한국해안·해양공학회논문집 (Journal of Korean Society of Coastal and Ocean Engineers)

  • 제33권6호
  • /
  • Pages.367-373
  • /
  • 2021
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  • 1976-8192(pISSN)
  • /
  • 2288-2227(eISSN)
한국해안·해양공학회 (Korean Society of Coastal and Ocean Engineers)
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딥러닝을 이용한 외해 해양기상자료로부터의 항내파고 예측

Wave Prediction in a Harbour using Deep Learning with Offshore Data

  • 이건세 ((주)대영엔지니어링 기술연구소, (주)대우조선해양 선박해양연구소) ;
  • 정동현 ((주)대영엔지니어링 기술연구소) ;
  • 문용호 ((주)대영엔지니어링 기술연구소) ;
  • 박원경 ((주)대영엔지니어링 기술연구소) ;
  • 채장원 ((주)대영엔지니어링 기술연구소)
  • 투고 : 2021.11.23
  • 심사 : 2021.12.22
  • 발행 : 2021.12.31
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초록

본 연구에서는 항내 파고를 신속하고 비교적 정확하게 예측할 수 있는 딥러닝 모델을 구축하였다.다양한 머신러닝 기법들을 외해파랑의 항내로 전파 변형 특성을 감안하여 모델에 적용하였으며 스웰로 인해 하역중단 문제가 심각했던 포항신항을 모델적용 대상지로 선정하였다. 모델의 입력 자료는 외해의 파고, 주기, 파향 그리고 출력 및 예측 자료로는 항내 파고자료로 하여 모델을 학습시켰다. 이때 자료의 전처리 과정으로 항내·외 파랑 시계열자료의 상관성을 감안하여 파향 자료를 분리하는 방법을 적용하고 딥러닝 기법을 이용하여 모델을 학습하였다. 결과적으로 모델을 통해 예측한 값이 항내관측치의 파고 시계열자료를 잘 재현하였으며 모델의 안정성을 크게 향상시켰다.

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.

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과제정보

이 논문은 2021년도 해양수산부 재원으로 해양수산과학기술진흥원의 지원을 받아 수행된 연구이다(20180404 연안침식관리 및 대응기술 실용화).

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