Advertisement

Computational Geosciences

, Volume 14, Issue 1, pp 55–64 | Cite as

An improved methodology for filling missing values in spatiotemporal climate data set

Application to Tanganyika Lake data set
  • Antti SorjamaaEmail author
  • Amaury Lendasse
  • Yves Cornet
  • Eric Deleersnijder
Original paper

Abstract

In this paper, an improved methodology for the determination of missing values in a spatiotemporal database is presented. This methodology performs denoising projection in order to accurately fill the missing values in the database. The improved methodology is called empirical orthogonal functions (EOF) pruning, and it is based on an original linear projection method called empirical orthogonal functions (EOF). The experiments demonstrate the performance of the improved methodology and present a comparison with the original EOF and with a widely used optimal interpolation method called objective analysis.

Keywords

Missing value problem Empirical orthogonal functions EOF Selection of singular values Tanganyika Lake 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Tangang, F.T., Tang, B., Monahan, A.H., Hsieh, W.W.: Forecasting enso events: a neural network—extended eof approach. J. Climate 11, 29–41 (1998)CrossRefGoogle Scholar
  2. 2.
    Wackernagel, H.: Multivariate Geostatistics—An Introduction with Applications. Springer, Berlin (1995)zbMATHGoogle Scholar
  3. 3.
    Preisendorfer, R.: Principal Component Analysis in Meteorology and Oceanography. Elsevier, Amsterdam (1988)Google Scholar
  4. 4.
    Beckers, J.M., Rixen, M.: Eof calculations and data filling from incomplete oceanographic datasets. J. Atmos. Ocean. Technol. 20(12), 1839–1856 (2003)CrossRefGoogle Scholar
  5. 5.
    Boyd, J., Kennelly, E., Pistek, P.: Estimation of eof expansion coefficients from incomplete data. Deep Sea Res. 41, 1479–1488 (1994)CrossRefGoogle Scholar
  6. 6.
    Alvera-Azcarate, A., Barth, A., Rixen, M., Beckers, J.M.: Reconstruction of incomplete oceanographic data sets using empirical orthogonal functions. Application to the adriatic sea. Ocean Model. 9, 325–346 (2005)CrossRefGoogle Scholar
  7. 7.
    Alvera-Azcarate, A., Barth, A., Beckers, J.M., Weisberg, R.H.: Multivariate reconstruction of missing data in sea surface temperature, chlorophyll and wind satellite fields. J. Geophys. Res. 112, C03008 (2007)CrossRefGoogle Scholar
  8. 8.
    Beckers, J.-M., Barth, A., Alvera-Azcarate, A.: Dineof reconstruction of clouded images including error maps. Application to the sea surface temperature around Corsican Island. Ocean Sci. 2(2), 183–199 (2006)CrossRefGoogle Scholar
  9. 9.
    Lendasse, A., Wertz, V., Verleysen, M.: Model selection with cross-validations and bootstraps—application to time series prediction with rbfn models. In: LNCS, no. 2714, pp. 573–580. Springer, Berlin (2003)Google Scholar
  10. 10.
    Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, vol. 2, Montreal, 20–25 August 1995Google Scholar
  11. 11.
    Efron, B., Tibshirani, R.J.: An Introduction to the Bootstrap. Chapman and Hall, London (1993)zbMATHGoogle Scholar
  12. 12.
    Efron, B., Tibshirani, R.J.: Improvements on cross-validation: the .632+ bootstrap method. J. Am. Stat. Assoc. 92, 548–560 (1997)zbMATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Sorjamaa, A., Hao, J., Reyhani, N., Ji, Y., Lendasse, A.: Methodology for long-term prediction of time series. Neurocomputing 70(16–18), 2861–2869 (2007)CrossRefGoogle Scholar
  14. 14.
    Gandin, L.S.: Objective Analysis of Meteorological Fields, p. 242. Israel Program for Scientific Translations, Jerusalem (1969)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • Antti Sorjamaa
    • 1
    Email author
  • Amaury Lendasse
    • 1
  • Yves Cornet
    • 2
  • Eric Deleersnijder
    • 3
  1. 1.Adaptive Informatics Research CentreHelsinki University of TechnologyEspooFinland
  2. 2.Unit of GeomaticsUniversity of LiegeLiegeBelgium
  3. 3.Louvain School of Engineering, Centre for Systems Engineering and Applied Mechanics (CESAME)Universite catholique de LouvainLouvain-la-NeuveBelgium

Personalised recommendations