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The goal of the Scientific Session: “Advances of International Collaboration on M9 Disaster Science” at the 2nd World Bosai Forum (WBF) in Sendai in November 2019 was to share progress on research projects and findings related to an M9 mega-disaster event, building on outcomes from a March 2019 collaborative workshop on M9 disaster science between research partners from the International Research Institute of Disaster Science (IRIDeS)/Tohoku University, University of Washington-Seattle (UW), and the Research Center for Integrated Disaster Risk Management (CIGIDEN). This paper reports on the presentations during the WBF Scientific Session, which shared updates and outputs of research collaborations from different disciplines, following the themes of risk-based planning, structural engineering, tsunami observation and early warning, and tsunami simulation and probabilistic tsunami risk assessment. This international and cross-disciplinary collaboration has led to the advancement of a number of specific research projects in different fields, as well as a robust network of researchers in the three countries. Based in coastal regions facing similar risks of massive earthquakes and tsunami in Japan, the United States, and Chile, it is hoped that ongoing and future collaboration within this network will continue to advance knowledge of disaster science and international disaster risk reduction.

We have developed a new numerical model suitable for rapid and wide-area estimation of tsunami inundation and damage. The model is based on the world-renowned TUNAMI code solving the two-dimensional nonlinear shallow water equations, and enables one-stop simulation of the initial tsunami distribution based on a fault model, tsunami propagation and inundation, and damage estimation. It extends the configuration of the grid system from conventional rectangular regions to polygonal regions so that deployment of high-resolution grids can be confined to the coastal lowland, resulting in remarkably improved efficiency in computation and better precision. For the purpose of real-time implementation of tsunami inundation simulation using a high-performance computing infrastructure, vectorization and MPI parallelization have also been conducted. Moreover, the model was verified and validated through several benchmark problems that the National Tsunami Hazard Mitigation Program, organized by federal agencies and states in the U.S., developed as the quality standards for simulating and assessing tsunami hazard and risk. The newly-developed model is named “Real-time Tsunami inundation (RTi) model,” and its computational performance was examined using the SX-ACE, a vector supercomputer installed at Tohoku University. The results show that it requires only 128 cores of the SX-ACE for implementing six-hour tsunami inundation simulation with a 10-meter grid resolution within 10 minutes for the 700 km long coastline of Kochi Prefecture, Japan. This means that the RTi model is over 10 times more efficient as the conventional tsunami model with the rectangular domains, and it can be inferred that 2,451 cores of the SX-ACE are the overall computational resources needed for real-time tsunami inundation forecast on the whole coastal regions along the Nankai Trough subduction zone, corresponding to the computational performance of 170 Tflop/s. The resources required are equivalent to 24% of all the SX-ACE resources at Tohoku University, indicating the feasibility of real-time tsunami inundation forecast on a regional scale by using the RTi model. Since the Disaster Information System operated by the Cabinet Office of the Japanese Government adopted a function of tsunami damage estimation using the aforementioned numerical model, at the end of this paper, a brief overview of the subsystem for rapidly estimating tsunami damage on a regional scale is described.

The DIM2SEA research project aims to increase urban resilience to large-scale disasters. We are engaged in developing a prototype Dynamic Integrated Model for Disaster Management and Socioeconomic Analysis (DIM2SEA) that will give disaster officials, stakeholders, urban engineers and planners an analytic tool for mitigating some of the worst excesses of catastrophic events. This is achieved by harnessing state-of-the-art developments in damage assessment, spatial simulation modeling, and Geographic Information System (GIS). At the heart of DIM2SEA is an agent-based model combined with post-disaster damage assessment and socioeconomic impact models. The large amounts of simulated spatial and temporal data generated by the agent-based models are fused with the socioeconomic profiles of the target population to generate a multidimensional database of inherently “synthetic” big data. Progress in the following areas is reported here: (1) Synthetic population generation from census tract data into agent profiling and spatial allocation, (2) developing scenarios of building damage due to earthquakes and tsunamis, (3) building debris scattering estimation and road network disruption, (4) logistics regarding post-disaster relief distribution, (5) the labor market in post-disaster urban dynamics, and (6) household insurance behavior as a reflection of urban resilience.

