Fuzzy Logic and Data Mining in Disaster Mitigation
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.
KeywordsFuzzy logic Fuzzy functions Fuzzy expectation Black swan Gray swan
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.
- 1.Taleb NN (2004) Fooled by randomness. Random House, New YorkGoogle Scholar
- 2.Taleb NN (2007) The black swan. Random House, New YorkGoogle Scholar
- 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.Mikhail RF, Berndt D, Kandel A (2010) Automated database application testing, series in machine perception and artificial intelligence, vol 76. World Scientific, SingaporeGoogle Scholar
- 13.Tamir DE, Lin J, Kandel A (2011) A new interpretation of complex membership grade. Int J Intell Syst 26(4):285–312Google Scholar
- 17.Mundici D, Cignoli R, D’Ottaviano IML (1999) Algebraic foundations of many-valued reasoning. Kluwer Academic, BostonGoogle Scholar
- 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.Last M, Kandel A (eds) (2005) Fighting terror in cyberspace, series in machine perception and artificial intelligence, vol 65. World Scientific, SingaporeGoogle Scholar