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

Korean Society of Coastal and Ocean Engineers (한국해안·해양공학회)
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AI-Based Particle Position Prediction Near Southwestern Area of Jeju Island

AI 기법을 활용한 제주도 남서부 해역의 입자추적 예측 연구

  • Ha, Seung Yun (Oceanographic Forecast Division, Korea Hydrographic and Oceanographic Agency) ;
  • Kim, Hee Jun (Underwater Survey Technology 21) ;
  • Kwak, Gyeong Il (Underwater Survey Technology 21) ;
  • Kim, Young-Taeg (Oceanographic Forecast Division, Korea Hydrographic and Oceanographic Agency) ;
  • Yoon, Han-Sam (College of Liberal Arts, Pukyong National University)
  • Received : 2022.05.02
  • Accepted : 2022.06.13
  • Published : 2022.06.30
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Abstract

Positions of five drifting buoys deployed on August 2020 near southwestern area of Jeju Island and numerically predicted velocities were used to develop five Artificial Intelligence-based models (AI models) for the prediction of particle tracks. Five AI models consisted of three machine learning models (Extra Trees, LightGBM, and Support Vector Machine) and two deep learning models (DNN and RBFN). To evaluate the prediction accuracy for six models, the predicted positions from five AI models and one numerical model were compared with the observed positions from five drifting buoys. Three skills (MAE, RMSE, and NCLS) for the five buoys and their averaged values were calculated. DNN model showed the best prediction accuracy in MAE, RMSE, and NCLS.

본 연구는 제주도 남서부 해역의 표류체 이동 예측을 위해 2020년 8월 제주도 남서부 5개 지점에서 투하된 표층 뜰개 위치자료와 수치모델 예측자료를 학습자료로 이용한 인공지능 기반 입자추적 모델 5개를 구축하였다. 구축된 AI 기법은 기계학습 3종(Extra Trees, LightGBM, Support Vector Machine)과 딥러닝 2종(DNN, RBFN)이다. 또한 해수유동 수치모델 입자추적 예측자료 1종 및 AI 기법 입자추적 예측자료 5종을 표층 뜰개 관측자료와 비교하여 각 예측모델별 예측 정확도를 평가하였다. 6종 모델의 예측 정확도를 평가하기 위해, 5개 정점에 대한 3개 스킬량(MAE, RMSE, NCLS)의 평균값을 비교 검토하였다. 최종적인 결과로서 딥러닝 DNN 모델이 MAE, RMSE, NCLS에서 다른 모델보다 가장 우수하게 나타났다.

Keywords

References

  1. Byun, D.S., Seo, G.H., Park, S.Y., Jeong, K.Y., Lee., J.Y., Choi, Y.J., Shin, J.A. and Choi, B.Y. (2017). A Technical Guide to Operational Regional Ocean Forecasting Systems in the Korea Hydrographic and Oceanographic Agency(I): Continuous Operation Strategy, Downloading External Data, and Error Notification. Journal of Korean Society of Oceanography, 22(3), 91-105 (in Korean).
  2. Chun, B., Lee, T., Kim, S., Kim, J., Jang, K., Chun, J., Jang, W. and Shin, Y. (2020). Estimation of DNN-based Soil Moisture at Mountainous Regions. Journal of The Korean Society of Agricultural Engineers, 62(5), 93-103 (in Korean). https://doi.org/10.5389/KSAE.2020.62.5.093
  3. Geurts, P., Ernst, D. and Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3-42. https://doi.org/10.1007/s10994-006-6226-1
  4. Ha, S.Y. (2021). A study on the trajectory prediction of drifting buoys considering machine learning methods in the Korea Strait. Pukyong National Univ., Master's thesis (in Korean).
  5. Ha, S.Y., Yoon, H.S. and Kim, Y.T. (2022). A Study on the Prediction of the Surface Drifter Trajectories in the Korean Strait. Journal of Korean Society of Coastal and Ocean Engineers, 34(1), 11-18 (in Korean). https://doi.org/10.9765/KSCOE.2022.34.1.11
  6. Hahm, D., Park, S., Choi, S.H., Kang, D.J., Rho, T. and Lee, T. (2019). Estimation of Surface fCO 2 in the Southwest East Sea using Machine Learning Techniques. The Sea: Journal of the Korean Society of Oceanography, 24(3), 375-388 (in Korean).
  7. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W. and Liu, T.Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30.
  8. Kim, G.D. and Kim, Y.H. (2017). A survey on oil spill and weather forecast using machine learning based on neural networks and statistical methods. Journal of the Korea Convergence Society, 8(10), 1-8 (in Korean). https://doi.org/10.15207/JKCS.2017.8.10.001
  9. Kim, G.D. and Kim, Y.H. (2018). Correction of drifter data using recurrent neural networks. Journal of the Korea Convergence Society, 9(3), 15-21 (in Korean). https://doi.org/10.15207/JKCS.2018.9.3.015
  10. Kim, J.M. and Park, Y.T. (2012). GPS-based Noise Reduction Approach for Trajectory Recognition. J. KIISE, 39(4), 328-337 (in Korean).
  11. Kampf, J. (2009). Ocean Modelling for Beginners (using opensource software), Springer, 109.
  12. Lee, C.J., Kim, G.D. and Kim, Y.H. (2017). Performance comparison of machine learning based on neural networks and statistical methods for prediction of drifter movement. Journal of the Korea Convergence Society, 8(10), 45-52 (in Korean). https://doi.org/10.15207/JKCS.2017.8.10.045
  13. Liu, Y. and Weisberg, R.H. (2011). Evaluation of trajectory modeling in different dynamic regions using Normalized Cumulative Lagrangian Separation. Journal of Geophysical Research: Oceans. 116(C9).
  14. Seo, J.H. (2021). Prediction of Surface Drifter Trajectory Based On Feature Generation. Journal Title: Journal of The Korean Institute of Intelligent Systems, 31(4), 299-304 (in Korean).
  15. Shepard, D. (1968). A two-dimensional interpolation function for irregularly-spaced data, In Proceedings of the 1968 23rd ACM National Conference, 517-524.
  16. Sim, W.S., Sung, S.Y. and Cheng, C.K. (2009). Performance analysis of Kernel function for support vector machine. In Proceedings of the IEEK Conference. The Institute of Electronics and Information Engineers, 405-407 (in Korean).
  17. Sze, V., Chen, Y.H., Yang, T.J. and Emer, J.S. (2017). Efficient processing of deep neural networks: A tutorial and survey. Proceedings of the IEEE, 105(12), 2295-2329. https://doi.org/10.1109/JPROC.2017.2761740
  18. Vapnik, V. (2000). The nature of statistical learning theory. Springer science & business media.
  19. William, J.G. and James, A.W. (1978). Shapard's method of "metric interpolation" to bivariate and multivariate interpolation, Math. Comp., 32(141), 253-264. https://doi.org/10.1090/S0025-5718-1978-0458027-6