JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Real Time Current Prediction with Recurrent Neural Networks and Model Tree
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Real Time Current Prediction with Recurrent Neural Networks and Model Tree
Cini, S.; Deo, Makarand Chintamani;
  PDF(new window)
 Abstract
The prediction of ocean currents in real time over the warning times of a few hours or days is required in planning many operation-related activities in the ocean. Traditionally this is done through numerical models which are targeted toward producing spatially distributed information. This paper discusses a complementary method to do so when site-specific predictions are desired. It is based on the use of a recurrent type of neural network as well as the statistical tool of model tree. The measurements made at a site in Indian Ocean over a period of 4 years were used. The predictions were made over 72 time steps in advance. The models developed were found to be fairly accurate in terms of the selected error statistics. Among the two modeling techniques the model tree performed better showing the necessity of using distributed models for different sub-domains of data rather than a unique one over the entire input domain. Typically such predictions were associated with average errors of less than 2.0 cm/s. Although the prediction accuracy declined over longer intervals, it was still very satisfactory in terms of theselected error criteria. Similarly prediction of extreme values matched with that of the rest of predictions. Unlike past studies both east-west and north-south current components were predicted fairly well.
 Keywords
Ocean current prediction;Ocean currents;Artificial neural networks;Real time forecasting;Model tree;
 Language
English
 Cited by
 References
1.
Wasserman, P.D., 1993, Advanced Methods in Neural Computing, Van Nostrand Reinhold. New York

2.
Wu, K.K., 1994, Neural Networks and Simulation Methods, Marcel Decker. New York

3.
Witten, I.H. and Frank, E., 2000, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, Burlington, MA, USA.

4.
Quinlan J.R. 1992, Learning with continuous classes, Proceedings of Australian Joint Conference on AI, 343-348, World Scientific, Singapore.

5.
Jain P. and Deo M. C. 2006, Neural networks in ocean engineering Ships and Offshore Structures1: 1, 25-35.

6.
Deo M C. 2010.Artificial neural networks in coastal and ocean engineering. Indian Journal of Geo-Marine Sciences,39(4), Dec., 2010, 589-596.

7.
Sakhare S and M C Deo. 2009. Derivation of wave spectrum using data driven methods. Marine Structures,Elsevier, doi: 10.1016/j.marstruc.2008.12.994

8.
Jain, P, M C Deo, G Latha, V Rajendran, V Sanil Kumar.2011. Determination of Wave Spectrum with Intelligent Computing. International Journal of Ocean and Climate Systems, Multi-Science, UK, 2(2), 137-152. crossref(new window)

9.
Charhate S. B., Deo M. C. and Kumar V. S. 2007. Soft and hard computing approaches for real-time prediction of currents in a tidedominated coastal area. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment 221:4, 147-163.

10.
Huang, W. and Murray,C. 2008. Multiplestation neural network for modeling tidal currents across a coastal inlet. Hydrological Processes, 22 (8), 1136-1149 crossref(new window)

11.
Aydogan B., Ayat B., Ozturk M. N., Cevik E. O. and Yuksel Y. 2010. Current velocity forecasting in straits with artificial neural networks, a case study: Strait of Istanbul. Ocean Engineering 37, 443-453. crossref(new window)