Advertisement

Regional Environmental Change

, Volume 8, Issue 4, pp 197–205 | Cite as

Using fuzzy set theory to address the uncertainty of susceptibility to drought

  • Frank Eierdanz
  • Joseph Alcamo
  • Lilibeth Acosta-Michlik
  • Dörthe Krömker
  • Dennis Tänzler
Original Article

Abstract

This paper presents the technical aspects of a new methodology for assessing the susceptibility of society to drought. The methodology consists of a combination of inference modelling and fuzzy logic applications. Four steps are followed: (1) model input variables are selected—these variables reflect the main factors influencing susceptibility in a social group, population or region, (2) fuzzification—the uncertainties of the input variables are made explicit by representing them as ‘fuzzy membership functions’, (3) inference modelling—the input variables are used to construct a model made up of linguistic rules, and (4) defuzzification—results from the model in linguistic form are translated into numerical form, also through the use of fuzzy membership functions. The disadvantages and advantages of this methodology became apparent when it was applied to the assessment of susceptibility from three disciplinary perspectives: Disadvantages include the difficulty in validating results and the subjectivity involved with specifying fuzzy membership functions and the rules of the inference model. Advantages of the methodology are its transparency, because all model assumptions have to be made explicit in the form of inference rules; its flexibility, in that informal and expert knowledge can be incorporated through ‘fuzzy membership functions’ and through the rules in the inference model; and its versatility, since numerical data can be converted to linguistic statements and vice versa through the procedures of ‘fuzzification’ and ‘defuzzification’.

Keywords

Susceptibility to drought Vulnerability to climate extremes Fuzzy set applications Climate change impacts 

