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Real-time in situ prediction of ocean currents

机译：实时原位预测海洋电流

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摘要

The prediction of ocean currents is essential for the path planning and control of Autonomous Underwater Vehicles. Regional physics-based forecast models provide valid predictions but are too computationally expensive for real-time prediction necessary for AUV navigation. While vehicle sensors can measure the spatial evolution of currents, temporal prediction remains an open problem as existing data-driven models with real-time capabilities have only been shown to work at locations where data have been used to develop the model. We propose in this paper two predictive tools using deep learning techniques, a Long Short-Term Memory (LSTM) Recurrent Neural Network and a Transformer, to perform real-time in-situ prediction of ocean currents at any location. A data set from the National Oceanic and Atmospheric Administration is split in two distinct sets to train and test the models. We show that the LSTM and the Transformer have an averaged Normalized Root Mean Squared Error respectively of 0.10 and 0.11 over all test sites with a standard deviation respectively of 0.024 and 0.031. Comparisons with Harmonic Method predictions at various locations in the territorial sea of the United States show that both models provide state-of-the-art accuracy without having been trained with data from these sites.
机译：海流预测对于自主水下车辆的路径规划和控制至关重要。基于区域物理的预测模型提供了有效的预测，但对于AUV导航所需的实时预测来说太昂贵了。虽然车辆传感器可以测量电流的空间演进，但是时间预测仍然是一个公开问题，因为只有实时功能的现有数据驱动模型仅显示在用于开发模型的数据的位置工作。我们在本文中提出了两种预测工具，使用深度学习技术，长短期记忆（LSTM）经常性神经网络和变压器，在任何位置执行海洋电流的实时原位预测。来自国家海洋和大气管理局的数据分为两个不同的集合，以培训和测试模型。我们表明LSTM和变压器具有分别为0.10和0.11的平均归一化均方的误差，在所有测试站点上，标准偏差分别为0.024和0.031。美国领海的各个地点的谐波方法预测的比较表明，两种模型都提供了最先进的准确性，而无需使用这些站点的数据训练。

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