Quality Enhancement of MIROS Wave Radar Data at Ieodo Ocean Research Station Using ANN SCOPUS KCI
DC Field | Value | Language |
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dc.contributor.author | Park, Donghyun | - |
dc.contributor.author | Do, Kideok | - |
dc.contributor.author | Yun, Miyoung | - |
dc.contributor.author | Jeong, Jin Yong | - |
dc.date.accessioned | 2024-08-05T01:30:00Z | - |
dc.date.available | 2024-08-05T01:30:00Z | - |
dc.date.created | 2024-08-05 | - |
dc.date.issued | 2024-06 | - |
dc.identifier.issn | 1225-0767 | - |
dc.identifier.uri | https://sciwatch.kiost.ac.kr/handle/2020.kiost/45825 | - |
dc.description.abstract | 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. | - |
dc.description.uri | 3 | - |
dc.language | English | - |
dc.publisher | 한국해양공학회 | - |
dc.title | Quality Enhancement of MIROS Wave Radar Data at Ieodo Ocean Research Station Using ANN | - |
dc.type | Article | - |
dc.citation.endPage | 114 | - |
dc.citation.startPage | 103 | - |
dc.citation.title | Journal of Ocean Engineering and Technology | - |
dc.citation.volume | 38 | - |
dc.citation.number | 3 | - |
dc.contributor.alternativeName | 정진용 | - |
dc.identifier.bibliographicCitation | Journal of Ocean Engineering and Technology, v.38, no.3, pp.103 - 114 | - |
dc.identifier.doi | 10.26748/KSOE.2024.045 | - |
dc.identifier.scopusid | 2-s2.0-85199774110 | - |
dc.type.docType | Article | - |
dc.description.journalClass | 3 | - |
dc.description.isOpenAccess | Y | - |
dc.subject.keywordAuthor | Stratified sampling | - |
dc.subject.keywordAuthor | Wave and current radar | - |
dc.subject.keywordAuthor | Artificial neural network | - |
dc.subject.keywordAuthor | Ieodo Ocean Research Station | - |
dc.subject.keywordAuthor | Quality control | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |