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Fuzzy Cognitive Mapping and Nonlinear Hebbian Learning for the Qualitative Simulation of the Climate System, from a Planetary Boundaries Perspective

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Book cover Simulation and Modeling Methodologies, Technologies and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 402))

Abstract

In the present work, we constructed a collective fuzzy cognitive map for the qualitative simulation of the Earth climate system. The map was developed by considering the subsystems on which the climate equilibrium depends, and by aggregating different experts opinions over this framework. The resulting network was characterized by graph indexes and used for the simulation and analysis of hidden patterns and model sensitivity. Then, linguistic variables were used to fuzzify the edges and aggregated to produce an overall linguistic weight for each one. The resulting linguistic weights were defuzzified using the center of gravity technique, and the current state of the Earth climate system was simulated and discussed. Finally, a nonlinear Hebbian learning algorithm was used for updating the edges of the map until a desired state was reached, defined by target values for the concepts. The results are discussed to explore possible policy implementation, as well as environmental decision making.

Currently PhD student at the Soft Computing Research Group, BarcelonaTech.

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Notes

  1. 1.

    Dobson unit is a measure of the ozone layer thickness, equal to 0,01 mm of thickness in normal conditions of pressure and temperature (1 atm and 0 C respectively), expressed as the molecule number. DU represents the existence of \(2.69 \times 10^{16}\) molecules per square centimeter.

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Acknowledgments

The present work was developed with the support of the Programa de Investigación en Cambio Climático (PINCC) of the Universidad Nacional Autónoma de México (UNAM).

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Authors and Affiliations

  1. Programa de Investigación en Cambio Climático, Universidad Nacional Autónoma de México Edificio de Programas Universitarios, Circuito de la Investigación Científica s/n, Ciudad Universitaria, Mexico City, Mexico

    Iván Paz-Ortiz & Carlos Gay-García

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  1. Iván Paz-Ortiz
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  2. Carlos Gay-García
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Correspondence to Iván Paz-Ortiz .

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Editors and Affiliations

  1. Dept. of Computer & Info Science, Fordham University, Bronx, New York, USA

    Mohammad S. Obaidat

  2. Faculty of Engg school of ECE, University of Ottawa, Ottawa, Ontario, Canada

    Tuncer Ören

  3. Intelligent Systems Laboratory, Sys Rese Insti of Polish Academy of Sci, Warsaw, Poland

    Janusz Kacprzyk

  4. INSTICC, Setubal, Portugal

    Joaquim Filipe

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Paz-Ortiz, I., Gay-García, C. (2015). Fuzzy Cognitive Mapping and Nonlinear Hebbian Learning for the Qualitative Simulation of the Climate System, from a Planetary Boundaries Perspective. In: Obaidat, M., Ören, T., Kacprzyk, J., Filipe, J. (eds) Simulation and Modeling Methodologies, Technologies and Applications . Advances in Intelligent Systems and Computing, vol 402. Springer, Cham. https://doi.org/10.1007/978-3-319-26470-7_15

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  • DOI: https://doi.org/10.1007/978-3-319-26470-7_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26469-1

  • Online ISBN: 978-3-319-26470-7

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