Special Issue on Disaster and Big Data Part 3
Project Leader of JST CREST “Establishing the most advanced disaster reduction management system by fusion of real-time disaster simulation and big data assimilation”
Professor, International Research Institute of Disaster Science (IRIDeS), Tohoku University
Aoba 468-1, Aramaki, Aoba-ku, Sendai 980-0845, Japan
The 2011 Great East Japan Earthquake and Tsunami Disaster left behind many lessons to learn, and there have since been many new findings and insights that have led to suggestions made and implemented in disaster observation, sensing, simulation, and damage determination. The challenges for mitigating the damage from future catastrophic natural disasters, such as the Tokyo Metropolitan Earthquake or the Nankai Trough Earthquake and Tsunami, are in how we share our visions of the possible impacts, how we prepare to mitigate the losses and damages, and how we enhance society’s disaster resilience.
The huge amount of information obtained, called “disaster big data,” is related to the dynamic movement, as IoT, of a large number people, vehicles, and goods from inside and outside the affected areas. This has dramatically facilitated our understanding of how our society has responded to unprecedented catastrophes. The key question is how to utilize big data in establishing social systems that respond promptly, sensibly, and effectively to natural disasters, and in withstanding adversity with resilience.
Researchers with various types of expertise are working together under a collaborative project called JST CREST “Establishing the advanced disaster reduction management system by fusion of real-time disaster simulation and big data assimilation.” The project aims to identify possible earthquake and tsunami disaster scenarios that occur and progress in a chained or compound manner and to create new technologies to lead responses and disaster mitigation measures to help society to recover from disasters.
As we have published two previous special issues entitled “Disaster and Big Data” since 2016, this issue is our third. Included are 14 papers that aim to share the recent progress of the project as the sequel to Part 2, published in March 2017. As one of the guest editors of this issue, I would like to express our deep gratitude for the insightful comments and suggestions made by the reviewers and the members of the editorial committee. I do hope that this work will be utilized in disaster management efforts to mitigate the damage and losses in future catastrophic disasters.
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