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http://dx.doi.org/10.9765/KSCOE.2022.34.3.72

AI-Based Particle Position Prediction Near Southwestern Area of Jeju Island  

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)
Publication Information
Journal of Korean Society of Coastal and Ocean Engineers / v.34, no.3, 2022 , pp. 72-81 More about this Journal
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.
Keywords
drifting buoy; particle tracking; machine learning; deep learning; numerical model;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
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