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http://dx.doi.org/10.15207/JKCS.2018.9.3.015

Correction of Drifter Data Using Recurrent Neural Networks  

Kim, Gyoung-Do (Dept. Computer Science, Kwangwoon University)
Kim, Yong-Hyuk (Dept. Computer Science, Kwangwoon University)
Publication Information
Journal of the Korea Convergence Society / v.9, no.3, 2018 , pp. 15-21 More about this Journal
Abstract
The ocean drifter is a device for observing the ocean weather by floating off the sea surface. The data observed through the drifter is utilized in the ocean weather prediction and oil spill. Observed data may contain incorrect or missing data at the time of observation, and accuracy may be lowered when we use the data. In this paper, we propose a data correction model using recurrent neural networks. We corrected data collected from 7 drifters in 2015 and 8 drifters in 2016, and conducted experiments of drifter moving prediction to reflect the correction results. Experimental results showed that observed data are corrected by 13.9% and improved the performance of the prediction model by 1.4%.
Keywords
Data Correction; RNN; Machine Learning; Prediction; Drifter;
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Times Cited By KSCI : 3  (Citation Analysis)
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