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  • 국립 부경대학교의 다양한 모습과 소식을 접하시면 부경대학교가 한번 더 가까워집니다.
작성자,작성일,첨부파일,조회수로 작성된 표
AI에 풍속 더해 해양 파고 예측 정확도↑ (AI-Powered Wave Forecasting Becomes More Accurate by Incorporating Wind Speed Data)
작성자 대외홍보센터 작성일 2026-07-20
조회수 485
작성자,작성일,첨부파일,조회수로 작성된 표
AI에 풍속 더해 해양 파고 예측 정확도↑ (AI-Powered Wave Forecasting Becomes More Accurate by Incorporating Wind Speed Data)
대외홍보센터 2026-07-20 485

국립부경대 연구팀, 풍속 정보 융합한 AI 모델로 장기 해양 파고 예측 정확도 높여
- 해양 안전·스마트 해양 분야 활용 기대

△ 왼쪽부터 강나윤 박사과정생, 장원두, 양영준 교수.


국립부경대학교 연구팀이 X밴드 해상 레이더 영상과 풍속 정보를 융합한 인공지능(AI) 기반 모델을 개발해 장기 데이터 환경에서도 유의파고(Significant Wave Height, SWH) 추정 정확도를 높이는 데 성공했다.

국립부경대 컴퓨터·인공지능공학부 강나윤 박사과정생과 장원두 교수, 동명대 조선해양공학과 양영준 교수 연구팀은 X밴드 해상 레이더 영상과 풍속 정보를 결합한 딥러닝 모델 ‘SWH-WindNet’을 개발하고, 장기간 수집된 관측 데이터를 활용해 유의파고 추정 성능을 분석했다.

연구 결과, 제안한 모델은 레이더 영상만 활용한 기존 모델보다 높은 상관계수와 낮은 RMSE(평균 제곱근 오차)를 기록하며 우수한 예측 성능을 보였다. 특히 풍속 정보를 외부 환경 변수로 융합한 결과 평균 상관계수 0.9461, RMSE 0.3887m를 달성해, 다양한 최신 2D·3D CNN 및 트랜스포머 기반 모델과의 비교에서도 뛰어난 성능을 나타냈다.

또한 연구팀은 속초 해변에서 1년간 수집한 장기 관측 데이터를 활용해 계절별 성능을 평가한 결과, 기존 모델은 계절 변화에 따라 예측 성능이 크게 달라지는 반면 제안한 모델은 모든 계절에서 안정적인 성능을 유지하는 것을 확인했다. 특히 겨울철에는 RMSE가 약 11.8% 개선되는 등 풍속 정보를 결합한 방식이 계절적 환경 변화에 따른 성능 저하를 효과적으로 완화하는 것으로 나타났다.

이번 연구는 장기 해양관측 데이터를 활용해 레이더 기반 유의파고 추정 모델의 일반화 가능성을 검증하고, 풍속 정보를 효과적으로 융합하는 인공지능 모델을 제시한 성과다. 향후 항로 계획, 선박 교통관제시스템(VTS), 자율운항선박, 장기 해양 모니터링 시스템 등 해양 안전과 스마트 해양 분야의 핵심 기반 기술로 활용될 것으로 기대된다.

이 연구 성과를 담은 논문 ‘Significant Wave Height Estimation Using X-band Radar Imagery with Wind Fusion: Performance Enhancement in Long-Term Data Environments’는 엘스비어(Elsevier)의 해양공학 분야 SCIE 국제학술지 ‘Ocean Engineering(IF 6.3, JCR Q1)에 지난 6월 온라인 게재됐다. <부경투데이>

 


 

Pukyong National University Research Team Improves Long-Term Ocean Wave Height Prediction Accuracy with an AI Model Integrating Wind Speed Data

― Enhanced long-term wave forecasting expected to support marine safety and smart ocean technologies

 

A research team at Pukyong National University has successfully improved the accuracy of Significant Wave Height (SWH) estimation under long-term data conditions by developing an artificial intelligence (AI)-based model that integrates X-band marine radar imagery with wind speed data.

 

The research team, led by Na-Yoon Kang, a doctoral student in the Department of Computer and Artificial Intelligence Engineering, and Professor Won-Doo Jang of Pukyong National University, in collaboration with Professor Young-Jun Yang of the Department of Naval Architecture and Ocean Engineering at Tongmyong University, developed a deep learning model named SWH-WindNet. The team evaluated the model’s performance in estimating significant wave height using long-term observational datasets that combine X-band marine radar imagery with wind speed information.

 

The proposed model demonstrated superior predictive performance compared with existing models that use radar imagery alone, recording a higher correlation coefficient and a lower root mean square error (RMSE). By incorporating wind speed as an external environmental variable, the model achieved an average correlation coefficient of 0.9461 and an RMSE of 0.3887 meters, outperforming a range of state-of-the-art 2D and 3D convolutional neural network (CNN) and transformer-based models.

 

The research team also evaluated the model’s seasonal performance using a full year of observational data collected at Sokcho Beach. While the performance of existing models varied significantly across seasons, the proposed model maintained stable prediction accuracy throughout the year. In winter, in particular, the model reduced RMSE by approximately 11.8 percent, demonstrating that the integration of wind speed data effectively mitigates performance degradation caused by seasonal environmental changes.

 

This study verified the generalizability of a radar-based significant wave height estimation model using long-term marine observation data and introduced an AI model that effectively integrates wind speed information. The technology is expected to serve as a key foundation for marine safety and smart ocean applications, including route planning, vessel traffic service (VTS) systems, autonomous ships, and long-term ocean monitoring systems.

 

The paper presenting the findings, titled “Significant Wave Height Estimation Using X-band Radar Imagery with Wind Fusion: Performance Enhancement in Long-Term Data Environments,” was published online in June in Ocean Engineering (IF 6.3, JCR Q1), an SCIE-indexed international journal in the field of ocean engineering published by Elsevier. <Pukyong Today>

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