FRAMEWORK FOR INDICATOR-BASED OPTIMIZATION OF DISASTER RISK MANAGEMENT IN LOCAL COMMUNITIES

Goran Janaćković, Suzana Savić, Miomir Stanković

DOI Number
10.22190/FUWLEP1701011J
First page
011
Last page
022

Abstract


The effects on functioning of the society and consequences of natural disasters and technological accidents require preparedness and rapid response. Disaster management is defined by decisions based on situation description and potential dangers. Risk assessment is performed at various levels, from national to local. This paper presents a framework to optimize natural disaster and technological accident risk management at local level based on application of risk indicators. The method of multi-criteria analysis is applied, and key indicators that best describe the risks at the level of local communities in Serbia were chosen. The results show the importance of raising the resilience of local communities to disasters, primarily in the areas of planning and capacity building. 


Keywords

indicators, risk, resilience, management, fuzzy AHP

Full Text:

PDF

References


Savić, S., Stanković, A. (2015), Indikatori rizika u vanrednim situacijama, 18. nacionalna konferencija “Upravljanje kvalitetom i pouzdanošću”, Prijevor, Srbija, 25-26. juni 2015, Zbornik radova, 310-317, Istraživački centar DQM, Prijevor.

Benson C. (2004), Macroeconomic Concepts of Vulnerability: Dynamics, Complexity and Public Policy, In: Mapping Vulnerability: Disasters, Development and People (eds. G. Bankoff, G. Frerks and D. Hilhorst), Earthscan Publishers, London, 2004.

Briguglio L., Cordina G., Bugeja S., Farrugia N. (2005), The Development of an Index on Economic Resilience, International Workshop on Building the Economic Resilience of Small Status, Commonwealth Secretariat and Department of Economics of University of Malta, Valletta.

Davidson R. (1997), Urban Earthquake Disaster Risk Index, Report No 121, The John A. Blume Earthquake Engineering Center, Department of Civil Engineering, Stanford University, Stanford, 1997. https://blume.stanford.edu/blume-tech-reports (Accessed: 09.01.2017.)

Indicators of disaster risk and risk management: Program for Latin America and the Caribbean, Summary Report, Inter-American Development Bank (IADB), Washington D. C., 2010. http://idbdocs.iadb.org/

wsdocs/getdocument.aspx?docnum=35177671 (Accessed: 09.01.2017.)

Saisana M., Tarantola S. (2002), State-of-the-art Report on Current Methodologies and Practices for Compozite Indicator Development, Applied Statistics Group, Joint Research Centre, European Commission, Institute for Protection and Security of Citizen Technological and Economic Risk Management, Ispra. https://bookshop.europa.eu (Accessed: 09.01.2017.)

UN International Strategy for Disaster Reduction (UNISDR) (2007), Building Disaster Resilient Communities - Good Practices and Lessons Learned, A Publication of the “Global Network of NGOs” for Disaster Risk Reduction, Geneva. http://www.unisdr.org/we/inform/publications /596 (Accessed: 09.01.2017.)

Eakin H., Bojorquez-Tapia L.A. (2008), Insights into the composition of household vulnerability from multi-criteria decision analysis, Global Environmental Change, 18(1), 112-127.

Janaćković G., Savić S., Stanković M. (2013), Selection and ranking of occupational safety indicators based on fuzzy AHP: Case study in road construction companies, South African Journal of Industrial Engineering, 24(3), 175-189.

Janaćković G., Savić S., Stanković M. (2011), Multi-criteria decision analysis in occupational safety management systems, Safety Engineering, 1(1), 17-23.

Orencio P.M., Fujii M. (2013), A Localized disaster-resilience index to assess coastal communities based on an Analytical Hierarchy Process (AHP), International Journal of Disaster Risk Reduction, 3(3), 62–75.

Srđević B., Medeiros Y. (2008), Fuzzy AHP Assessment of Water Management Plans, Water Resources Management, 22, 877-894.

Yang X., Zhou J., Ding J., Zou Q., Zhang Y. (2012), A fuzzy AHP-TFN based evaluation model of flood risk analysis, Journal of Computational Information Systems, 8(22), 9281-9289.

Birkmann, J. (2006), Measuring vulnerability to promote disaster-resilient societies: Conceptual frameworks and definitions, in: Measuring Vulnerability to Natural Hazards: towards disaster resilient societies, edited by: Birkmann, J., United Nations University Press, Tokyo, 9–54.

Carreno M.L., Cardona O.D., Barbat A.H. (2007), A disaster risk management performance index, Natural Hazards, 41(1), 1-20.

Cutter S.L., Burton C.G., Emrich C.T. (2010), Disaster resilience indicators for benchmarking baseline conditions, Journal of Homeland Security and Emergency Management, 7(1), 1-22.

Sherrieb K., Norris F.H., Galea, S. (2010), Measuring capacities for community resilience, Social Indicators Research, 99(2), 227-247.

Twigg J. (2009), Characteristics of a Disaster-Resilient Community, A GUIDANCE NOTE, Version 2, http://community.eldis.org/.59e907ee/Characteristics2 EDITION.pdf (Accessed: 09.01.2017.)

UN Development Programme (UNDP) (2004), Reducing Disaster Risk: A Challenge for Development, A Global Report, Chapter 2: International Patterns of Risk, the Disaster Risk Index, DRI, Geneva. http://www.planat.ch/fileadmin/PLANAT/planat_pdf/alle_2012/2001-2005/Pelling_Maskrey_et_al_2004_-_ Reducing_Disaster_Risk.pdf (Accessed: 09.01.2017.)

Janjić A., Savić S., Janaćković G., Stanković M., Velimirović L., (2016), Multi-criteria assessment of the Smart Grid efficiency using the fuzzy analytic hierarchy process, Facta Universitatis, Series: Electronics and Energetics, 29 (4), 631-646.




DOI: https://doi.org/10.22190/FUWLEP1701011J

Refbacks

  • There are currently no refbacks.


ISSN   0354-804X (Print)

ISSN   2406-0534 (Online)