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COMPSTAT pp 401–406Cite as

Data Imputation and Nowcasting in the Environmental Sciences Using Clustering and Connectionist Modelling

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Abstract

We discuss data fusion in the context of measured sea surface temperature data, wind stress and radiation budget data, topographic feature information, and output of physical oceanographic models. Our immediate set of objectives are data imputation and feature selection. Our longer term goal is nowcasting and forecasting of oceanic upwelling.

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References

  • Aussem, A., Murtagh, F. & Sarazin, M. (1995). Dynamical recurrent neural networks — towards environmental time series prediction. International Journal of Neural Networks, 6, 145–170.

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Author information

Authors and Affiliations

  1. Faculty of Informatics, University of Ulster, BT48 7JL, L’Derry, Northern Ireland

    F. Murtagh, G. Zheng & J. Campbell

  2. Clermont-Ferrand II, I.S.I.M.A., Université Blaise Pascal, Campus des Cezeaux, B.P. 125, 63173, Aubière Cedex, France

    A. Aussem

  3. Space Applications Institute, Marine Environment Unit, Joint Research Centre, 21020, Ispra (Va), Italy

    M. Ouberdous, E. Demirov & W. Eifler

  4. Laboratoire d’Océanographie Dynamique et de Climatologie, Université Pierre et Marie Curie, 75252, Paris Cedex 05, France

    M. Crépon

Authors
  1. F. Murtagh
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  2. G. Zheng
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  3. J. Campbell
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  4. A. Aussem
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  5. M. Ouberdous
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  6. E. Demirov
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  7. W. Eifler
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  8. M. Crépon
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Editor information

Editors and Affiliations

  1. Statistics Department, IACR-Rothamsted, AL5 2JQ, Harpenden, Herts, UK

    Roger Payne

  2. Department of Mathematics, University of Bristol, BS8 1TW, Bristol, UK

    Peter Green

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© 1998 Springer-Verlag Berlin Heidelberg

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Murtagh, F. et al. (1998). Data Imputation and Nowcasting in the Environmental Sciences Using Clustering and Connectionist Modelling. In: Payne, R., Green, P. (eds) COMPSTAT. Physica, Heidelberg. https://doi.org/10.1007/978-3-662-01131-7_55

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  • DOI: https://doi.org/10.1007/978-3-662-01131-7_55

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-1131-5

  • Online ISBN: 978-3-662-01131-7

  • eBook Packages: Springer Book Archive

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