The Journal of Korean Institute of Information Technology (한국정보기술학회논문지)

Korean Institute of Information Technology (한국정보기술학회)
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A Study of Machine Learning Model for Prediction of Swelling Waves Occurrence on East Sea

동해안 너울성 파도 예측을 위한 머신러닝 모델 연구

  • 강동훈 (단국대학교 데이터사이언스학과) ;
  • 오세종 (단국대학교 소프트웨어학과)
  • Received : 2019.07.11
  • Accepted : 2019.08.15
  • Published : 2019.09.30
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Abstract

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

최근 들어 동해안에서 너울성 파도에 의한 손실이 빈번히 발생하고 있다. 너울성 파도는 다양한 요인들이 결합되어 발생하기 때문에 예측이 어렵다. 본 연구에서는 머신러닝 기술에 기초하여 동해안에서 너울성 파도의 발생을 예측하는 모델을 제안하였다. 모델 개발을 위해 포항 신항의 하역중단 데이터 및 신항 부근의 기압, 풍속, 풍향, 수온 등의 기상자료를 수집하였다. 수집한 데이터로부터 너울발생에 중요한 영향을 미치는 변수들을 선별하였으며, 모델 개발을 위해 다양한 머신러닝 예측 알고리즘들을 테스트 하였다. 그 결과 조위, 수온, 기압이 너울 발생 예측을 위한 주요 변수로 확인이 되었고, Random Forest 모델이 가장 우수한 성능을 보였으며. 모델의 예측 정확도는 88.6%이다.

Keywords

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