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Combining case-based reasoning systems and support vector regression to evaluate the atmosphere–ocean interaction

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Abstract

This work presents a system for automatically evaluating the interaction that exists between the atmosphere and the ocean’s surface. Monitoring and evaluating the ocean’s carbon exchange process is a function that requires working with a great amount of data: satellite images and in situ vessel’s data. The system presented in this study focuses on computational intelligence. The study presents an intelligent system based on the use of case-based reasoning (CBR) systems and offers a distributed model for such an interaction. Moreover, the system takes into account the fact that the working environment is dynamic and therefore it requires autonomous models that evolve over time. In order to resolve this problem, an intelligent environment has been developed, based on the use of CBR systems, which are capable of handling several goals, by constructing plans from the data obtained through satellite images and research vessels, acquiring knowledge and adapting to environmental changes. The artificial intelligence system has been successfully tested in the North Atlantic Ocean, and the results obtained will be presented in this study.

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Authors and Affiliations

  1. Department of Computer Science and Automation, University of Salamanca, Plaza de la Merced s/n, 37008, Salamanca, Spain

    Juan F. De Paz, Angélica González, Sara Rodríguez & Juan M. Corchado

  2. Faculty of Computer Sciences, Pontifical University of Salamanca, Compañía 5, 37002, Salamanca, Spain

    Javier Bajo

  3. Faculty of Computer Sciences, University of Salamanca, Plaza de la Merced s/n, 37008, Salamanca, Spain

    Juan F. De Paz

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  1. Juan F. De Paz
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  2. Javier Bajo
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  3. Angélica González
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  4. Sara Rodríguez
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  5. Juan M. Corchado
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Correspondence to Juan F. De Paz.

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De Paz, J.F., Bajo, J., González, A. et al. Combining case-based reasoning systems and support vector regression to evaluate the atmosphere–ocean interaction. Knowl Inf Syst 30, 155–177 (2012). https://doi.org/10.1007/s10115-010-0368-y

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