Regional disaster data are important for understanding the characteristics of disasters and for identifying potential mitigation measures. However, many countries have no official disaster database that includes information such as numbers of deaths or damaged buildings for each disaster event. The Global Centre for Disaster Statistics (GCDS) was established to assist countries and organizations in the collection of disaster data. At present, a significant amount of tsunami disaster data are available from Indonesia, which will be used to demonstrate its application for analyzing vulnerability characteristics of historical tsunamis. There are 53 data points covering 13 tsunami events between the year 1861 and 2014. Based on data availability, five tsunami events, namely the 1977 Sumba, the 2004 Indian Ocean, the 2006 Java, the 2010 Mentawai, and the 2011 Great East Japan, were selected. Numbers of deaths and damaged buildings were used in combination with hazard data to estimate vulnerability, defined as the ratio between maximum flow depth against death and building damage ratios. Numbers of evacuees were initially used to estimate actual numbers of exposed population but it was later discovered that this parameter overestimated the exposed population in certain cases. As a result, this study presents the vulnerability characteristics of people and buildings in Indonesia, exposed to unusual or extreme tsunamis, mostly in a condition without or with limited access to official warnings. In brief, a maximum flow depth of 5 m caused an approximate 100% death ratio in the majority of Indonesian tsunamis in this study. On the other hand, death ratio in the 2011 Japan tsunami was limited to 10% because of the early warning and high disaster awareness. Effective disaster risk reduction activities such as official warnings, evacuations, and tsunami education were observed for certain locations. Lastly, adding hazard and population data at the village level is recommended for improving the collection of future tsunami disaster data for the GCDS database.

Japan has suffered significant damage from countless earthquakes throughout its history. Thus, it is important to take prompt and effective measures against major future earthquakes predicted. Among the components of damage, measures against tsunamis are a top priority, as demonstrated by the catastrophic losses caused by the Great East Japan Earthquake (GEJE). To date, many studies have been conducted on tsunamis to investigate detection, prediction of flooding, and models of evacuation behaviors. Therefore, this study sought to integrate the results of increasingly advanced research in various fields to construct a system that generates data on human flow during a tsunami disaster. We proceeded with a scenario assuming the Great Nankai Trough Earthquake and considered Kochi City as a case study to conduct a trial tsunami evacuation simulation. We validated and evaluated the evacuation behaviors as a scheme for utilizing knowledge of the ever-changing conditions of evacuation and conducted a visualization and analysis of the simulation results.

Tsunami disasters can cause serious casualties and damage to social infrastructures. An early understanding of disaster states is required in order to advise evacuations and plan rescues and recoveries. We have developed a real-time tsunami inundation forecast system using a vector supercomputer SX-ACE. The system can complete a tsunami inundation and damage estimation for coastal city regions at the resolution of a 10 m grid size in under 20 minutes, and distribute tsunami inundation and infrastructure damage information to local governments in Japan. We also develop a new configuration for the computational domain, which is changed from rectangles to polygons and called a polygonal domain, in order to effectively simulate in the entire coast of Japan. Meanwhile, new supercomputers have been developed, and their peak performances have increased year by year. In 2016, a new Xeon Phi processor calledKnights Landingwas released for high-performance computing. In this paper, we present an overview of our real-time tsunami inundation forecast system and the polygonal domain, which can decrease the amount of computation in a simulation, and then discuss its performance on a vector supercomputer SX-ACE and a supercomputer system based on Intel Xeon Phi. We also clarify that the real-time tsunami inundation forecast system requires the efficient vector processing of a supercomputer with high-performance cores.

