Browse > Article
http://dx.doi.org/10.15207/JKCS.2017.8.10.045

Performance Comparison of Machine Learning Based on Neural Networks and Statistical Methods for Prediction of Drifter Movement  

Lee, Chan-Jae (Dept. Computer Science, Kwangwoon University)
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.8, no.10, 2017 , pp. 45-52 More about this Journal
Abstract
Drifter is an equipment for observing the characteristics of seawater in the ocean, and it can be used to predict effluent oil diffusion and to observe ocean currents. In this paper, we design models or the prediction of drifter trajectory using machine learning. We propose methods for estimating the trajectory of drifter using support vector regression, radial basis function network, Gaussian process, multilayer perceptron, and recurrent neural network. When the propose mothods were compared with the existing MOHID numerical model, performance was improve on three of the four cases. In particular, LSTM, the best performed method, showed the imporvement by 47.59% Future work will improve the accuracy by weighting using bagging and boosting.
Keywords
Oil Spill; Drifter; Machine learning; Recurrent neural network; LSTM; Prediction;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Yoo, C. S., and Park, J. Y. "Combining radar and rain gauge observations utilizing Gaussian-process basedregression and support vector learning." Journal of Korean Institute of Intelligent Systems Vol. 18, No. 3, pp.297-305, 2008.   DOI
2 Dalto, M., Vasak, M., Baotic, M., Matusko, J., and Horvath, K. "Neural-network-based ultra-short-term wind forecasting." European Wind Energy Association 2014 Annual Event 2014.
3 Baruque, B., Corchado, E., Mata, A., and Corchado, J. M. "forecasting solution to the oil spill problem based on a hybrid intelligent system." Information Sciences Vol. 180, No.10, pp.2029-2043, 2010.   DOI
4 Ramedani, Z., Omid, M., Keyhani, A., Shamshirband, S. and Khoshnevisan, B. "Potential of radial basis function based support vector regression for global solar radiation prediction." Renewable and Sustainable Energy Reviews 39, pp.1005-1011, 2014.   DOI
5 Basak, D., Pal, S., and Patranabis, D. C. "Support vector regression." Neural Information Processing-Letters and Reviews Vol. 11, No. 10, pp.203-224, 2007.
6 Orr, M. J. Introduction to Radial Basis Function Networks, Institute for Adaptive and Neural Computation, Edinburgh Univ 1996.
7 Rasmussen, C. E., and Williams, C. K. Gaussian Processes for Machine Learning. Vol. 1. MIT press 2006.
8 Gardner, M. W., and Dorling, S. R. "Artificial neural networks (the multilayer perceptron)-a review of applications in the atmospheric sciences." Atmospheric Environment Vol. 32, No. 14, pp.2627-2636, 1998.   DOI
9 Mikolov, Tomas, et al. "Recurrent neural network based language model." Interspeech. Vol. 2. 2010.
10 Sak, Hasim, Andrew Senior, and Francoise Beaufays. "Long short-term memory recurrent neural network architectures for large scale acoustic modeling." Fifteenth Annual Conference of the International Speech Communication Association. 2014.
11 Liu, Y., and Weisberg, R. H. "Evaluation of trajectory modeling in different dynamic regions using normalized cumulative Lagrangian separation." Journal of Geophysical Research: Oceans Vol. 116, No.C9, 2011.
12 Y. D. Yun, Y. Wook. Yang, H. S. Ji, H. S. Lim, "Development of Smart Senior Classification Model based on Activity Profile Using Machine Learning Method", Journal of the Korea Convergence Society, Vol. 8, No. 1, pp.25-34, 2017.   DOI
13 Davis, R. E. "Drifter observations of coastal surface currents during CODE: the statistical and dynamical views." Journal of Geophysical Research: Oceans Vol. 90, No. C3, pp.4756-4772, 1985.   DOI
14 Fratantoni, D. M. "North Atlantic surface circulation during the 1990's observed with satellite-tracked drifters." Journal of Geophysical Research: Oceans Vol. 106, No. C10, pp.22067-22093, 2001.   DOI
15 MOHID, Water Modeling System, http://www.mohid.com
16 Matkan, A. A., M. Hajeb, and Z. Azarakhsh. "Oil spill detection from SAR image using SVM based classification." ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Vol. 1. No. 3, pp.55-60, 2013.