Evaluating flood vulnerability is being an essential factor in flood risk assessment and damage evaluation. Since it is influenced by several spatial factors covering exposure, susceptibility, and resilience, thus evaluating methods vary and are complicated. This study proposed Flood Vulnerability Index (FVI) evaluation by applying the fuzzy set theory involving subjective uncertainties and ambiguities of judgments. Five sub-catchments in Bangkok were selected, and 14-districts information was gathered. The flood events on June 21, 2016, and October 13, 2017, were simulated by applying a Storm Water Management Model (SWMM) mathematical model with the spatial physical characteristics such as topographic data, land-use data, drainage system, rainfall distribution, and inundation level. The flood vulnerability factor and fuzzy Membership Function (MF) were defined. It changed the magnitude of flood vulnerability factors to linguistic function. The fuzzy rule-based was then constructed in a fuzzy inference engine where the rainfall, drainage performance, population density, traffic condition, and garbage management were set as the primary variables. Further, the spatial FVIs were evaluated by Fuzzy Inference Systems (FIS). The evaluated FVIs were compared with the prior version of FVI, which developed under the consideration of just the physical characteristics and the annual rainfall. The comparisons with the actual floods reveal that the improved FVIs obtained from the proposed method are more consistent with the actual event conditions. Besides, the improved FVI of 14 districts in 5 sub-catchments that range from 0.81 to 0.91 also indicates a very high vulnerability to flooding due to a very high impermeable ratio and the lowest efficiency of drainage systems. The sensitivity analysis suggests that the FVI are sensitive to changes in garbage collection management, especially in the very high impermeable area, follows by drainage efficiency, traffic index, and population density, respectively.
