Social vulnerability assessments are largely ignored when compared with biophysical vulnerability assessments. This is mainly due to the fact that there are more difficulties in quantifying them. Aiming at several pitfalls still existing in the Hoovering approach which is widely accepted, a suitable modified model is provided. In this modified model, the integrated vulnerability is made an analogy to the elasticity coefficient of a spring, and an objective evaluation criterion is established. With the evaluation criterion, the assessment indicators of social vulnerability are filtered and their weight assignments are accomplished. There is an application in the city of Changsha where floods occur often. With the relative data from the PICC Hunan Province Branch, a generalized regression neural network model is established in Matlab 7.0 and used to evaluate a company's flood social vulnerability index (SoVI). The results show that the average flood social vulnerability in Yuhua district is the highest, while Yuelu district is the lowest. It is good for disaster risk management and decision-making of insurance companies.
