Deploying a GIS-based decision model for disaster management support bases (DMSBs) siting in urban environments based on ordered weighted average (OWA) procedure

Abstract

The establishment of Disaster Management Support Bases (DMSBs) is becoming increasingly essential due to the rapid expansion of urban populations and the geographical positioning of Iran's cities in high-risk areas, such as earthquake-prone zones. This study aimed to identify appropriate locations for DMSBs within Region 1 of the Tehran Municipality. In this process, 26 environmental factors derived from prior research on urban disaster management were identified and categorized into five groups: urban texture, transportation, proximity (compatibility), non-proximity (incompatibility), and topography/seismicity. The factors were standardized to ensure comparability by converting different computational units into uniform values. Additionally, their relative weights were determined using the Analytical Hierarchy Process (AHP) method. Subsequent to this, Ordered Weighted Averaging (OWA) modeling was employed, applying various scenarios such as AND, OR, and Weighted Linear Combination (WLC). Through this approach, order weights allowed for controlling the trade-off between factors and varying levels of risk-taking. The next phase of the study involved evaluating DMSB site suitability using zonal land suitability (ZLS) assessments. The findings identified 17 suitable zones for DMSB construction. Following detailed field inspections, five zones were selected as optimal sites spanning different districts. An analysis of the results highlighted that, in contrast to the WLC method, OWA offers a broader range of solutions by enabling adjustments to risk levels and compensation across factors. The study concludes that the decision-making rules and methodology applied in this research are valuable and adaptable for future investigations into suitable sites for DMSBs. OWA has also significantly contributed to disaster knowledge production by providing a versatile, theoretically rich, and practically applicable framework for aggregating information, supporting decision making, and advancing research across multiple scientific and engineering disciplines.