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

  • 제33권3호
  • /
  • Pages.101-109
  • /
  • 2021
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  • 1976-8192(pISSN)
  • /
  • 2288-2227(eISSN)
한국해안·해양공학회 (Korean Society of Coastal and Ocean Engineers)
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CNN 강우여부 분류기를 적용한 ANN 기반 X-Band 레이다 유의파고 보정

Estimation of Significant Wave Heights from X-Band Radar Based on ANN Using CNN Rainfall Classifier

  • 김희연 (한동대학교 공간설계공학과) ;
  • 안경모 (한동대학교 공간환경시스템공학부) ;
  • 오찬영 (한동대학교 건설환경연구소)
  • Kim, Heeyeon (Department of Spatial Design & Engineering, Handong Global University) ;
  • Ahn, Kyungmo (School of Spatial Environment System Engineering, Handong Global University) ;
  • Oh, Chanyeong (Institute of Construction & Environmental Research, Handong Global University)
  • 투고 : 2021.06.09
  • 심사 : 2021.06.14
  • 발행 : 2021.06.30
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초록

항해용 X-band 레이다를 이용한 파랑관측은 해수면에 후방산란 된 전자기파 이미지를 분석하여 이루어진다. 1분당 42개의 해수면 시계열 이미지로부터 3차원 FFT를 계산하고 변조전달함수(Modulation Transfer Function)를 구하여 파랑정보를 추출한다. 따라서 레이다 파고계로 계측한 유의파고의 정확도는 X-band 레이다 영상의 상태에 따라 결정된다. 2020년 여름 태풍 마이삭과 하이선 내습 시 강릉 안인 해안에 설치된 X-band 레이다 파고계로 관측한 유의파고의 오차가 크게 발생하였다. 이는 태풍 내습 시 급격히 유의파고가 증가하는 한편 강한 강우가 동반되어 X-band 레이다 영상의 품질이 저하되었기 때문이다. 최대 오차 발생 이전까지 많은 강우가 있었음이 확인된다. 본 연구에서는 convolution neural network(CNN)을 이용하여 레이다 이미지로부터 강우 여부를 분류하고 강우여부에 따라 강우시 인공신경망 모델을 적용하여 태풍 시 유의파고 관측 정확도를 향상시켰다. 폭우를 동반한 태풍 시 레이다 자료 특성에 기반하여 인공신경망 유의파고 산출 알고리즘을 개선하고 이를 통해 X-band 레이다 파고계의 정확도를 향상시키는 방법을 제시하였다.

Wave observations using a marine X-band radar are conducted by analyzing the backscattered radar signal from sea surfaces. Wave parameters are extracted using Modulation Transfer Function obtained from 3D wave number and frequency spectra which are calculated by 3D FFT of time series of sea surface images (42 images per minute). The accuracy of estimation of the significant wave height is, therefore, critically dependent on the quality of radar images. Wave observations during Typhoon Maysak and Haishen in the summer of 2020 show large errors in the estimation of the significant wave heights. It is because of the deteriorated radar images due to raindrops falling on the sea surface. This paper presents the algorithm developed to increase the accuracy of wave heights estimation from radar images by adopting convolution neural network(CNN) which automatically classify radar images into rain and non-rain cases. Then, an algorithm for deriving the Hs is proposed by creating different ANN models and selectively applying them according to the rain or non-rain cases. The developed algorithm applied to heavy rain cases during typhoons and showed critically improved results.

키워드

과제정보

본 연구는 해양수산부의 해양과학조사 및 예보 기술사업의 일환인 "연안이상현상(이상고파, 이안류) 발생원인 규명 및 대응체계 구축"(NO.20140057) 연구비 지원으로 수행되었습니다.

참고문헌

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