A Framework to Integrate Social Media and Authoritative Data for Disaster Relief Detection and Distribution Optimization

In this paper, we propose an interdisciplinary approach to (natural) disaster relief
management. Our framework combines dynamic and static databases, which
consist of social media and authoritative data of an afflicted region, respectively,
to model rescue demand during a disaster situation. Using Global Particle Swarm
Optimization and Mixed-Integer Linear Programming, we then determine the optimal
amount and locations of temporal rescue centers. Furthermore, our disaster
relief system identifies an efficient distribution of supplies between hospitals and
rescue centers and rescue demand points. By leveraging the temporal dimension of
the social media data, our framework manages to iteratively optimize the disaster
relief distribution.