• 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

• Journal of Korea Water Resources Association
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• v.41 no.8
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• pp.785-795
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• 2008

The swell is a major cause of interruption for the activity in a port and the ship navigation in coastal waters, coastal geographical changes, and the disaster with a loss of lives. However, many researches about the observation and the prediction of swells have not been conducted actively due to the difficulties to collect and synthesize the massive amount of long term field data for waves and meteorological information. In this study, the internet-based realtime monitoring system(Fieldbox) was developed to collect the wave data. The characteristics and main components of swells occurred in Korea were analyzed using wave data observed through the Fieldbox and the meteorological data collected by the KMA(Korea Meteorological Administration) and NASA(National Aeronautics and Space Administration). The characteristics of the swell generation patterns were analyzed using the monthly data of the Kwangan Tower between 2004 and 2006 to estimate the specific features such as sources and locations of swells generated in Korea.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.6
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• pp.2329-2341
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• 2013

In this study, we develop an algorithm to predict swell-like significant waves in the east coast of Korea using the directional wave gauge which is installed near Sokcho. Using the numerical wave model SWAN, we estimate wave data in open sea from the wave data observed through the directional wave gauge. Then, using the wave ray method with the open-sea wave data as offshore boundary conditions, we predict the swell-like significant waves at several points in the east coast of Korea. We verify the prediction methods with the SWAN and wave ray methods by comparing numerically predicted data against either target or measured data at the observation site. We can improve the prediction of the swell-like significant waves in the east sea of Korea using both the real-time wave measurement system and the present prediction algorithm.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.26 no.3
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• pp.149-159
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• 2014

In this study, we develop an algorithm to predict swell-like high waves on the east coast of Korea using the directional wave gauge which was installed near Sokcho. Using the numerical wave model SWAN, we estimate wave data in open sea from the wave data collected by using the directional wave gauge. Then, using the wave ray method and SWAN model with the open-sea wave data as offshore boundary conditions, we predict the swell-like high waves at several major points on the east coast of Korea. We verify the prediction methods with the SWAN and wave ray methods by comparing predicted data against measured one at Wangdolcho. We can improve the prediction of the swell-like high waves in the east sea of Korea using both the real-time wave measurement system and the present prediction algorithm.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.15 no.2
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• pp.62-67
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• 2010

Swells occurred on the coast of the East Sea on February 24, 2008 caused a loss of three lives and also damaged several west coasts of Japan. The recent increase of swell intensity with number of accidents demands more accurate forecasting of swells in terms of time and location. The swells occurred in February 2008 are hindcasted using SWAN model to examine the accuracy of the model for future forecasting. The model results are compared with ReWW3 data as well as measurement wave data and specially, wave spectrum is analysed by comparing with observed spectrum at two wave stations located in the east coast of Korea. The SWAN model shows similar results with observation data in terms of significant wave heights and swell arrival time but the shapes of wave spectrum are different between model and in-situ measurement data. For further improvement of swell forecasting, more comparison and analysis with observed wave spectrum is necessary and wave directional spectrum data are required to study on the characteristics of swells in the East Sea.

• 한국방재학회:학술대회논문집
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• 2009.02b
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• pp.85.2-85.2
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• 2009

• Water for future
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• v.45 no.12
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• pp.63-71
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• 2012

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.476-476
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• 2016

근래 들어 우리나라 동해안에서 이상고파라 불리는 너울성 고파가 자주 발생하여 상당한 인명 피해를 야기하는 등 사회적으로 큰 이슈가 되고 있다. 이상고파는 일반적으로 동해상에서 발달한 강한 저기압에 의해 발생한 고파가 상대적으로 주기가 긴 너울의 특성을 띄며 우리나라 연안에 도달하여 피해를 발생시키는 것으로 알려져 있으며, 연안에 해상상태가 잦아지는 상황에서 갑작스럽게 전파되어 오기 때문에 많은 인명피해가 발생하게 된다. 현재 미국 등의 해양예보 선진국들은 파랑모델을 운용하여 너울을 포함한 파랑예보를 수행하고 있으며, 해상부이 등의 다양한 파랑관측을 통해 그 성능을 향상시키고 있다. 우리나라에서도 선진 해양예보시스템을 활용하여 이상고파를 예측하고자 하는 연구의 필요성이 제기되고 있으며 정부 관련 부처를 중심으로 그에 대한 연구가 점차 진행되고 있다. 본 연구에서는 파랑모델을 활용하여 기존에 발생한 이상고파 피해사례에 대한 후측모의를 수행하고 우리나라에서 발생하는 이상고파의 발달과정을 분석하였다. 또한, 파랑모델의 후측모의 결과를 관측자료와 비교하여 모델의 성능을 검증하고 문제점을 분석하였다.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.28 no.4
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• pp.212-221
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• 2016

In this study, characteristics of swell-like high waves in the East Sea were analyzed using observed wave data and predicted meteorological data from the National Oceanic and Atmospheric Administration (NOAA). And, the wave prediction system using the data from the NOAA has been established. Furthermore, the applicability of the system has been verified by comparing the predicted results with the corresponding observed data. For some case, there were two times of wave height increase and the second increase occurred in a calm weather condition on the coast which might cause casualties. The direction of wave energy propagation was estimated from observed wave data in February, 2008. Through comparison between the direction of wave energy propagation and the meteorological data, it turns out that the second increase of waves is originated from the seas between Russia and Japan which is far from the East Sea.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.30 no.5
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• pp.223-233
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• 2018

The predictability of winter storm waves using KMA's operational wind forecasts has been studied to predict wind waves and swells in the East coast of Korea using SWAN. The nested model were employed along the East coast of Korea to simulate the wave transformation in the coastal area and wave dissipation term of whitecapping is optimized to improve swell prediction accuracy. In this study, KMA's operational meteorological models (RDAPS and LDAPS) are used as input wind fields. In order to evaluate model accuracy, we also simulate wind waves and swells using ECMWF reanalysis and KIOST WRF wind and they are compared with the KMA's operational wave model and the wave measurement data on the offshore and onshore stations. As a result, it has the lowest RMSE and the highest correlation coefficient in the onshore when the input wind fields are KMA's operational meteorological forecasts. In the offshore, all of the simulate results shows good agreements with similar error statistics. It means that it is very feasible to use SWAN model with the modified whitecapping factor and KMA's operational meteorological forecasts for predicting the wind waves and swells in the East coast of Korea.