References

  1. Acosta-Michlik L, Kavi-Kumar K, Klein R, Campe S (2008) Application of fuzzy models to assess susceptibility to drought from a socio-economic perspective. Reg Environ Change. XXGoogle Scholar
  2. Alcamo J, Acosta-Michlik L, Carius A, Eierdanz F, Klein R, Krömker D, Tänzler D (2008) A new approach to quantifying and comparing vulnerability to drought. Reg Environ Change. XXGoogle Scholar
  3. Alcamo J, Acosta-Michlik E, Carius A, Eierdanz F, Klein R, Krömker D, Tänzler D (2005) A new approach to the assessment of vulnerability to drought. In: Proceedings of Final Symposium of the German Climate Research Programme (DEKLIM). LeipzigGoogle Scholar
  4. Aliev R, Bondif KW, Aliew F (2000) Soft Computing: eine grundlegende Einführung. BerlinGoogle Scholar
  5. Andriantiatsaholiniaina L, Kouikoglou VS, Phillis YA (2004) Evaluating strategies for sustainable development: fuzzy logic reasoning and sensitivity analysis. Ecol Econ 48(2, 2004):149–172CrossRefGoogle Scholar
  6. Bender MJ, Simonovic SP (2000) A fuzzy compromise approach to water resource systems planning under uncertainty. Fuzzy Sets Syst 115:35–44. doi: 10.1016/S0165-0114(99)00025-1 CrossRefGoogle Scholar
  7. Bothe H-H (1998) Neuro-Fuzzy-Methoden. Einführung in Theorie und Anwendungen, BerlinGoogle Scholar
  8. Callens I, Tyteca D (1999) Towards indicators of sustainable development for firms: A productive efficiency perspective. Ecol Econ 28:41–53. doi: 10.1016/S0921-8009(98)00035-4 CrossRefGoogle Scholar
  9. Cassel-Gintz M, Petschel-Held G (2000) GIS-based assessment of the threat to world rorests by patterns of non-sustainable civilisation nature interaction. J Environ Manage 59:279–298. doi: 10.1006/jema.2000.0370 CrossRefGoogle Scholar
  10. Cornelissen AMG, vd Berg J, Koops WJ, Grossman M, Udo HMJ (2001) Assessment of the contribution of sustainability indicators to sustainable development: A novel approach using fuzzy set theory. Agric Ecosyst Environ 86:173–185. doi: 10.1016/S0167-8809(00)00272-3 CrossRefGoogle Scholar
  11. Dompere KK (1997) Cost-benefit analysis, benefit accounting and fuzzy decisions (i): theory. Fuzzy Sets Syst 92:275–287. doi: 10.1016/S0165-0114(96)00180-7 CrossRefGoogle Scholar
  12. Kangas AS, Kangas J (2004) Probability, possibility and evidence: approaches to consider risk and uncertainty in forestry decision analysis. For Policy Econ 6(2):169–188. doi: 10.1016/S1389-9341(02)00083-7 Google Scholar
  13. Koprinkova P (2000) Membership functions shape and its influence on the dynamical behaviour of fuzzy logic controller. Cybern Syst 31(2):161–174. doi: 10.1080/019697200124865 CrossRefGoogle Scholar
  14. Koprinkova P, Penev V (1999) Dynamical behaviour of fuzzy logic based velocity control autopilot with respect to changes in linguistic variables membership functions shape. Inf Secur. 3Google Scholar
  15. Krömker D, Eierdanz F, Stolberg A (2008) Who is susceptible and why? An agent-based approach to assessing vulnerability to drought. Regional Environmental Change. Volume XXGoogle Scholar
  16. Levy JB, Yoon E (1995) Theory and methodology: modelling global market entry decision by fuzzy logic with an application to country risk assessment. Eur J Oper Res 82:53–78. doi: 10.1016/0377-2217(93)E0166-U CrossRefGoogle Scholar
  17. Lienenkamp R (1999) Internationale Wanderungen im 21. Jahrhundert : die Ermittlung von Dispositionsräumen globaler Migrationen auf der Basis von Fuzzy Logic. Dortmunder Beiträge zur Raumplanung : Blaue Reihe; 93. DortmundGoogle Scholar
  18. Lindström T (1998) A fuzzy design of the willingness to invest in Sweden. J Econ Behav Organ 36:1–17. doi: 10.1016/S0167-2681(98)00067-5 CrossRefGoogle Scholar
  19. Mackay DS, Robinson VB (2000) A multiple criteria decision support system for testing integrated environmental models. Fuzzy Sets Syst 113:53–67. doi: 10.1016/S0165-0114(99)00012-3 CrossRefGoogle Scholar
  20. Mosmans A, Praet J-C, Dumont C (2002) A decision support system for the budgeting of the Belgian health care system. Eur J Oper Res 139:449–460. doi: 10.1016/S0377-2217(01)00369-1 CrossRefGoogle Scholar
  21. Phillis YA, Andriantiatsaholiniaina LA (2001) Sustainability: an ill-defined concept and its assessment using fuzzy logic. Ecol Econ 37:435–456. doi: 10.1016/S0921-8009(00)00290-1 CrossRefGoogle Scholar
  22. Roberts DW (1996) Modelling forest dynamics with vital attributes and fuzzy systems theory. Ecol Modell 90:161–173. doi: 10.1016/0304-3800(95)00163-8 CrossRefGoogle Scholar
  23. Silvert W (2000) Fuzzy indices of environmental conditions. Ecol Modell 130:111–119. doi: 10.1016/S0304-3800(00)00204-0 CrossRefGoogle Scholar
  24. Tänzler D, Carius A (2008) Assessing the susceptibility of societies to droughts: a political science perspective. Reg Environ Change. XXGoogle Scholar
  25. Tänzler D, Feil M, Krömker D, Eierdanz F (2008) The challenge of validating vulnerability estimates—the option of media content analysis for identifying drought-related crises. Reg Environ Change. XXGoogle Scholar
  26. Wu H-I, Li B-L, Stoker R, Li Y (1996) A semi-arid grazing ecosystem simulation model with probabilistic and fuzzy parameters. Ecol Modell 90:147–160. doi: 10.1016/0304-3800(95)00162-X CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Frank Eierdanz
    • 1
  • Joseph Alcamo
    • 2
  • Lilibeth Acosta-Michlik
    • 3
  • Dörthe Krömker
    • 1
  • Dennis Tänzler
    • 4
  1. 1.Faculty of PsychologyUniversity of KasselKasselGermany
  2. 2.Center for Environmental Systems Research at the University of KasselKasselGermany
  3. 3.Département de Géologie et de GéographieUniversité Catholique de LouvainLouvainBelgium
  4. 4.Adelphi ResearchBerlinGermany

Personalised recommendations