The traditional approaches to environmental risk analysis based on first principle methods don't cover assessment systemic vulnerability, a meaningful component of the natural hazard. We argue that complex task can be accomplished with a case-based approach through a process of pairwise preference elicitation. Up to now the main restriction of the case-based risk assessment was related to the scalability issue and the cognitive overload of the experts. In this paper we propose a methodology based on a mixed initiative strategy that combines the user preference elicitation and the machine rank approximation. The work includes both the case-based model and the related computational tools. We illustrate how a boosting algorithm can effectively estimate pairwise preferences and reduce the effort of the elicitation pro?cess. Experimental results, both on artificial data and on a real world in the problem domain of civil defence, showed that a good trade-off can achieved between accuracy of the estimated preferences, and elicitation effort of user