HomePage
 




 


 

ISSN 2083-6473
ISSN 2083-6481 (electronic version)
 

 

 

Editor-in-Chief

Associate Editor
Tomasz Neumann
 

Published by
TransNav, Faculty of Navigation
Gdynia Maritime University
3, John Paul II Avenue
81-345 Gdynia, POLAND
www http://www.transnav.eu
e-mail transnav@am.gdynia.pl
Gap Filling of Daily Sea Levels by Artificial Neural Networks
1 Bulgarian Academy of Sciences, Sofia, Bulgaria
ABSTRACT: In the recent years, intelligent methods as artificial neural networks are successfully applied for data analysis from different fields of the geosciences. One of the encountered practical problems is the availability of gaps in the time series that prevent their comprehensive usage for the scientific and practical purposes. The article briefly describes two types of the artificial neural network (ANN) architectures - Feed-Forward Backpropagation (FFBP) and recurrent Echo state network (ESN). In some cases, the ANN can be used as an alternative on the traditional methods, to fill in missing values in the time series. We have been conducted several experiments to fill the missing values of daily sea levels spanning a 5-years period using both ANN architectures. A multiple linear regression for the same purpose has been also applied. The sea level data are derived from the records of the tide gauge Burgas, which is located on the western Black Sea coast. The achieved results have shown that the performance of ANN models is better than that of the classical one and they are very promising for the real-time interpolation of missing data in the time series.
REFERENCES
Allende, H., Moraga, C., Salas, R. 2002. Artificial neural networks in time series forecasting: A comparative analysis. Kybernetika 38 (6): 685-707.
Dergachev, V.A., N. G. Makarenko, L. N. Karimova, and E. B. Danilkina. 2001. Nonlinear methods of analysis of data with gaps. Geochronometria Vol. 20: 45-50.
Demuth, H. & Beale, M. 1992-2000. Neural Network Toolbox™, User’s Guide. Version 4. MathWorks, Inc.
Gilat, A. 2011. MATLAB - An Introduction with Applications, 4th Edition SI Version, Wiley.
Jaeger, H. 2003. Adaptive nonlinear system identification with echo state networks. Advances in Neural Information Processing Systems, 15 (NIPS 2002), MIT Press, Cambridge, MA, 593-600.
Kondrashov, D. & Ghil, M. 2006. Spatio-temporal filling of missing points in geophysical data sets. Nonlin. Processes Geophys. 13: 151-159.
Koprinkova-Hristova, P., Hadjiski, M., Doukovska, L., Beloreshki, S. 2011. Recurrent neural networks for predictive maintenance of mill fan systems. International Journal of Electronics and Telecommunications vol. 57 (3): 401-406.
Lukosevicius, M. & Jaeger, H. 2009. Reservoir computing approaches to recurrent neural network training. Computer Science Review 3: 127-149.
Moffat, A. M., Papale, D., Reichstein, M., Hollinger, D. Y., Richardson, A. D., Barr, A. G., Beckstein, C., Braswell, B. H., Churkina, G., Desai, A. R., Falge, E., Gove, J. H. Heimann, M., Hui, D., Jarvis, A. J., Kattge, J., Noormets, A., Stauch, V. J. 2007.Comprehensive comparison of gap filling techniques for eddy covariance net carbon fluxes. Agric. Forest Meteorol. 147: 209–232.
Musial, J. P., Verstraete, M. M., Gobron, N. 2011. Comparing the effectiveness of recent algorithms to fill and smooth incomplete and noisy time series. Atmos. Chem. Phys. Discuss. 11: 14259–14308.
Pashova, L. & Popova, S. 2011. Daily sea level forecast at tide gauge Burgas, Bulgaria using artificial neural networks. Journal of Sea Research 66: 154–161.
Pashova, L., Koprinkova-Hristova, P., Popova, S. 2012. An application of intelligent methods for geodetic data processing and analysis, In: Proceedings of the International jubilee scientific conference UACEG’2012, 15-17 November 2012, (in Bulgarian).
Rumelhart, D. E. & McClelland, J. L. 1986. Parallel Distributed Processing, Vol. 1. Cambridge, MA: MIT Press.
Tsai, J.-C. & Tsai, C.-H. 2009. Wave measurements by pressure transducers using artificial neural networks. Ocean Eng. 36 (15–16): 1149–1157.
Wenzel, M. & Schröter, J. 2010. Reconstruction of regional mean sea level anomalies from tide gauges using neural networks. Journal of Geophysical Research - Oceans 115, C08013, DOI: 10.1029/2009JC005630.
Zang, N. & P. K. Behera 2012. Urban Stormwater Runoff Prediction Using Computational Intelligence Methods. Final Report. University of the District of Columbia.
Citation note:
Pashova L., Koprinkova-Hristova P., Popova S.: Gap Filling of Daily Sea Levels by Artificial Neural Networks. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 7, No. 2, doi:10.12716/1001.07.02.10, pp. 225-232, 2013

File downloaded 925 times








Important: TransNav.eu cookie usage
The TransNav.eu website uses certain cookies. A cookie is a text-only string of information that the TransNav.EU website transfers to the cookie file of the browser on your computer. Cookies allow the TransNav.eu website to perform properly and remember your browsing history. Cookies also help a website to arrange content to match your preferred interests more quickly. Cookies alone cannot be used to identify you.
Akceptuję pliki cookies z tej strony