Skip to main content

Advertisement

Log in

Trapezoidal fuzzy DEMATEL method to analyze and correct for relations between variables in a composite indicator for disaster resilience

  • Regular Article
  • Published:
OR Spectrum Aims and scope Submit manuscript

Abstract

Indicator systems of disaster vulnerability are important for monitoring and increasing the capacity in risk management. Various composite indicators have been developed to operationalize social vulnerability at national and sub-national level. Problems with relations between the sub-indicators of the composite indicator are a common phenomenon. The fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) method analyzes the structure of complex cause-effect relationships between the sub-indicators based on perceived direct influences. The results provide insight into the composite indicators and can be used to correct the sub-indicator weighting for relations between the sub-indicators and allow the identification of cause- and effect-group sub-indicators which is an important information for selecting mitigation measures in risk management. The fuzzy DEMATEL method is generalized to take into account trapezoidal membership functions. A composite indicator originally developed to determine the disaster resilience in US counties is adapted, operationalized and used to assess the resilience of Germany at county level using corrected weights. Resilience is highest in urban areas and in southern Germany and lowest in rural areas, in particular in eastern Germany.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Abbasbandy S, Amirfakhrian M (2006) The nearest trapezoidal form of a generalized left right fuzzy number. Int J Approx Reason 43(2): 166–178

    Article  Google Scholar 

  • Adger W (2006) Vulnerability. Global Environmental Change 16: 57–61

    Article  Google Scholar 

  • Belton V, Stewart TJ (2002) Multiple criteria decision analysis: an integrated approach. Kluwer Academic Publishers, Boston

    Google Scholar 

  • Birkmann J (2006) Vulnerability to hazards of natural origin—towards disaster resilient societies. In: Measuring vulnerability to promote disaster-resilient societies: conceptional frameworks and definitions. UNU Press, New York

  • Birkmann J (2007) Risk and vulnerability indicators at different scales: Applicability, usefulness and policy implications. Environ Hazards 7: 20–31

    Article  Google Scholar 

  • Birkmann J, Wisner B (2006) Measuring the un-measurable: the challenges of vulnerability. Tech. Rep. 5, UNU Institute for Environment and Human Security (UNU-EHS), Bonn

  • Cardona O, Bankoff G, Frerks G, Hilhorst D (2004) The need for rethinking the concepts of vulnerability and risk from a holistic perspective: a necessary review and criticism for effective risk management. Earthscann Publications, London

    Google Scholar 

  • Chen S, Hwang C (1992) Fuzzy multiple attribute decision making: methods and applications. Springer, Berlin

    Book  Google Scholar 

  • Crichton D (1999) Natural disaster management. In: The Risk triangle. Tudor Rose, Leicester

  • Cutter S (2001) A research agenda for vulnerability science and environmental hazards. Update IDHP, Newsletter of the International Human Dimensions Programme on Global Environmental Change 2: article no. 3

  • Cutter S, Boruff B, Shirley W (2003) Social vulnerability to environmental hazards. Soc Sci Q 84(2): 242–261

    Article  Google Scholar 

  • Cutter S, Burton C, Emrich C (2010) Disaster resilience indicators for benchmarking baseline conditions. J Homeland Security Emerg Manag 7(1). doi:10.2202/1547-7355.1732

  • Dytczak M, Ginda G (2008) Classification of building repair policy choice criteria. In: Continuous optimization and knowledge-Based technologies, EURO mini conference, 20–23 May, Neringia, Lithuania, pp 353–357

  • Fekete A (2009) Validation of a social vulnerability index in context to river-floods in Germany. Nat Hazards and Earth Syst Sci 9: 393–403

    Article  Google Scholar 

  • Fontela E, Gabus A (1976) The DEMATEL observer. Tech. rep. Battelle Geneva Research Center, Geneva

  • Gall M (2007) Indices of social vulnerability to natural hazards: a comparative evaluation. PhD thesis, University of South Carolina

  • Gheorghe A, Vamanu D (2004) Decision support systems for risk mapping: viewing the risk from the hazards perspective. J Hazard Mater 111(1–3): 45–55

    Article  Google Scholar 

  • Godet M (1986) Introduction to la prospective, seven key ideas and one scenario method. Futures 18(2): 134–157

    Article  Google Scholar 

  • Hu H, Lee Y, Yen T, Tsai C (2009) Using BPNN and DEMATEL to modify importance-performance analysis model—a study of the computer industry. Expert Syst Appl 36: 9969–9979

    Article  Google Scholar 

  • Kaplan S, Garrick J (1981) On the quantitative definition of risk. Risk Anal 1(1): 11–27

    Article  Google Scholar 

  • Keeney R (1988) Structuring objectives for problems of public interest. Oper Res 36(3): 396–405

    Article  Google Scholar 

  • Keeney R (1992) Value-focused thinking, a path to creative decisionmaking. Harvard University Press, Cambridge

    Google Scholar 

  • Keeney R, Raiffa H (1976) Decisions with multiple objectives. Wiley, New York

    Google Scholar 

  • Klir G, Folger T (1988) Fuzzy sets, uncertainty and information. Prentice Hall, Englewood Cliffs

    Google Scholar 

  • Leitch M (2010) ISO 31000:2009–the new international standard on risk management. Risk Anal 30: 887–892

