A fuzzy multiple-attribute decision-making modelling for vulnerability analysis on the basis of population information for disaster management
Research activity and published literature on the reliability and vulnerability analysis of urban areas for disaster management has grown tremendously in the recent past. Population information has played the most important role during the entire disaster management process. In this article, population information was used as the evaluation criterion, and a fuzzy multiple-attribute decision-making (MADM) approach was used to support a vulnerability analysis of the Helsinki area for disaster management. A kernel density map was produced as a result that showed the vulnerable spatial locations in the event of a disaster. Model results were first validated against the original population information kernel density maps. In the second step, the model was validated by using fuzzy set accuracy assessment and the actual domain knowledge of the rescue experts. This is a novel approach to validation, which makes it possible to see how and if computer decision-making models compare to a real decision-making process in disaster management. The validation results showed that the fuzzy model has produced a reasonably accurate result. By using fuzzy modelling, the number of vulnerable areas was reduced to a reasonable scale and compares to the actual human assessment of these areas, which allows resources to be optimised during the rescue planning and operation.