해양환경안전학회지 (Journal of the Korean Society of Marine Environment & Safety)

  • 제28권4호
  • /
  • Pages.469-476
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  • 2022
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  • 1229-3431(pISSN)
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  • 2287-3341(eISSN)
해양환경안전학회 (The Korean Society of Marine Environment and safety)
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Long Short-Term Memory를 이용한 부산항 조위 예측

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

  • 김해림 (부경대학교 해양산업공학(협)) ;
  • 전용호 ((주)씨엔에스솔루션) ;
  • 박재형 ((주)씨엔에스솔루션) ;
  • 윤한삼 (부경대학교 교양교육원)
  • 투고 : 2022.04.25
  • 심사 : 2022.06.27
  • 발행 : 2022.06.30
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⟨ 이전 논문 다음 논문 ⟩

초록

본 연구는 조위 관측자료를 이용하여 부산항에서의 장기 조위 자료를 생성하는 Long Short-Term Memory (LSTM)으로 구현된 순환신경망 모델을 개발하였다. 국립해양조사원의 부산 신항과 통영에서 관측된 조위 자료를 모델 입력 자료로 사용하여 부산항의 조위를 예측하였다. 모델에 대하여 2019년 1월 한 달의 학습을 수행하였으며, 이후 2019년 2월에서 2020년 1월까지 1년에 대하여 정확도를 계산하였다. 구축된 모델은 부산 신항과 통영의 조위 시계열을 함께 입력한 경우에 상관계수 0.997 및 평균 제곱근 오차 2.69 m로 가장 성능이 높았다. 본 연구 결과를 바탕으로 딥러닝 순환신경망 모델을 이용하여 임의 항만의 장기 조위 자료 예측이 가능함을 알 수 있었다.

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.

키워드

과제정보

이 논문은 부경대학교 자율창의학술연구비(2021년)에 의하여 연구되었음.

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