Discrete Dynamics in Nature and Society
Volume 2018 (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.
Linked References
- T. M. Özgökmen, L. I. Piterbarg, A. J. Mariano, and E. H. Ryan, “Predictability of drifter trajectories in the tropical Pacific Ocean,” Journal of Physical Oceanography, vol. 31, no. 9, pp. 2691–2720, 2001. View at Publisher · View at Google Scholar · View at Scopus
- S. Castellari, A. Griffa, T. M. Özgökmen, and P.-M. Poulain, “Prediction of particle trajectories in the adriatic sea using Lagrangian data assimilation,” Journal of Marine Systems, vol. 29, no. 1-4, pp. 33–50, 2001. View at Publisher · View at Google Scholar · View at Scopus
- K. R. Thompson, J. Sheng, P. C. Smith, and L. Cong, “Prediction of surface currents and drifter trajectories on the inner Scotian shelf,” Journal of Geophysical Research: Oceans, vol. 108, no. 9, pp. 3–1, 2003. View at Google Scholar · View at Scopus
- C. D. Winant, D. J. Alden, E. P. Dever, K. A. Edwards, and M. C. Hendershott, “Near-surface trajectories off central and southern California,” Journal of Geophysical Research: Oceans, vol. 104, no. 7, Article ID 1999JC900083, pp. 15713–15726, 1999. View at Publisher · View at Google Scholar · View at Scopus
- The Global Drifter Program: What's a drifter?, http://www.aoml.noaa.gov/phod/dac/gdp_drifter.php. View at Publisher · View at Google Scholar
- ARA Consulting Technology, URL, http://www.aracnt.com/.
- Earth:: a global map of wind, weather and ocean condition, https://earth.nullschool.net/.
- OpenWeatherMap, https://openweathermap.org/api.
- https://www.nefsc.noaa.gov/press_release/2012/SciSpot/SS1211/.
- C. M. Allen, “Numerical simulation of contaminant dispersion in estuary flows,” Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol. 381, pp. 179–194. View at Publisher · View at Google Scholar
- C.-Y. Yang, Y.-F. Liaw, C.-M. Chu, and I.-S. Sheen, “White count, pH and lactate in ascites in the diagnosis of spontaneous bacterial peritonitis,” Hepatology, vol. 5, no. 1, pp. 85–90, 1985. View at Publisher · View at Google Scholar · View at Scopus
- K. Price, R. M. Storn, and J. A. Lampinen, Differential Evolution: a Practical Approach to Global Optimization, Springer Science & Business Media, 2006.
- J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948. View at Scopus
- L. Lamberti, “An efficient simulated annealing algorithm for design optimization of truss structures,” Computers & Structures, vol. 86, no. 19-20, pp. 1936–1953, 2008. View at Publisher · View at Google Scholar · View at Scopus
- N. Hansen, S. D. Müller, and P. Koumoutsakos, “Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES),” Evolutionary Computation, vol. 11, no. 1, pp. 1–18, 2003. View at Publisher · View at Google Scholar · View at Scopus
- The CMA Evolution Strategy, https://www.lri.fr/~hansen/cmaesintro.html.
- Y. Liu, X. Yao, and T. Higuchi, “Evolutionary ensembles with negative correlation learning,” IEEE Transactions on Evolutionary Computation, vol. 4, no. 4, pp. 380–387, 2000. View at Publisher · View at Google Scholar · View at Scopus
- P. Refaeilzadeh, L. Tang, and H. Liu, “Cross-validation,” in Encyclopedia of Database Systems, pp. 532–538, Springer, USA, 2009. View at Google Scholar
- Y. Liu and R. H. Weisberg, “Evaluation of trajectory modeling in different dynamic regions using normalized cumulative Lagrangian separation,” Journal of Geophysical Research: Oceans, vol. 116, no. 9, Article ID C09013, 2011. View at Publisher · View at Google Scholar · View at Scopus
- R. Sorgente, C. Tedesco, F. Pessini et al., “Forecast of drifter trajectories using a Rapid Environmental Assessment based on CTD observations,” Deep-Sea Research Part II: Topical Studies in Oceanography, vol. 133, pp. 39–53, 2016. View at Publisher · View at Google Scholar · View at Scopus
- Y. Liu, R. H. Weisberg, S. Vignudelli, and G. T. Mitchum, “Evaluation of altimetry-derived surface current products using Lagrangian drifter trajectories in the eastern Gulf of Mexico,” Journal of Geophysical Research: Oceans, vol. 119, no. 5, pp. 2827–2842, 2014. View at Publisher · View at Google Scholar · View at Scopus
- Differential Evolution, http://www1.icsi.berkeley.edu/~storn/code.html. View at Publisher · View at Google Scholar
- CMA-ES Source Code, https://www.lri.fr/~hansen/cmaes_inmatlab.html.
- Particle Swarm Optimization (PSO) in C, https://github.com/kkentzo/pso.
- L. I. Kuncheva and J. J. Rodríguez, “A weighted voting framework for classifiers ensembles,” Knowledge and Information Systems, vol. 38, no. 2, pp. 259–275, 2014. View at Publisher · View at Google Scholar · View at Scopus