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

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.

Automated summary

A spatial decision support system is developed to model urban resilience and select resilient zones in response to natural hazards. It uses 22 criteria grouped into demographics, infrastructure, and environmental categories, which are standardized and weighted using the minimum and maximum methods and the analytical hierarchy process (AHP). Resilience maps are created using the ordered weighted average (OWA) method. The system aids urban planners and policymakers in improving resilience in low-resilience areas.