Composite indices are used in many of the traditional approaches to measure risk to natural hazards. However, such indices are often built assuming linear interdependencies between the aggregated components, comprising in this way any realistic representation of the intricate and unseen processes that are behind each component along with their mutual influences when it comes to encapsulate a complex reality in a single construct. In this paper we used a Fuzzy Inference Systems type Mamdami to aggregate physical seismic risk and social vulnerability indicators without assuming linearity. The aggregation is made by establishing rules (if-then type) over the indicators in order to get a fuzzy composite index. We generated solution surfaces representing the outcome of a widely known linear aggregation method and the fuzzy system. Finally we performed a qualitative comparison between both to highlight their differences and possible consequences in terms of risk management.

Peer Reviewed