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Quantifying the Ocean’s CO2 Budget with a CoHeL-IBR System

  • Juan M. Corchado
  • Jim Aiken
  • Emilio S. Corchado
  • Nathalie Lefevre
  • Tim Smyth
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3155)

Abstract

By improving accuracy in the quantification of the ocean’s CO2 budget, a more precise estimation can be made of the terrestrial fraction of global CO2 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 – a Cooperative Maximum Likelihood Hebbian Learning model (CoHeL). This paper reviews the problems of measuring the ocean’s CO2 budget and presents the CoHeL model developed and outlines the IBR system developed to resolve the problem.

Keywords

Sparse Representation Case Base Reasoning Projection Pursuit Hebbian Learn 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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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Juan M. Corchado
    • 1
  • Jim Aiken
    • 2
  • Emilio S. Corchado
    • 3
  • Nathalie Lefevre
    • 4
  • Tim Smyth
    • 2
  1. 1.Departamento Informática y AutomáticaUniversidad de SalamancaSalamancaSpain
  2. 2.Centre for Air-Sea Interactions and fluxesPlymouth Marine LaboratoryPlymouthUK
  3. 3.Departamento de Ingeniería Civil, Escuela Politécnica SuperiorUniversidad de BurgosBurgosSpain
  4. 4.School of Environmental SciencesUniversity of East AngliaNorwichUK

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