Advertisement

Fuzzy Logic and Data Mining in Disaster Mitigation

  • Abraham Kandel
  • Dan Tamir
  • Naphtali D. Rishe
Conference paper
Part of the NATO Science for Peace and Security Series C: Environmental Security book series (NAPSC)

Abstract

Disaster mitigation and management is one of the most challenging examples of decision making under uncertain, missing, and sketchy, information. Even in the extreme cases where the nature of the disaster is known, preparedness plans are in place, and analysis, evaluation, and simulations of the disaster management procedures have been performed, the amount and magnitude of “surprises” that accompany the real disaster pose an enormous demand. In the more severe cases, where the entire disaster is an unpredicted event, the disaster management and response system might fast run into a chaotic state. Hence, the key for improving disaster preparedness and mitigation capabilities is employing sound techniques for data collection, information processing, and decision making under uncertainty. Fuzzy logic based techniques are some of the most promising approaches for disaster mitigation. The advantage of the fuzzy-based approach is that it enables keeping account on events with perceived low possibility of occurrence via low fuzzy membership/truth-values and updating these values as information is accumulated or changed. Several fuzzy logic based algorithms can be deployed in the data collection, accumulation, and retention stage, in the information processing phase, and in the decision making process. In this chapter a comprehensive assessment of fuzzy techniques for disaster mitigation is presented. The use of fuzzy logic as a possible tool for disaster management is investigated and the strengths and weaknesses of several fuzzy techniques are evaluated. In addition to classical fuzzy techniques, the use of incremental fuzzy clustering in the context of complex and high order fuzzy logic system is evaluated.

Keywords

Fuzzy logic Fuzzy functions Fuzzy expectation Black swan Gray swan 

Notes

Acknowledgments

This material is based in part upon work supported by the National Science Foundation under Grant Nos. CNS-0821345, CNS-1126619, HRD-0833093, IIP-0829576, CNS-1057661, IIS-1052625, CNS-0959985, OISE-1157372, IIP-1237818, IIP-1330943, IIP-1230661, IIP-1026265, IIP-1058606, IIS-1213026.

References

  1. 1.
    Taleb NN (2004) Fooled by randomness. Random House, New YorkGoogle Scholar
  2. 2.
    Taleb NN (2007) The black swan. Random House, New YorkGoogle Scholar
  3. 3.
    Kandel A (1986) Fuzzy mathematical techniques with applications. Addison-Wesley, ReadingMATHGoogle Scholar
  4. 4.
    Zemankova-Leech M, Kandel A (1984) Fuzzy relational data bases – a key to expert systems. Verlag TUV Rheinland, KolnMATHGoogle Scholar
  5. 5.
    Last M, Kandel A, Bunke H (eds) (2004) Data mining in time series databases, series in machine perception and artificial intelligence, vol 57. World Scientific, SingaporeGoogle Scholar
  6. 6.
    Mikhail RF, Berndt D, Kandel A (2010) Automated database application testing, series in machine perception and artificial intelligence, vol 76. World Scientific, SingaporeGoogle Scholar
  7. 7.
    Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning – Part I. Inform Sci 7:199–249CrossRefMathSciNetGoogle Scholar
  8. 8.
    Klir GJ, Tina A (1988) Fuzzy sets, uncertainty, and information. Prentice Hall, Upper Saddle RiverMATHGoogle Scholar
  9. 9.
    Tamir DE, Kandel A (1990) An axiomatic approach to fuzzy set theory. Inform Sci 52:75–83CrossRefMATHMathSciNetGoogle Scholar
  10. 10.
    Tamir DE, Kandel A (1995) Fuzzy semantic analysis and formal specification of conceptual knowledge. Inform Sci Intell Syst 82(3–4):181–196MATHGoogle Scholar
  11. 11.
    Ramot D, Milo R, Friedman M, Kandel A (2002) Complex fuzzy sets. IEEE Trans Fuzzy Syst 10(2):171–186CrossRefGoogle Scholar
  12. 12.
    Ramot D, Friedman M, Langholz G, Kandel A (2003) Complex fuzzy logic. IEEE Trans Fuzzy Syst 11(4):450–461CrossRefGoogle Scholar
  13. 13.
    Tamir DE, Lin J, Kandel A (2011) A new interpretation of complex membership grade. Int J Intell Syst 26(4):285–312Google Scholar
  14. 14.
    Dick S (2005) Towards complex fuzzy logic. IEEE Trans Fuzzy Syst 13:405–414CrossRefGoogle Scholar
  15. 15.
    Běhounek L, Cintula P (2005) Fuzzy class theory. Fuzzy Set Syst 154(1):34–55CrossRefMATHGoogle Scholar
  16. 16.
    Fraenkel AA, Bar-Hillel Y, Levy A (1973) Foundations of set theory, 2nd edn. Elsevier, AmsterdamMATHGoogle Scholar
  17. 17.
    Mundici D, Cignoli R, D’Ottaviano IML (1999) Algebraic foundations of many-valued reasoning. Kluwer Academic, BostonGoogle Scholar
  18. 18.
    Hájek P (1995) Fuzzy logic and arithmetical hierarchy. Fuzzy Set Syst 3(8):359–363CrossRefGoogle Scholar
  19. 19.
    Casasnovas J, Rosselló F (2009) Scalar and fuzzy cardinalities of crisp and fuzzy multisets. Int J Intell Syst 24(6):587–623CrossRefMATHGoogle Scholar
  20. 20.
    Cintula P (2003) Advances in LΠ and LΠ1/2 logics. Arch Math Log 42:449–468CrossRefMATHMathSciNetGoogle Scholar
  21. 21.
    Montagna F (2005) On the predicate logics of continuous t-norm BL-algebras. Arc Math Log 44:97–114CrossRefMATHMathSciNetGoogle Scholar
  22. 22.
    Lemnios ZJ, Shaffer A (2009) The critical role of science and technology for national defense, Computing Research News, a publication of the CRA, vol 21, no 5Google Scholar
  23. 23.
    Last M, Kandel A (eds) (2005) Fighting terror in cyberspace, series in machine perception and artificial intelligence, vol 65. World Scientific, SingaporeGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.School of Computer ScienceFlorida International University MiamiMiamiUSA
  2. 2.Department of Computer ScienceTexas State UniversitySan MarcosUSA

Personalised recommendations