Prioritizing key measures to increase public participation in natural disaster management in Iran

جغرافیا و مخاطرات محیطی

Article 11, Volume 10, Issue 3 - Serial Number 39, September 2021, Pages 215-227   

Document Type: Research Article

DOI: 10.22067/geoeh.2021.70190.1055

Authors

Ahmad Nohegar^{* 1}; Ali Alavi Naeini²; Motahare Abbasi Nodeh³

¹Professor, Faculty of Environment, College of Engineering, University of Tehran, Tehran, Iran

²Ph.D. in environmental Planning, University of Tehran, Tehran, Iran

³Master in EcoHydrology, University of Tehran, Tehran, Iran

Abstract

Natural Disasters have brought numerous casualties, damages, and financial losses throughout the years in Iran. In this paper, the importance of public participation of institutions and people in disasters management is discussed. Afterward, ten types of key measures to increase the success of public participation as well as three criteria related to speed, quality, and effectiveness, all three of which are positive criteria, were considered. To carry out the decision-making process, the methods were ranked using a multi-criteria decision-making method to select the best method in the last step. For this purpose, the Shannon entropy method was used to weight the criteria and the Topsis method was used to rank the measures. Outcomes of ten types of key measures illustrate that support for trustworthy sovereignty and commitment in the first place, the need to define common goals and interests in the second place, determination of unity between institutions and people in the third place, determining effective leadership of the government in the fourth place, finding common approaches to achieving the goal in fifth place, ensuring transparent and effective communication in the sixth place, clarification of roles and responsibilities in the seventh place, investment in the accurate performance of processes in the eighth place, increasing government staff time to facilitate and support processes in the ninth place, and managing and monitoring problems in partnerships are ranked in the tenth place.

Keywords

Natural disasters management; Public participation; Decision-making; TOPSIS

References

Alexander, D., 2002. Principles of emergency planning and management. Oxford, England: Oxford University Press.

Australian Emergency Management Institute., 2011. Community recovery (3rd ed.). Australia: Commonwealth of Australia.

Blaikie, P. M., Cannon, T., Davis, I., & Wisner, B., 1994. At risk: Natural hazards, people’s vulnerability, and disasters. London, New York: Routledge.

Bracken, I., 2014. Urban planning methods: Research and policy analysis. Hoboken: Taylor and Francis.

Chuansheng X, Dapeng D, Shengping H, Xin X, Yingjie C., 2012. “Safety evaluation of smart grid based on AHP-entropy method”. Systems Engineering Procedia, 4(2), pp. 203-209.

Cutter, S., Barnes, L., Berry, M., Burton, C., Evans, E., Tate, E., & Webb, J., 2008. A place-based model for understanding community resilience to natural disasters. Global Environmental Change, 3(1), 598–606.

Fu G., 2008. “A fuzzy optimization method for multicriteria decision making: An application to reservoir flood control operation”. Expert Systems with Applications, 34(1), pp. 145-9.

Kornakova, M., 2016. Improving the integration of urban planning with disaster risk reduction (Ph.D.). University of Melbourne (Unpublished).

Norris, F., Stevens, S., Pfefferbaum, B., Wyche, K., & Pfefferbaum, R., 2008. Community resilience as a metaphor, theory, set of capacities, and strategy for disaster readiness. American Journal of Community Psychology, 41(1–2), 127–150.

Özcan EC, Ünlüsoy S, Eren T., 2017. “A combined goal programming–AHP approach supported with TOPSIS for maintenance strategy selection in hydroelectric power plants”. Renewable and Sustainable Energy Reviews, 78(4), pp. 10-23.

Twigg J. Disaster risk reduction. London: Overseas Development Institute, Humanitarian Policy Group; 2015.

Wamsler, C., 2014. Cities, disaster risk and adaptation. New York: Routledge.

Wang E, Alp N, Shi J, Wang C, Zhang X, Chen H., 2017. “Multi-criteria building energy performance benchmarking through variable clustering based compromise TOPSIS with objective entropy weighting”. Energy, 125(1), pp. 197-210.

WWW.EM-DAT.BE

Yadav, S.P. and Kumar, S., 2009. “A multi-criteria interval-valued intuitionistic fuzzy group decision making for supplier selection with TOPSIS method”. In International workshop on rough sets, fuzzy sets, data mining, and granular-soft computing . Springer, Berlin, Heidelberg, pp. 303-312.

Yue Z., 2011. “A method for group decision-making based on determining weights of

Zschau J, Küppers AN, editors., 2013. Early warning systems for natural disaster reduction, Springer Science & Business Media.