• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.367-373
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• 2021

In this study, deep learning model was set up to predict the wave heights inside a harbour. Various machine learning techniques were applied to the model in consideration of the transformation characteristics of offshore waves while propagating into the harbour. Pohang New Port was selected for model application, which had a serious problem of unloading due to swell and has lots of available wave data. Wave height, wave period, and wave direction at offshore sites and wave heights inside the harbour were used for the model input and output, respectively, and then the model was trained using deep learning method. By considering the correlation between the time series wave data of offshore and inside the harbour, the data set was separated into prevailing wave directions as a pre-processing method. As a result, It was confirmed that accuracy and stability of the model prediction are considerably increased.

• The Journal of Korean Institute of Information Technology
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• v.17 no.9
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• pp.11-17
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• 2019

In recent years, damage and loss of life and property have been occurred frequently due to swelling waves in the East Sea. Swelling waves are not easy to predict because they are caused by various factors. In this research, we build a model for predicting the swelling waves occurrence in the East Coast of Korea using machine learning technique. We collect historical data of unloading interruption in the Pohang Port, and collect air pressure, wind speed, direction, water temperature data of the offshore Pohang Port. We select important variables for prediction, and test various machine learning prediction algorithms. As a result, tide level, water temperature, and air pressure were selected, and Random Forest model produced best performance. We confirm that Random Forest model shows best performance and it produces 88.86% of accuracy

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.3
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• pp.873-883
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• 2014

This study proposed an estimation method of allowable wave height for loading and unloading of the ship considering ship motion that is affected by ship sizes, mooring conditions, wave periods and directions. The method was examined validity by comparison with wave field data at pier $8^{th}$ in Pohang new harbor. The wave field data obtained with wave height of 0.10~0.75m and wave period of 7~13s in ship sizes of 800~35,000ton when a downtimes have occurred. On the other hand, the results of allowable wave height for loading and unloading of the ship in this method have obtained with wave heights of 0.19~0.50m and wave periods of 8~12s for ship sizes of 5,000, 10,000 and 30,000ton. Thus this method well reproduced the field data respond to various a ship sizes and wave periods. And the results of this method tended to decrease in 16~62% when have considered long wave, and it is decreased in 0~46% when didn't consider long wave than design standards in case of the ship sizes of 5,000~30,000ton, wave period of 12s and wave angle of $75^{\circ}C$. The allowable wave heights for loading and unloading of the ship proposed by design standards are didn't respond to various the ship sizes and wave periods, and we have found that the design standards has overestimated on smaller than 10,000ton.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.29 no.4
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• pp.180-188
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• 2017

Recently, as the strength of winds and waves increases due to the climate change, abnormal waves such as swells have been also increased, which results in the increase of downtime events of loading/unloading in a harbour. To reduce the downtime events, breakwaters were constructed in a harbour to improve the tranquility. However, it is also important and useful for efficient port operation by predicting accurately and also quickly the downtime events when the harbour operation is in a limiting condition. In this study, numerical simulations were carried out to calculate the wave conditions based on the forecasted wind data in offshore area/outside harbour and also the long-term observation was carried out to obtain the wave data in a harbour. A forecasting method was designed using an auto-regressive (AR) and artificial neural networks (ANN) models in order to establish the relationship between the wave conditions calculated by wave model (SWAN) in offshore area and observed ones in a harbour. To evaluate the applicability of the proposed method, this method was applied to predict wave heights in a harbour and to forecast the downtime events in Pohang New Harbour with highly complex topography were compared. From the verification study, it was observed that the ANN model was more accurate than the AR model.

• Journal of Navigation and Port Research
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• v.34 no.8
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• pp.641-647
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• 2010

Container port development in Korea seems to be based on the policy of balanced regional development rather than demand and supply theory. The problem of overcapacity and low utilization faced by several ports such as Kwangyang, Ulsan New Port and Phohang Youngil New Port can back up this. Furthermore as some ports are located closely sharing the same domestic hinterland the revitalization of the ports is not easy resulting in wasting resources with both regional and national aspect. This study, therefore, aims at providing an empirical results for the container port classification of the 5 ports such as Busan, Kwangyang, Incheon, Pyeongtaek and Ulsan. For this several time series data for the ports such as transshipment containers, import and export containers, origin and destination countries, and local origin and destination are analysed. Based on the results of the analysis the 5 container ports are classified together with their practical roles, and the functional overlap of the port including Phohang was analysed.