개인구독
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지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.6
- 수록면
- 103 - 114 (12page)
이용수
초록· 키워드
오류제보하기
Remote sensing wave observation data are crucial when analyzing ocean waves, the main external force of coastal disasters. Nevertheless, it has limitations in accuracy when used in low-wind environments. Therefore, this study collected the raw data from MIROS Wave and Current Radar (MWR) and wave radar at the Ieodo Ocean Research Station (IORS) and applied the optimal filter by combining filters provided by MIROS software. The data were validated by a comparison with South Jeju ocean buoy data. The results showed it maintained accuracy for significant wave height, but errors were observed in significant wave periods and extreme waves. Hence, this study used an artificial neural network (ANN) to improve these errors. The ANN was generalized by separating the data into training and test datasets through stratified sampling, and the optimal model structure was derived by adjusting the hyperparameters. The application of ANN effectively improved the accuracy in significant wave periods and high wave conditions. Consequently, this study reproduced past wave data by enhancing the reliability of the MWR, contributing to understanding wave generation and propagation in storm conditions, and improving the accuracy of wave prediction. On the other hand, errors persisted under high wave conditions because of wave shadow effects, necessitating more data collection and future research.
목차
ABSTRACT
1. Introduction
2. Wave Radar Observation and Verification
3. Quality Enhancement of Wave Radar Data with Artificial Neural Network
4. Conclusions
References
참고문헌 (21)
참고문헌 신청
Datawell. (2009). Wave unit reference manual. Datawell BV Oceanographic Instruments. Netherlands, Datawell.
Jeong, W., Oh, S., Ryu, K., Back, J., & Choi, I. (2018). Establish of wave information network of Korea (WINK). Journal of Korean Society of Coastal and Ocean Engineers, 30(6), 326–336. https://doi.org/10.9765/KSCOE.2018.30.6.326
Jun, H., Min, Y., Jeong, J. Y., & Do, K. (2020). Measurement and quality control of MIROS Wave Radar data at Dokdo. Journal of Korean Society of Coastal and Ocean Engineers, 32(2), 135–145. https://doi.org/10.9765/kscoe.2020.32.2.135
Kim, H., Ahn, K., & Oh, C. (2021). Estimation of significant wave heights from X-band radar based on ANN using CNN rainfall classifier. Journal of Korean Society of Coastal and Ocean Engineers, 33(3), 101–109. http://doi.org/10.9765/KSCOE.2021.33.3.101
Korea Hydrographic and Oceanographic Agency (KHOA). (2012). Korea ocean observing network analysis report. http://www.khoa.go.kr/webzine/viewer.do?pub_seq=2012023&pub_type=6&sc_type=popup
Jeong, W., Oh, S., Ryu, K., Back, J., & Choi, I. (2018). Establish of wave information network of Korea (WINK). Journal of Korean Society of Coastal and Ocean Engineers, 30(6), 326–336. https://doi.org/10.9765/KSCOE.2018.30.6.326
Jun, H., Min, Y., Jeong, J. Y., & Do, K. (2020). Measurement and quality control of MIROS Wave Radar data at Dokdo. Journal of Korean Society of Coastal and Ocean Engineers, 32(2), 135–145. https://doi.org/10.9765/kscoe.2020.32.2.135
Kim, H., Ahn, K., & Oh, C. (2021). Estimation of significant wave heights from X-band radar based on ANN using CNN rainfall classifier. Journal of Korean Society of Coastal and Ocean Engineers, 33(3), 101–109. http://doi.org/10.9765/KSCOE.2021.33.3.101
Korea Hydrographic and Oceanographic Agency (KHOA). (2012). Korea ocean observing network analysis report. http://www.khoa.go.kr/webzine/viewer.do?pub_seq=2012023&pub_type=6&sc_type=popup
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