A Spatial Decision Support Approach for Flood Vulnerability Analysis in Urban Areas: A Case Study of Tehran

Abstract

Preparedness against floods in a hazard management perspective plays a major role in the pre-event phase. Hence, assessing urban vulnerability and resilience towards floods for different risk scenarios is a prerequisite for urban planners and decision makers. Therefore, the main objective of this study is to propose the design and implementation of a spatial decision support tool for mapping flood vulnerability in the metropolis of Tehran under different risk scenarios. Several factors reflecting topographical and hydrological characteristics, demographics, vegetation, land use, and urban features were considered, and their weights were determined using expert opinions and the fuzzy analytic hierarchy process (FAHP) method. Thereafter, a vulnerability map for different risk scenarios was prepared using the ordered weighted averaging (OWA) method. Based on our findings from the vulnerability analysis of the case study, it was concluded that in the optimistic scenario (ORness = 1), more than 36% of Tehran’s metropolis area was marked with very high vulnerability, and in the pessimistic scenario (ORness = 0), it was less than 1%was marked with very high vulnerability. The sensitivity analysis of our results confirmed that the validity of the model’s outcomes in different scenarios, i.e., high reliability of the model’s outcomes. The methodical approach, choice of data, and the presented results and discussions can be exploited by a wide range of stakeholders, e.g., urban planners, decision makers, and hydrologists, to better plan and build resilience against floods.

A Scenario-Based Multi-Criteria Decision-Making Approach for Allocation of Pistachio Processing Facilities: A Case Study of Zarand, Iran

“…Table 2 shows the compatibility index values: In this study, the OWA method was used to model land suitability for the development of pistachio processing facilities in different scenarios. In past studies, this method was used in various applications such as potential of renewable energies [4,28], vulnerability and resilience [15,59], and modeling the physical growth of cities [60]. OWA is a multi-criteria integration operator developed by Yager [61].…”

“…Determining the optimal location for the construction of facilities depends on a set of different factors, including accessibility and environmental, economic, social, etc., criteria [13,14]. Spatial decision making prevents decision makers from focusing on one criterion and losing sight of the others [15]. The integration of GIS and MCDA helps the decision maker to perform decision analysis functions such as location ranking [16].…”

Using Artificial Neural Networks to Assess Earthquake Vulnerability in Urban Blocks of Tehran

“…By combining spatial factors such as exposure, sensitivity, and adaptive capacity into a multi-criteria decision-making framework, combined with machine learning methods in a GIS setting, this study seeks to provide this information. We need to consider a process that combines and transforms spatial data (metric maps) and values associated with people's judgment (priority of decision-makers) in order to obtain valuable information for decision-making [13,14]. GIS, on the other hand, is a valuable tool for storing, manipulating, analyzing, and managing spatial data [15,16].…”

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

“…Similarly, floods are also a serious natural hazard which are mainly caused by changes in land use, vegetation removal, soil erosion, drainage system limitations, and the occupation of floodplains or flood-prone areas [9]. It is anticipated that the number of people who will be affected by river floods worldwide will increase to 54 million by 2030 as a result of socio-economic development and climate change [10]. Therefore, it is important to take proactive steps to mitigate the risk of floods to protect communities from potential devastation.…”

“…For the past few decades, OWA-based GIS has been utilized for land use analysis such as health care [32], residential-quantity assessment [33], natural-based tourism [34], parking [35], and urban traffic management [36]. GIS-OWA integration has been used for a variety of socio-economic applications, including environmental monitoring [37], natural hazards [10,38], renewable energy [22,39], water resource management [40], and landfill location [41].…”