An Improvement on Estimated Drifter Tracking through Machine Learning and Evolutionary Search

An Improvement on Estimated Drifter Tracking through Machine Learning and Evolutionary Search

In this study, we estimated drifter tracking over seawater using machine learning and evolutionary search techniques. The parameters used for the prediction are the hourly position of the drifter, the wind velocity, and the flow velocity of each drifter position. Our prediction model was constructed...

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Bibliographic Details
Main Authors: Yong-Wook Nam, Hwi-Yeon Cho, Do-Youn Kim, Seung-Hyun Moon, Yong-Hyuk Kim
Format: Article
Language:English
Published: MDPI AG 2020-11-01
Series:Applied Sciences
Subjects:
drifter trajectory
evolutionary computation
machine learning
deep learning
NCLS
Online Access:https://www.mdpi.com/2076-3417/10/22/8123

Internet

https://www.mdpi.com/2076-3417/10/22/8123

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