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An IBR System to Quantify the Ocean’s Carbon Dioxide Budget

  • Juan M. Corchado
  • Emilio S. Corchado
  • Jim Aiken
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3275)

Abstract

The interaction of the atmosphere and the ocean has a profound effect on climate, while the uptake by the oceans of a major fraction of atmospheric carbon dioxide has a moderating influence. By improving accuracy in the quantification of the ocean’s carbon dioxide budget, a more precise estimation can be made of the terrestrial fraction of global carbon dioxide budget and its subsequent effect on climate change. First steps have been taken towards this from an environmental and economic point of view, by using an instance based reasoning system, which incorporates a novel clustering and retrieval method. This paper reviews the problems of measuring the ocean’s carbon dioxide budget and presents the model developed to resolve them.

Keywords

Radial Basis Function Neural Network Radial Basis Function Network Atmospheric Carbon Dioxide Projection Pursuit Artificial Intelligence Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Corchado, E., Fyfe, C.: Orientation Selection Using Maximum Likelihood Hebbian Learning. International Journal of Knowledge-Based Intelligent Engineering Systems 7(2) (April 2003)Google Scholar
  2. 2.
    Corchado, J.M., Aiken, J.: Hybrid Artificial Intelligence Methods in Oceanographic Forecasting Models. IEEE SMC Transactions Part C 32(4), 307–313 (2002)Google Scholar
  3. 3.
    Corchado, J.M., Corchado, E.S., Aiken, J., Fyfe, C., Fdez-Riverola, F., Glez-Bedia, M.: Maximum Likelihood Hebbian Learning Based Retrieval Method for CBR Systems. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS, vol. 2689, Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Freedman, J., Tukey, J.: A Projection Pursuit Algorithm for Exploratory Data Analysis. IEEE Transaction on Computers (23), 881–890 (1974)Google Scholar
  5. 5.
    Fyfe, C., Corchado, J.M.: Automating the construction of CBR Systems using Kernel Methods. International Journal of Intelligent Systems. 16(4) (April 2001)Google Scholar
  6. 6.
    Fyfe, C., Corchado, E.S.: Maximum Likelihood Hebbian Rules. In: European Symposium on Artificial Neural Networks (2002)Google Scholar
  7. 7.
    Lefevre, N., Aiken, J., Rutllant, J., Daneri, G., Lavender, S., Smyth, T.: Observations of pCO2 in the coastal upwelling off Chile: Sapatial and temporal extrapolation using satellite data. Journal of Geophysical research 107(0) (2002)Google Scholar
  8. 8.
    MacDonald, D., Corchado, E., Fyfe, C.: Analysing Spectroscopic Data Using Hierarchical Cooperative Maximum Likelihood Hebbian Learning. In: Mexican International Conference on Artificial Intelligence 2004. LNCS, Springer, Heidelberg (2004)Google Scholar
  9. 9.
    Pal, S.K., Dillon, T.S., Yeung, D.S.: Soft Computing in Case-based Reasoning. Springer, London (2000)zbMATHGoogle Scholar
  10. 10.
    Seung, H.S., Socci, N.D., Lee, D.: The Rectified Gaussian Distribution. Advances in Neural Information Processing Systems 10 (1998)Google Scholar
  11. 11.
    Smola, A.J., Scholkopf, B.: A Tutorial on Support Vector Regression. Technical Report NC2-TR-1998-030, NeuroCOLT2 Technical Report Series (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Juan M. Corchado
    • 1
  • Emilio S. Corchado
    • 2
  • Jim Aiken
    • 3
  1. 1.Departamento Informática y AutomáticaUniversidad de SalamancaSalamancaSpain
  2. 2.Departamento de Ingeniería Civil, Escuela Politécnica SuperiorUniversidad de BurgosBurgosSpain
  3. 3.Plymouth Marine LaboratoryCentre for Air-Sea Interactions and fluxes (CASIX)PlymouthUK

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