A Spatial Decision Support System for Modeling Urban Resilience to Natural Hazards

Hamid Rezaei, Elżbieta Macioszek (), Parisa Derakhshesh, Hassan Houshyar, Elias Ghabouli, Amir Reza Bakhshi Lomer, Ronak Ghanbari and Abdulsalam Esmailzadeh
Additional contact information
Hamid Rezaei: Department of Civil and Environmental Engineering, Florida International University, Miami, FL 33174, USA
Elżbieta Macioszek: Department of Transport Systems, Traffic Engineering and Logistics, Faculty of Transport and Aviation Engineering, Silesian University of Technology, 40-019 Katowice, Poland
Parisa Derakhshesh: Department of Urban Design, Faculty of Engineering, North Tehran Branch, Islamic Azad University, Tehran 1651153511, Iran
Hassan Houshyar: Department of Geography, Faculty of Social Science, Payame Noor University, Tehran 193954697, Iran
Elias Ghabouli: Department of Urban Planning & Design, Faculty of Arts and Architecture, Tarbiat Modares University, Tehran 14115, Iran
Amir Reza Bakhshi Lomer: Department of Geography, Birkbeck, University of London, London WC1E 7HX, UK
Ronak Ghanbari: Department of Computer Science, Atmospheric and Environmental Research Lab, University of Iowa, Iowa City, IA 52242, USA
Abdulsalam Esmailzadeh: Department of Social Planning, Faculty of Social Science, Allameh Tabataba’i University, Tehran 1544915113, Iran

Sustainability, 2023, vol. 15, issue 11, 1-18

Abstract: A major component of urban management is studying and evaluating urban resilience in order to minimize the effects of natural hazards. This is because of the increasing number of natural hazards occurring worldwide. A spatial decision support system is presented for modeling urban resilience and selecting resilient zones in response to natural hazards. This system is implemented based on 22 criteria, grouped into three categories: demographics, infrastructure, and environmental. The criteria are then standardized using minimum and maximum methods, and their importance is determined by the analytical hierarchy process (AHP). The resilience maps in various scenarios are prepared using the ordered weighted average (OWA) method. Flow accumulation (distance from fault), vulnerable population density (vulnerable population density), and distance from road network (material type) were regarded as the most important criteria for flood resilience (earthquake resilience) from environmental, demographic, and infrastructure criteria, respectively. There are different areas that are considered to have very low resilience depending on the risk attitude. According a pessimistic scenario, 1% of Tehran’s area has very low resilience, while according to an optimistic scenario, 38% has very low resilience. This system can be used by urban planners and policymakers for the purpose of improving resilience to natural hazards in low-resilience areas.

Keywords: natural hazards; urban resilience; spatial decision support system (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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