    Article  Google Scholar 

  • Li C, Tzeng G (2009) Identification of a threshold value for the DEMATEL method using the maximum mean de-entropy algorithm to find critical services provided by a semiconductor intellectual property mall. Expert Syst Appl 36: 9891–9898

    Article  Google Scholar 

  • Lin C, Wu W (2008) A causal analytical method for group decision-making under fuzzy environment. Expert Syst Appl 34(1): 205–213

    Article  Google Scholar 

  • Mateos A, Jimenez A (2009) A trapezoidal fuzzy numbers-based approach for aggregating group preferences and ranking decision alternatives in MCDM. In: Evolutionary multi-criterion optimization. Lecture notes in computer science, vol 5467/2009, pp 365–379. Springer, Berlin

  • Nardo M, Saisana M, Saltelli A, Tarantola S (2005a) Tools for composite indicator building. Tech. Rep. EUR 221682 EN, Joint Research Centre, Ispra, Italy

  • Nardo M, Saisana M, Saltelli A, Tarantola S, Hoffman A, Giovanni E (2005b) Handbook on constructing composite indicators: methodology and user guide. Tech. Rep. STD/DOC(2005)3-JT00188147. OECD Publishing, Paris

  • Opricovic S, Tzeng G (2003) Defuzzification within a multicriteria decision model. Int J Uncertain Fuzziness Knowl Based Syst 11(5): 635–652

    Article  Google Scholar 

  • Pelling M (2004) Vision of risk: a review of international indicators of disaster risk and its management. Tech. rep., ISDR International Strategy for Disaster Reduction, London

  • Pöyhönen M, Hämäläinen R (2001) On the convergence of multiattribute weighting methods. Eur J Oper Res 129: 569–585

    Article  Google Scholar 

  • Prabhu N (2007) Stochastic processes. Basic theory and its applications. World Scientific Publishing, Singapore

    Book  Google Scholar 

  • Purdy G (2010) ISO 31000:2009—setting a new standard for risk management. Risk Anal 30: 881–886

    Article  Google Scholar 

  • Saaty T (1980) The analytic hierarchy process: planning, priority setting, resource allocation. McGraw-Hill, New York

    Google Scholar 

  • Saaty T (1990) How to make a decision: the analytic hierarchy process. Eur J Oper Res 48: 9–26

    Article  Google Scholar 

  • Scholz R, Tietje O (2002) Embedded case study methods: integrating quantitative and qualitative knowledge. Sage, Thousand Oaks

    Google Scholar 

  • Sterman J (2000) Business dynamics: systems thinking and modeling for a complex world. Irwin/McGraw-Hill, Boston

    Google Scholar 

  • Tamura H, Akazawa K (2005) Stochastic DEMATEL for structure modeling of a complex problematique for realizing safe, secure and reliable society. J Telecommun Inf Technol 4: 139–146

    Google Scholar 

  • Thywissen K (2006) Components of risk—a comparative glossary. Tech. rep., UNU Institute for Environment and Human Security (UNU-EHS), Bonn

  • Tsai W, Chou W (2009) Selecting management systems for sustainable development in SMEs: a novel hybrid model based on DEMATEL ANP and ZOGP. Expert Syst Appl 36: 1444–1458

    Article  Google Scholar 

  • Tseng M (2009) A causal and effect decision making model of service quality expectation using grey-fuzzy dematel approach. Expert Syst Appl 36: 7738–7748

    Article  Google Scholar 

  • UN/ISDR (2005) Hyogo framework for action 2005-2015, building the resilience of nations and communities to disasters. Tech. rep., United Nations Inter-Agency Secretariat of the International Strategy for Disaster Reduction (UN/ISDR), Geneva

  • Villagran de Leon J (2006) Vulnerability—a conceptional and methodological review. Tech. Rep. 4, UNU Institute for Environment and Human Security (UNU-EHS), Bonn

  • von Winterfeldt D, Edwards W (1992) Decision analysis and behavioral research. Cambridge University Press, Cambridge

    Google Scholar 

  • Werner D (2000) Funktionalanalysis, 4th edn. Springer, Berlin

    Google Scholar 

  • Wu H, Shieh J, Li Y, Chen H (2010) A combination of AHP and DEMATEL in evaluating the criteria of employment service outreach program personnel. Inf Technol J 9(3): 569–575

    Article  Google Scholar 

  • Wu W (2008) Choosing knowledge management strategies by using a combined ANP and DEMATEL approach. Expert Syst Appl 35: 828–835

    Article  Google Scholar 

  • Wu W, Lee Y (2007) Developing global managers’ competencies using the fuzzy DEMATEL method. Expert Syst Appl 32(2): 499–507

    Article  Google Scholar 

  • Yager RR (2008) Using trapezoids for representing granular objects: Applications to learning and OWA aggregation. Inf Sci 178(2): 363–380

    Article  Google Scholar 

  • Zadeh LA (2008) Is there a need for fuzzy logic. Inf Sci 78(13): 2751–2779

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Michael Hiete.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hiete, M., Merz, M., Comes, T. et al. Trapezoidal fuzzy DEMATEL method to analyze and correct for relations between variables in a composite indicator for disaster resilience. OR Spectrum 34, 971–995 (2012). https://doi.org/10.1007/s00291-011-0269-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00291-011-0269-9

Keywords

Navigation