Spatiotemporal Prediction of Tidal Currents Using Gaussian Processes
Spatiotemporal Prediction of Tidal Currents Using Gaussian Processes
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DOI：
10.1029/2018jc014471
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发表时间：
2019-04
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通讯作者：
Dripta Sarkar;Michael A. Osborne;T. Adcock
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作者：
Dripta Sarkar;Michael A. Osborne;T. Adcock
预测快速潮流可能是一项具有挑战性的任务。与潮汐水位不同，水流在短距离内变化剧烈。谐波分析的经典方法可以分析点位置处的数据，并且需要一种可以处理时空数据的方法，并且对现实世界测量中不可避免的不确定性和噪声具有鲁棒性。在这项工作中，我们提出了一种贝叶斯机器学习（ML）方法来解决这个问题。该方法基于高斯过程，这是一种非参数ML技术，使用核函数来捕获数据中的结构。一个案例研究是使用一个经过验证的数值模型模拟在彭特兰湾地区，英国的潮汐动力学的数据。几种采样策略进行了探讨，并发现在每次采样后测量位置发生变化的情况下，预测的平均误差最低。我们表明，所提出的方法，使用来自一个单一的移动数据源的数据可以提供一个更好的时空描述比传统的技术，使用连续的数据从大量的位置。这项工作对潮汐能农场、导航和其他用途的开发者很有用。
Predicting fast tidal currents can be a challenging task. Unlike tidal water levels, currents can vary sharply over short distances. The classical approach of harmonic analysis can analyze data at point locations and there is a need for a method that can handle spatiotemporal data, as well as be robust to the uncertainty and noise inevitable in real-world measurements. In this work, we present a Bayesian machine learning (ML) approach to tackle the problem. The method is based on Gaussian processes, a nonparametric ML technique that uses a kernel function to capture structures in the data. A case study is performed using data from a validated numerical model simulating the tidal dynamics in the Pentland Firth region, UK. Several sampling strategies are explored and the case where measurement location is changed after every sampling is found to produce the lowest average error in the predictions. We show that the presented methodology using data from just a single moving data source can provide a better spatiotemporal description than traditional techniques using continuous data from a large number of locations. The work can be useful to developers of tidal energy farms, navigation, and other purposes.