Decision support system for post-disaster resilience

Abstract

Efficient resource allocation after a disaster is critical to enhance the resilience of social infrastructures. This research proposes a post-disaster decision-support system (PDDSS) to make an effective yet efficient decision in timely manner for allocating the resources after the disasters. The location-based PDDSS utilizes the integration of multiple sources of data with a modified Analytical Hierarchical Process (AHP) to enhance the effectiveness of the decision. The various test cases validate the feasibility of the Python programming based PDDSS and demonstrate that the proposed system facilitates disaster recovery to enhance the effectiveness of resource distribution in the post-disaster scenarios.