The path planning of a ship requires much information, and one of the essential factors is predicting the ocean environment. Ocean weather can generally be gathered from forecasting information provided by weather centers. However, these data are difficult to obtain when satellite communication is unstable during voyages, or there are cases where forecast data for a more extended period of time are needed for the operation of the fleet. Therefore, shipping companies and classification societies have attempted to establish a model for predicting the ocean weather on its own. Historically, ocean weather has been primarily predicted using empirical and numerical methods. Recently, a method for predicting ocean weather using deep learning has emerged. In this study, a deep learning model combining a denoising AutoEncoder and convolutional long short-term memory (LSTM) was proposed to predict the ocean weather worldwide. The denoising AutoEncoder is effective for removing noise that hinders the training of deep learning models. While the LSTM could be used as time-series inputs at specific points, the convolutional LSTM can use time-series images as inputs, making them suitable for predicting a wide range of ocean weather. Herein, using the proposed model, eight parameters of ocean weather were predicted. The proposed learning model predicted ocean weather after one week, showing an average error of 6.7%. The results show the applicability of the proposed learning model for predicting ocean weather.

中文翻译：

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基于降噪自动编码器和卷积LSTM的海洋天气预测

船只的路径规划需要大量信息，而基本因素之一就是预测海洋环境。通常可以从气象中心提供的预报信息中收集海洋天气。但是，在航行中卫星通信不稳定时，或者在某些情况下，需要更长时间段的预报数据以使船队运营时，很难获得这些数据。因此，船运公司和船级社已尝试建立一种可自行预测海洋天气的模型。历史上，主要使用经验和数值方法来预测海洋天气。最近，出现了一种使用深度学习预测海洋天气的方法。在这个研究中，提出了一种结合去噪自动编码器和卷积长短期记忆（LSTM）的深度学习模型来预测全球海洋天气。去噪自动编码器可有效消除干扰深度学习模型训练的噪声。虽然LSTM可以在特定点用作时间序列输入，但是卷积LSTM可以将时间序列图像用作输入，这使其适合于预测广泛的海洋天气。在此，使用所提出的模型，预测了海洋天气的八个参数。拟议的学习模型预测了一周后的海洋天气，显示平均误差为6.7％。结果表明所提出的学习模型在预测海洋天气方面的适用性。去噪自动编码器可有效消除干扰深度学习模型训练的噪声。虽然LSTM可以在特定点用作时间序列输入，但是卷积LSTM可以将时间序列图像用作输入，这使其适合于预测广泛的海洋天气。在此，使用所提出的模型，预测了海洋天气的八个参数。拟议的学习模型预测了一周后的海洋天气，显示平均误差为6.7％。结果表明所提出的学习模型在预测海洋天气方面的适用性。去噪自动编码器可有效消除干扰深度学习模型训练的噪声。虽然LSTM可以在特定点用作时间序列输入，但是卷积LSTM可以将时间序列图像用作输入，这使其适合于预测广泛的海洋天气。在此，使用所提出的模型，预测了海洋天气的八个参数。拟议的学习模型预测了一周后的海洋天气，显示平均误差为6.7％。结果表明所提出的学习模型在预测海洋天气方面的适用性。在此，使用所提出的模型，预测了海洋天气的八个参数。拟议的学习模型预测了一周后的海洋天气，显示平均误差为6.7％。结果表明所提出的学习模型在预测海洋天气方面的适用性。在此，使用所提出的模型，预测了海洋天气的八个参数。拟议的学习模型预测了一周后的海洋天气，显示平均误差为6.7％。结果表明所提出的学习模型在预测海洋天气方面的适用性。