PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Gap Filling of Daily Sea Levels by Artificial Neural Networks

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.
Twórcy
autor
  • National Institute of Geophysics, Geodesy and Geography, Bulgarian Academy of Sciences, Bulgaria
  • Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Bulgaria
autor
  • Institute of System Engineering and Robotics, Bulgarian Academy of Sciences, Bulgaria
Bibliografia
  • [1] Allende, H., Moraga, C., Salas, R. 2002. Artificial neural networks in time series forecasting: A comparative analysis. Kybernetika 38 (6): 685‐707.
  • [2] 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.
  • [3] Demuth, H. & Beale, M. 1992‐2000. Neural Network Toolbox™, User’s Guide. Version 4. MathWorks, Inc.
  • [4] Gilat, A. 2011. MATLAB ‐ An Introduction with Applications, 4th Edition SI Version, Wiley.
  • [5] 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.
  • [6] Kondrashov, D. & Ghil, M. 2006. Spatio‐temporal filling of missing points in geophysical data sets. Nonlin. Processes Geophys. 13: 151‐159.
  • [7] 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.
  • [8] Lukosevicius, M. & Jaeger, H. 2009. Reservoir computing approaches to recurrent neural network training. Computer Science Review 3: 127‐149.
  • [9] 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.
  • [10] 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.
  • [11] 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.
  • [12] 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).
  • [13] Rumelhart, D. E. & McClelland, J. L. 1986. Parallel Distributed Processing, Vol. 1. Cambridge, MA: MIT Press.
  • [14] Tsai, J.‐C. & Tsai, C.‐H. 2009. Wave measurements by pressure transducers using artificial neural networks. Ocean Eng. 36 (15–16): 1149–1157.
  • [15] 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.
  • [16] Zang, N. & P. K. Behera 2012. Urban Stormwater Runoff Prediction Using Computational Intelligence Methods. Final Report. University of the District of Columbia.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8de7178a-18d3-4b80-af49-18b240cc9c35
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.