Journal of Ocean Engineering and Technology (한국해양공학회지)

Korean Society of Ocean Engineers (한국해양공학회)
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Quality Enhancement of MIROS Wave Radar Data at Ieodo Ocean Research Station Using ANN

  • Donghyun Park (Department of Convergence Study on the Ocean Science and Technology, National Korea Maritime and Ocean University) ;
  • Kideok Do (Department of Ocean Engineering, National Korea Maritime and Ocean University) ;
  • Miyoung Yun (Department of Ocean Engineering, National Korea Maritime and Ocean University) ;
  • Jin-Yong Jeong (Sea Power Enhancement Research Division, Korea Institute of Ocean Science and Technology)
  • Received : 2024.02.05
  • Accepted : 2024.05.18
  • Published : 2024.06.30
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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.

Keywords

Acknowledgement

This study was partly supported by the National Research Foundation of Korea grant funded by the Korean government (NRF-2022R1I1A306559912) and Korea Institute of Marine Science & Technology Promotion (KIMST) funded by the Ministry of Oceans and Fisheries (20210607, Establishment of the Ocean Research Station in the Jurisdiction Zone and Convergence Research).

References

  1. Datawell. (2009). Wave unit reference manual. Datawell BV Oceanographic Instruments. Netherlands, Datawell. 
  2. 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 
  3. 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 
  4. 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 
  5. 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 
  6. Korea Hydrographic and Oceanographic Agency (KHOA). (2023). Ieodo Ocean Research Station image. https://www.khoa.go.kr/ors/images/gallery/C/002.jpg 
  7. Korea Institute of Ocean Science Technology (KIOST). (2022). Construction of ocean research stations & their application studies. https://sciwatch.kiost.ac.kr/handle/2020.kiost/43879 
  8. Korea Ocean Observing and Forecasting System (KOOFS). (2023). Ocean data in grid framework. http://www.khoa.go.kr/koofs 
  9. Korea Ocean Research Station (KORS). (2022a). Equipment installation of Ieodo Ocearn Research Station. https://kors.kiost.ac.kr/ko/base/ieodo3.php 
  10. Korea Ocean Research Station (KORS). (2022b). Equipment installation of Socheongcho Ocearn Research Station. https://kors.kiost.ac.kr/ko/base/sochung3.php 
  11. Korea Ocean Research Station (KORS). (2023). Ieodo Ocean Research Station image. https://kors.kiost.ac.kr/ko/D/gallery/19/162685630082309.jpg 
  12. Lee, D., Shim, J., Park, K. (2007). Future plan for application of the Ieodo ocean research station. Proceedings of Ieodo Research Group, 1-9. https://sciwatch.kiost.ac.kr/handle/2020.kiost/30662 
  13. Min, Y., Jeong, J., Min, I., Kim, Y., Shim, J., & Do, K. (2018). Enhancement of wave radar observation data quality at the socheongcho ocean research station. Journal of Coastal Research, 85, 571-575. https://doi.org/10.2112/SI85-115.1 
  14. MIROS. (2011). Wave and current radar system handbook. Norway, MIROS. 
  15. Mun, I., Shim, J., Min, I., Choi, E. (2007). Importance of Ieodo tower in ocean science and meteorology. The 1st Proceedings of Ieodo Research, 85-91. https://sciwatch.kiost.ac.kr/handle/2020.kiost/30658 
  16. Ocean Observatories Initiative (OOI). (2012). Data product specification for spike test. 5-15. https://oceanobservatories.org/wp-content/uploads/2015/09/1341-10006_Data_Product_SPEC_SPKETST_OOI.pdf 
  17. Park, S., Shin, S., Jung, K., & Lee, B. (2021). Prediction of significant wave height in Korea strait using machine learning. Journal of Ocean Engineering and Technology, 35(5), 336-346. https://doi.org/10.26748/KSOE.2021.021 
  18. Shim, J., & Chun, I. (2004). Construction and operation of Ieodo Ocean Research Stations. The Magazine of the Korean society of Civil Engineers, 52(4) 28-36. 
  19. Shim, J., & Min, I. (2007). Construction of IEODO Ocean Research Station and its data analysis. The 1st Proceedings of Ieodo Research, 56-65. 
  20. Wei, Z. (2021). Forecasting wind waves in the US Atlantic Coast using an artificial neural network model: Towards an AI-based storm forecast system. Ocean Engineering, 237, 109646. https://doi.org/10.1016/j.oceaneng.2021.109646 
  21. Yun, M., Kim, J., & Do, K. (2022). Estimation of Wave-Breaking Index by Learning Nonlinear Relation Using Multilayer Neural Network. Journal of Marine Science and Engineering, 10(1), 50. https://doi.org/10.3390/jmse10010050