A framework to integrate social media and authoritative data for disaster relief detection and distribution optimization. (October 2019)

• Record Type: Journal Article
• Authors: Schempp, Timothy; Zhang, Haoran; Schmidt, Alexander; Hong, Minsung; Akerkar, Rajendra
• Is Part Of: International journal of disaster risk reduction. Volume 39(2019)
• Publication Date: 2019-10
• Subjects: Disaster relief -- Disaster management -- Social media -- Optimization; Emergency management -- Periodicals; Risk management -- Periodicals; Disaster relief -- Periodicals; Hazard mitigation -- Periodicals; 363.34
• DOI: 10.1016/j.ijdrr.2019.101143
• Languages: English
• ISSNs: 2212-4209

Abstract

Abstract: 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.