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Discrete Dynamics in Nature and Society
Volume 2018, Article ID 6848745, 15 pages
https://doi.org/10.1155/2018/6848745
Research Article

Prediction of Drifter Trajectory Using Evolutionary Computation

Department of Computer Science, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Republic of Korea

Correspondence should be addressed to Yong-Hyuk Kim; rk.ca.wk@ylfdhy

Received 11 September 2017; Revised 6 November 2017; Accepted 19 December 2017; Published 24 January 2018

Academic Editor: Alicia Cordero

Copyright © 2018 Yong-Wook Nam and Yong-Hyuk Kim. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We used evolutionary computation to predict the trajectory of surface drifters. The data used to create the predictive model comprise the hourly position of the drifters, the flow and wind velocity at the location, and the location predicted by the MOHID model. In contrast to existing numerical models that use the Lagrangian method, we used an optimization algorithm to predict the trajectory. As the evaluation measure, a method that gives a better score as the Mean Absolute Error (MAE) when the difference between the predicted position in time and the actual position is lower and the Normalized Cumulative Lagrangian Separation (NCLS), which is widely used as a trajectory evaluation method of drifters, were used. The evolutionary methods Differential Evolution (DE), Particle Swarm Optimization (PSO), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), and ensembles of the above were used, with the DE&PSO ensemble found to be the best prediction model. Considering our objective to find a parameter that minimizes the fitness function to identify the average of the difference between the predictive change and the actual change, this model yielded better results than the existing numerical model in three of the four cases used for the test data, at an average of 19.36% for MAE and 5.96% for NCLS. Thus, the model using the fitness function set in this study showed improved results in NCLS and thus shows that NCLS can be used sufficiently in the evaluation system.