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Abstract 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.
Publication Date 2020-10-14
Role Corresponding Author
Category SCIE
Impact Factor 2.033

Ki-Su Kim, June-Beom Lee, Myung-Il Roh, Ki-Min Han, Gap-Heon Lee, "Prediction of Ocean Weather Based on Denoising AutoEncoder and Convolutional LSTM", Journal of Marine Science and Engineering, Vol. 8, No. 10, pp. 805:1-24, 2020.10.14

https://doi.org/10.3390/jmse8100805


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  3. No Image 14Oct
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    in International Journal

    Ki-Su Kim, June-Beom Lee, Myung-Il Roh, Ki-Min Han, Gap-Heon Lee, "Prediction of Ocean Weather Based on Denoising AutoEncoder and Convolutional LSTM", Journal of Marine Science and Engineering, Vol. 8, No. 10, pp. 805:1-24, 2020.10.14

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    Joo-Pil Lee, Myung-Il Roh, Hye-Won Lee, Seung-Ho Ham, "Design of a Wreck Removal Method Considering Safety and Economy", Ships and Offshore Structures, Vol. 15, No. 10, pp. 1037-1056, 2020.12.01

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    Seung-Ho Ham, Myung-Il Roh, Hye-Won Lee, "Collision Detection and Response of Multibody Systems Using a Position Difference Method and Non-Interpenetration Constraint in Shipbuilding Simulation", Ocean Engineering, Vol. 195, pp. 1-18, 2020.01.01

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    Luman Zhao, Myung-Il Roh, Sung-Jun Lee, "Control Method for Path Following and Collision Avoidance of Autonomous Ships Based on Deep Reinforcement Learning", Journal of Marine Science and Technology (Taiwan), Vol. 27, No. 4, pp. 293-310, 2019.08.01

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    Xing Li, Myung-Il Roh, Seung-Ho Ham, "A Collaborative Simulation in Shipbuilding and the Offshore Installation Based on the Integration of the Dynamic Analysis, Virtual Reality, and Control Devices", IJNAOE, Vol. 11, No. 2, pp. 699-722, 2019.07.01

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    Min-Jae Oh, Myung-Il Roh, Sung-Woo Park, Do-Hyun Chun, Se-Hyun Myung, "Big Data-based Piping Material Analysis Framework in Offshore Structure for Contract Design", Ocean Systems Engineering, Vol. 9, No. 1, pp. 79-95, 2019.01.01

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