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Artificial Intelligence and Environmental Decision Support Systems

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Abstract

An effective protection of our environment is largely dependent on the quality of the available information used to make an appropriate decision. Problems arise when the quantities of available information are huge and nonuniform (i.e., coming from many different disciplines or sources) and their quality could not be stated in advance. Another associated issue is the dynamical nature of the problem. Computers are central in contemporary environmental protection in tasks such as monitoring, data analysis, communication, information storage and retrieval, so it has been natural to try to integrate and enhance all these tasks with Artificial Intelligence knowledge-based techniques. This paper presents an overview of the impact of Artificial Intelligence techniques on the definition and development of Environmental Decision Support Systems (EDSS) during the last fifteen years. The review highlights the desirable features that an EDSS must show. The paper concludes with a selection of successful applications to a wide range of environmental problems.

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Authors and Affiliations

  1. Software Department, Technical University of Catalonia (UPC), Jordi Girona 1-3, E08034, Barcelona, Catalonia, Spain

    U. Cortés

  2. Software Department, Technical University of Catalonia (UPC), Jordi Girona 1-3, E08034, Barcelona, Catalonia, Spain

    M. Sànchez-Marrè

  3. Software Department, Technical University of Catalonia (UPC), Jordi Girona 1-3, E08034, Barcelona, Catalonia, Spain

    L. Ceccaroni

  4. Chemical and Environmental Engineering Laboratory, University of Girona, Campus de Montilivi, E17071, Girona, Catalonia, Spain

    I. R-Roda

  5. Chemical and Environmental Engineering Laboratory, University of Girona, Campus de Montilivi, E17071, Girona, Catalonia, Spain

    M. Poch

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Cortés, U., Sànchez-Marrè, M., Ceccaroni, L. et al. Artificial Intelligence and Environmental Decision Support Systems. Applied Intelligence 13, 77–91 (2000). https://doi.org/10.1023/A:1008331413864

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