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http://dx.doi.org/10.9765/KSCOE.2022.34.1.11

A Study on the Prediction of the Surface Drifter Trajectories in the Korean Strait  

Ha, Seung Yun (Oceanographic Forecast Division, Korea Hydrographic and Oceanographic Agency)
Yoon, Han-Sam (Department of Ecological Engineering, Pukyong National University)
Kim, Young-Taeg (Oceanographic Forecast Division, Korea Hydrographic and Oceanographic Agency)
Publication Information
Journal of Korean Society of Coastal and Ocean Engineers / v.34, no.1, 2022 , pp. 11-18 More about this Journal
Abstract
In order to improve the accuracy of particle tracking prediction techniques near the Korean Strait, this study compared and analyzed a particle tracking model based on a seawater flow numerical model and a machine learning based on a particle tracking model using field observation data. The data used in the study were the surface drifter buoy movement trajectory data observed in the Korea Strait, prediction data by machine learning (linear regression, decision tree) using the tide and wind data from three observation stations (Gageo Island, Geoje Island, Gyoboncho), and prediciton data by numerical models (ROMS, MOHID). The above three data were compared through three error evaluation methods (Correlation Coefficient (CC), Root Mean Square Errors (RMSE), and Normalized Cumulative Lagrangian Separation (NCLS)). As a final result, the decision tree model had the best prediction accuracy in CC and RMSE, and the MOHID model had the best prediction results in NCLS.
Keywords
Korea Strait; drifting buoy; particle tracking; machine learning; numerical model;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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