Constraint and Preference Modelling for Spatial Decision Making with Use of Possibility Theory

  • Jan Caha
  • Veronika Nevtípilová
  • Jiří Dvorský
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8480)


Decision making support is one of the main objectives of geographical information systems. So far mainly boolean queries and boolean logic are used for spatial decision making problems. The study presents utilization of Possibility theory for modelling constraints and preferences for spatial data. The importance of aggregation operators in decision making is discussed as well. The case study involving a simple decision making problem is presented: selection of a waste disposal site based on three parameters - slope, distance from water and landuse. The results are presented and discussed. The main aim is focused on providing more information to the decision maker that will allow him to select the most suitable alternative.


Possibility theory decision making spatial query 


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  1. 1.
    Abraham, A.: Special issue: Hybrid approaches for approximate reasoning. Journal of Intelligent and Fuzzy Systems 23(2-3), 41–42 (2012)MathSciNetGoogle Scholar
  2. 2.
    Benferhat, S., Dubois, D., Kaci, S., Prade, H.: Bipolar possibility theory in preference modeling: Representation, fusion and optimal solutions. Information Fusion 7(1), 135–150 (2006)CrossRefGoogle Scholar
  3. 3.
    Boroushaki, S., Malczewski, J.: Using the fuzzy majority approach for GIS-based multicriteria group decision-making. Computers & Geosciences 36(3), 302–312 (2010)CrossRefGoogle Scholar
  4. 4.
    Borrajo, M.L., Baruque, B., Corchado, E., Bajo, J., Corchado, J.M.: Hybrid Neural Intelligent System to Predict Business Failure in SMEs. International Journal of Neural Systems 21(4) (2011)Google Scholar
  5. 5.
    Caha, J., Dvorský, J.: Querying on Fuzzy Surfaces with Vague Queries. In: Pan, J.-S., Polycarpou, M.M., Woźniak, M., de Carvalho, A.C.P.L.F., Quintián, H., Corchado, E. (eds.) HAIS 2013. LNCS, vol. 8073, pp. 548–557. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  6. 6.
    Caha, J., Vondráková, A., Dvorský, J.: Comparison of Crisp, Fuzzy and Possibilistic Threshold in Spatial Queries. In: Abraham, A., Krömer, P., Snášel, V. (eds.) Innovations in Bio-inspired Computing and Applications. AISC, vol. 237, pp. 239–248. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  7. 7.
    Destercke, S., Buche, P., Guillard, V.: A flexible bipolar querying approach with imprecise data and guaranteed results. Fuzzy Sets and Systems 169(1), 51–64 (2011)CrossRefMathSciNetGoogle Scholar
  8. 8.
    De Bruin, S.: Querying probabilistic land cover data using fuzzy set theory. International Journal of Geographical Information Science 14(4), 359–372 (2000)CrossRefGoogle Scholar
  9. 9.
    Dubois, D., Prade, H.: Ranking Fuzzy Numbers in the Setting of Possibility Theory. Information Sciences 30(3), 183–224 (1983)CrossRefzbMATHMathSciNetGoogle Scholar
  10. 10.
    Dubois, D., Prade, H.: Possibility Theory: An approach to Computerized Processing of Uncertainty. Plenum Press, New York (1986)Google Scholar
  11. 11.
    Dubois, D., Fargier, H., Prade, H.: Possibility theory in constraint satisfaction problems: Handling priority, preference and uncertainty. Applied Intelligence 6, 287–309 (2006)CrossRefGoogle Scholar
  12. 12.
    Dubois, D.: The role of fuzzy sets in decision sciences: Old techniques and new directions. Fuzzy Sets and Systems 184(1), 3–28 (2011)CrossRefzbMATHMathSciNetGoogle Scholar
  13. 13.
    Hanss, M.: Applied fuzzy arithmetic: An introduction with engineering applications. Springer, Berlin (2005)Google Scholar
  14. 14.
    Janoška, Z., Dvorský, J.: P systems: State of the art with respect to representation of geographical space. In: CEUR Workshop Proceedings - 12th Annual Workshop on Databases, Texts, Specifications and Objects, DATESO 2012, pp. 13–24 (2012)Google Scholar
  15. 15.
    Sugumaran, R., Degroote, J.: Spatial decision support systems: principles and practices. Taylor & Francis, Boca Raton (2011)Google Scholar
  16. 16.
    Vinotha, J.M., Ritha, W., Abraham, A.: Total time minimization of fuzzy transportation problem. Journal of Intelligent and Fuzzy Systems 23(2), 93–99 (2012)zbMATHMathSciNetGoogle Scholar
  17. 17.
    Wang, F.J.: A fuzzy grammar and possibility theory-based natural language user interface for spatial queries. Fuzzy Sets and Systems 113(1), 147–159 (2000)CrossRefzbMATHGoogle Scholar
  18. 18.
    Witlox, F., Derudder, B.: Spatial Decision-Making Using Fuzzy Decision Tables: Theory, Application and Limitations. In: Petry, F., Robinson, V.B., Cobb, M.A. (eds.) Fuzzy Modeling with Spatial Information for Geographic Problems, pp. 120–142. Springer, Berlin (2005)Google Scholar
  19. 19.
    Yager, R.R.: On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Transactions on Systems, Man and Cybernetics 18(1), 183–190 (1988)CrossRefzbMATHMathSciNetGoogle Scholar
  20. 20.
    Zadeh, L.A.: Possibility theory and soft data analysis. In: Klir, G.J., Yuan, B. (eds.) Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems, pp. 481–541. World Scientific Publishing Co., Inc. (1996)Google Scholar
  21. 21.
    Zimmermann, H.J.: Fuzzy set theory - and its applications, 2nd rev. edn. Kluwer Academic Publishers, Boston (1991)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jan Caha
    • 1
  • Veronika Nevtípilová
    • 1
  • Jiří Dvorský
    • 1
  1. 1.Department of Geoinformatics, Faculty of SciencePalacký University in OlomoucOlomoucCzech Republic

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