning Approach to the Prediction of Tidal Currents.-猫眼课题宝

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2016-06

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作者：

Dripta Sarkar;Michael A. Osborne;T. Adcock

我们建议使用机器学习技术来预测潮流。调和分析的经典方法被广泛应用于潮流预报中，基于该方法的计算机算法已经被用于该目的几十年。该方法通过使用最小二乘优化方法最小化原始数据和模型输出之间的差异来确定参数。然而，虽然该方法被认为是最先进的，它具有几个缺点，可能会导致显着的预测误差，特别是在快速潮流和“噪声”潮汐信号的位置。在一般情况下，需要仔细选择潮汐成分，以实现良好的预测，并在时间上的平稳性的基本假设可以限制特定情况下的方法的适用性。需要有原则的方法，可以处理不确定性和适应数据中的噪声。在这项工作中，我们使用高斯过程，贝叶斯非参数技术，预测潮流。总体目标是利用机器学习的最新进展来构建一个强大而有效的算法。该开发项目特别有利于潮汐能界，旨在利用快速潮流位置的能源。

We propose the use of techniques from Machine Learning for the prediction of tidal currents. The classical methodology of harmonic analysis is widely used in the prediction of tidal currents and computer algorithms based on the method have been used for decades for the purpose. The approach determines parameters by minimizing the difference between the raw data and model output using the least squares optimization approach. However, although the approach is considered to be state-of-the-art, it possesses several drawbacks that can lead to significant prediction errors, especially at locations of fast tidal currents and ’noisy’ tidal signal. In general, careful selection of tidal constituents is required in order to achieve good predictions, and the underlying assumption of stationarity in time can restrict the applicability of the method to particular situations. There is a need for principled approaches which can handle uncertainty and accommodate noise in the data. In this work, we use Gaussian process, a Bayesian non-parametric technique, to predict tidal currents. The overall objective is to take advantage of the recent progress in machine learning to construct a robust yet efficient algorithm. The development can specifically benefit the tidal energy community, aiming to harness energy from location of fast tidal currents.

A Machine Learning Approach to the Prediction of Tidal Currents.

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