Special Issue on Disaster and Big Data Part 4
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
468-1 Aoba, Aramaki, Aoba-ku, Sendai 980-0845, Japan
The 2011 Great East Japan Earthquake and Tsunami disaster taught us many lessons. Many new findings, insights, and suggestions have been made and implemented in damage determination and in disaster observation, sensing, and simulation. The challenges in terms of mitigating damage from future catastrophic natural disasters, such as the expected Metropolitan Tokyo Earthquake and Nankai Trough Earthquake and Tsunami, are how we share the visions of the possible impacts and prepare to mitigate loss and damage, how we enhance society’s disaster resilience and the ability of society and social systems to prepare well, how we respond promptly and effectively to natural disasters, and how we apply lessons learned to future disaster management.
In recent years, a huge amount of information known as “disaster big data,” including data related to the dynamic movement of a large number of people, vehicles, and goods as IoT, has been obtained to understand how our society responds to natural disasters, both inside and outside the affected areas. The key question is how to utilize disaster big data to enhance disaster resilience.
Researchers with various areas of expertise are working together in a collaborative project called JST CREST: “Establishing the Most Advanced Disaster Reduction Management System by Fusion of Real-Time Disaster Simulation and Big Data Assimilation.” The project aims to identify possible disaster scenarios caused by earthquakes and tsunamis that occur and progress in a chained or compound manner, as well as to create new technologies to lead responses and disaster mitigation measures that help societies recover from disasters.
Since 2016, we have published three special issues entitled “Disaster and Big Data,” and now we will publish a fourth one which includes 10 research papers and 1 report. These aim to share the recent progress of the project as a sequel to Part 3, published in March 2018. As a guest editor of this issue, I would like to express our deep gratitude for the insightful comments and suggestions made by the reviewers and members of the editorial committee. It is my hope that the fruits of everyone’s efforts and outcomes will be utilized in disaster management efforts to mitigate damage and losses from future catastrophic disasters.
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