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

Abstract

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 through cross-validation. Trajectories were affected by wind velocity and flow velocity from the starting points of drifters. Mean absolute error (MAE) and normalized cumulative Lagrangian separation (NCLS) were used to evaluate various prediction models. Radial basis function network showed the lowest MAE of 0.0556, an improvement of 35.20% over the numerical model MOHID. Long short-term memory showed the highest NCLS of 0.8762, an improvement of 6.24% over MOHID.

Keywords

• drifter trajectory
• evolutionary computation
• machine learning
• deep learning
• NCLS