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Making Predictions of Global Warming Impacts Using a Semantic Web Tool that Simulates Fuzzy Cognitive Maps

Author

Listed:
  • Athanasios Tsadiras

    (Aristotle University of Thessaloniki)

  • Maria Pempetzoglou

    (Democritus University of Thrace)

  • Iosif Viktoratos

    (Aristotle University of Thessaloniki)

Abstract

One of the most important environmental problems of our era is Global Warming (GW), which derives its roots mainly from anthropogenic activities and is expected to cause far-reaching and long-lasting impacts to the natural environment, ecosystems and human societies. The purpose of this paper is twofold: (a) to develop a model of the causal relationships that exist in the field of GW, using the well-established Artificial Intelligence technique of Fuzzy Cognitive Maps (FCMs) and (b) to develop a Semantic Web simulation software tool, that visually simulates the FCM dynamic behavior and studies the equilibrium that the FCM dynamic system reaches. Using this generic tool, various scenarios can be imposed to the FCM model and predictions can be made on these, in a “what-if” manner. The features of the web simulation tool are exhibited using the FCM that was created and concerns “Global Warming”. By applying Semantic Web technologies, the tool makes the results and the various FCM models, that can be implemented in it, easily accessible to various users or systems, through the Internet. In this way, policy makers can use this technique and tool to make predictions by viewing dynamically the consequences that the system predicts to their imposed scenarios and share them through the world wide web.

Suggested Citation

  • Athanasios Tsadiras & Maria Pempetzoglou & Iosif Viktoratos, 2021. "Making Predictions of Global Warming Impacts Using a Semantic Web Tool that Simulates Fuzzy Cognitive Maps," Computational Economics, Springer;Society for Computational Economics, vol. 58(3), pages 715-745, October.
    • Handle: RePEc:kap:compec:v:58:y:2021:i:3:d:10.1007_s10614-020-10025-1
    DOI: 10.1007/s10614-020-10025-1
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    References listed on IDEAS

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    1. Konstantinos Papageorgiou & Gustavo Carvalho & Elpiniki I. Papageorgiou & Dionysis Bochtis & George Stamoulis, 2020. "Decision-Making Process for Photovoltaic Solar Energy Sector Development using Fuzzy Cognitive Map Technique," Energies, MDPI, vol. 13(6), pages 1-23, March.
    2. Stern,Nicholas, 2007. "The Economics of Climate Change," Cambridge Books, Cambridge University Press, number 9780521700801.
    3. Doukas, Haris & Nikas, Alexandros, 2020. "Decision support models in climate policy," European Journal of Operational Research, Elsevier, vol. 280(1), pages 1-24.
    4. A. S. Andreou & N. H. Mateou & G. A. Zombanakis, 2003. "The Cyprus puzzle and the Greek - Turkish arms race: Forecasting developments using genetically evolved fuzzy cognitive maps," Defence and Peace Economics, Taylor & Francis Journals, vol. 14(4), pages 293-310.
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    Cited by:

    1. Themistoklis Koutsellis & Georgios Xexakis & Konstantinos Koasidis & Alexandros Nikas & Haris Doukas, 2022. "Parameter analysis for sigmoid and hyperbolic transfer functions of fuzzy cognitive maps," Operational Research, Springer, vol. 22(5), pages 5733-5763, November.
    2. Halkos, George & Tsilika, Kyriaki, 2021. "Computational aspects of sustainability: Conceptual review and analytical framework," MPRA Paper 109632, University Library of Munich, Germany.
    3. George E. Halkos & Kyriaki D. Tsilika, 2021. "Computational Aspects of Sustainability," Computational Economics, Springer;Society for Computational Economics, vol. 58(3), pages 549-553, October.

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    More about this item

    Keywords

    Global Warming; Fuzzy Cognitive Maps; Semantic Web; Neural Networks; Simulation Modeling;
    All these keywords.

    JEL classification:

    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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