Real-time in situ prediction of ocean currents
Real-time in situ prediction of ocean currents
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DOI：
10.1016/j.oceaneng.2021.108922
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发表时间：
2021-04-06
影响因子：
5
通讯作者：
Alam, Mohammad-Reza
中科院分区：
文献类型：
--
作者：
Immas, Alexandre;Do, Ninh;Alam, Mohammad-Reza
洋流预测是自主水下航行器路径规划和控制的重要内容。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，计算成本太高。虽然车辆传感器可以测量电流的空间演变，但时间预测仍然是一个悬而未决的问题，因为现有的具有实时功能的数据驱动模型仅在使用数据开发模型的位置有效。我们在本文中提出了两种使用深度学习技术的预测工具，长短期记忆（LSTM）递归神经网络和变压器，用于在任何位置进行实时的洋流原位预测。来自国家海洋和大气管理局的数据集被分成两个不同的集来训练和测试模型。我们表明，LSTM和Transformer在所有测试点上的平均标准化均方根误差分别为0.10和0.11，标准差分别为0.024和0.031。与harmonmethod在美国领海不同地点的预测进行比较表明，这两种模式都提供了最先进的精度，而无需使用这些地点的数据进行训练。
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.