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http://dx.doi.org/10.7837/kosomes.2022.28.4.469

Tidal Level Prediction of Busan Port using Long Short-Term Memory  

Kim, Hae Lim (Interdisciplinary Program of Ocean Industrial Engineering, Pukyong National University)
Jeon, Yong-Ho (CnS Solution Co., Ltd.)
Park, Jae-Hyung (CnS Solution Co., Ltd.)
Yoon, Han-sam (College of Liberal Arts, Pukyong National University)
Publication Information
Journal of the Korean Society of Marine Environment & Safety / v.28, no.4, 2022 , pp. 469-476 More about this Journal
Abstract
This study developed a Recurrent Neural Network model implemented through Long Short-Term Memory (LSTM) that generates long-term tidal level data at Busan Port using tide observation data. The tide levels in Busan Port were predicted by the Korea Hydrographic and Oceanographic Administration (KHOA) using the tide data observed at Busan New Port and Tongyeong as model input data. The model was trained for one month in January 2019, and subsequently, the accuracy was calculated for one year from February 2019 to January 2020. The constructed model showed the highest performance with a correlation coefficient of 0.997 and a root mean squared error of 2.69 cm when the tide time series of Busan New Port and Tongyeong were inputted together. The study's finding reveal that long-term tidal level data prediction of an arbitrary port is possible using the deep learning recurrent neural network model.
Keywords
Busan Port; Long Short-Term Memory(LSTM); Deep learning; Recurrent neural network; Tidal level prediction;
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Times Cited By KSCI : 2  (Citation Analysis)
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