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http://dx.doi.org/10.9765/KSCOE.2017.29.4.180

Wave Height and Downtime Event Forecasting in Harbour with Complex Topography Using Auto-Regressive and Artificial Neural Networks Models  

Yi, Jin-Hak (Coastal Engineering Division, Korea Institute of Ocean Science and Technology)
Ryu, Kyong-Ho (Coastal Engineering Division, Korea Institute of Ocean Science and Technology)
Baek, Won-Dae (Coastal Engineering Division, Korea Institute of Ocean Science and Technology)
Jeong, Weon-Mu (Coastal Engineering Division, Korea Institute of Ocean Science and Technology)
Publication Information
Journal of Korean Society of Coastal and Ocean Engineers / v.29, no.4, 2017 , pp. 180-188 More about this Journal
Abstract
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.
Keywords
auto-regressive model; artificial neural networks; wave height estimation; downtime event; numerical simulation; observation;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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