Submission: November 15, 2018
Notification of decision: February 15, 2019
Final acceptance notification: April 30, 2019
In recent years, there have been an increasing number of large-scale crises, such as natural disasters or armed attacks, that have had major effect on individual lives and infrastructure, and have caused the devastation to communities. During these mass emergencies, victims, responders, and volunteers increasingly use social media and mobile devices to provide real-time situation updates, i.e., reports on damage, or request and offer help. This has generated vast volumes of crisis data in different forms and from different sources. There are a number of challenges associated with the near-real-time processing of vast volumes of information in a way that makes sense for people directly affected, for volunteer organizations, and for official emergency response agencies. There is a growing need for developing new AI techniques that process large-scale crisis data to gain a "big picture" of an emergency, detect and predict how a disaster could develop, analyze the impact of disasters and the effect of negative externalities in a cyber-physical society, and assist in disaster response and resource allocation. These AI techniques can allow better preparation for emergency situations, help save lives and reduce loss, limit economic impact, provide effective disaster relief, and make communities stronger and more resilient.
This special issue is to call for research initiatives toward the next generation disaster management that leverage AI to strengthen disaster resilience at all levels of society in the new age of mass emergencies. Topics of interest include (but are not limited to):
* harnessing big data from the Web or Social Web to facilitate disaster and risk management
* emergency- or disaster-related data mining and knowledge management methodologies
* extracting actionable insights from crisis data to support decision making
* integrating community-provided data with data from official sources
* digital volunteering and all forms of citizen participation in disaster response
* analyzing and/or establishing trust in local and global communities
* understanding and/or designing socio-technical systems in mass emergencies
* building resilience to disasters through the Internet of Things and ubiquitous intelligence
* successful applications of disaster management and response systems
* fairness, accountability, and transparency of AI systems for emergency/disaster response
Yu-Ru Lin ([log in to unmask]), University of Pittsburgh, USA
Carlos Castillo ([log in to unmask]), Pompeu Fabra University, Spain
Jie Yin ([log in to unmask]), The University of Sydney, Australia
* For general information about the special issue, contact Yu-Ru Lin (include the keyword "Intelligent Systems AI & Disaster" in the subject line) at [log in to unmask].
* For general author guidelines, see www.computer.org/intelligent/author.
* For submission details, see [log in to unmask].
* To submit an article, go to https://mc.manuscriptcentral.com/is-cs (log in and then select "Special Issue on AI & Disaster").