The 2016 magnitude 6.4 Meinong earthquake caused catastrophic damage to peoples lives and properties in Taiwan. Synthetic Aperture Radar remote sensing is a useful tool to rapidly grasp the near real-time building damage to areas affected by the earthquake. Previous studies employed X-band single polarized high-resolution synthetic aperture radar imagery to identify building damage. However, suitable X-band single polarized high-resolution synthetic aperture radar imagery is not always accessible. Therefore, this research applied L-band dual-polarimetric ALOS-2/PALSAR-2 data to analyze the radar scattering characteristics of three types of affected buildings in the 2016 Meinong earthquake. The results show that collapsed buildings are characterized by a weak double-bounce scattering due to a reduced dihedral structure, while the characteristics of slightly damaged buildings are similar to those of undamaged buildings. Furthermore, the discrimination ability of a series of polarimetric, texture, and color features derived from the dual-polarimetric SAR data for three types of buildings affected by the earthquake are quantified based on a statistical analysis using the pixels in the combined areas of layover, shadow, and building footprint of each building. The results of the statistical analysis show that the spaceborne dual-polarimetric ALOS-2/PALSAR-2 images have good potential to distinguish between slightly damaged buildings and collapsed and tilted buildings. However, it is still difficult to distinguish between collapsed and tilted buildings. In addition, the results of the statistical analysis show that the mean value and variance value of the Gray-Level Co-Occurrence Matrix of the span image are two suitable features by which the categories of building damage can be distinguished. The polarimetric and color features demonstrated poorer performance in terms of distinguishing between damage categories than the texture features.

Numerical simulation and modeling became an essential technology in tsunami research and disaster management. Various numerical models were proposed and utilized for the development of tsunami risk assessment, inundation maps, and evacuation plans. The model verification and validation standards would be crucial to ensure sufficient reliability of tsunami risk assessment, inundation maps, as well as a consistency among various efforts. Common approach to ensure sufficient accuracy and reliability of numerical modeling is developing benchmark problems of hydraulic experiments and to use them for numerical model’s verification and validation. To satisfy this requirement, “Tsunami Modeling Hackathon” was held in September 2020 to organize new benchmark problems in numerical modeling of tsunamis and to improve their reliability and accuracy. Hackathon is an intensive-gathering event of computer programmers and others involved in software development to create outcomes by the end of the event. This event was organized by Prof. Tomoyuki Takahashi of Kansai University and his colleagues, who led the tsunami research subcommittee in Japan Society of Civil Engineers (JSCE). Tsunami modeling hackathon, in which about 23 teams and 162 researchers joined, included experiment and modeling teams in seven benchmark problems: urban tsunami inundation, landslide tsunami, tsunami loading on seawalls and coastal structures, sediment transport, drift of floating objects. The modeling groups performed the blind tests to cross-validate and interpret the results of their simulations in seven benchmark problems given by the experiment groups and discussed the improvement. This special issue reports the outcomes of the tsunami modeling hackathon, and includes six papers (five in this issue, one in the regular issue). We hope this issue will provide useful insights for tsunami modelers and contribute to establishing a standardized way to ensure that various tsunami numerical models would be validated through the benchmark problems.

Synthetic Aperture Radar (SAR) remote sensing is a useful tool for mapping earthquake-induced building damage. A series of operational methodologies based on SAR data using either multi-temporal or only post-event SAR images have been developed and used to serve disaster activities. This presents a critical problem: which method is more likely to obtain reliable results and should be adopted for disaster response when both pre- and post-event SAR data are available? To explore this question, this study takes the 2016 Kumamoto earthquake as a case study. ALOS-2/PALSAR-2 SAR images were employed with a machine learning framework to quantitatively compare the performance of building damage mapping using only post-event SAR images and mapping using multi-temporal SAR images. The results show that an overall accuracy of 64.5% was achieved when only post-event SAR images were used, which is 2.3% higher than the overall accuracy when multi-temporal SAR images were used. The estimated building damage ratio for the former and the latter are 29.7% and 31.1%, respectively, which are both close to the building damage ratio obtained from an optical image.

Remote sensing technology is effective for identifying the Remote sensing technology is effective for identifying the extensive damage caused by tsunami disasters. Many methods have been developed to detect building damage at the building unit scale. Of these methods, X-band Synthetic Aperture Radar (SAR) data has a high resolution and is useful to investigate the detailed conditions on the Earth’s surface, although its spatial coverage is relatively small. In contrast, L-band SAR data has a lower resolution, leading to difficulties detecting building damage, although it can cover a broad area. During disasters, it is important to understand the damage across extensive areas in a short time; therefore, it is necessary to develop a method with broad coverage with high accuracy. The primary objective of this study is to develop a method to estimate building damage in tsunami affected areas using L-band SAR (ALOS/PALSAR) data. We developed our method by extending a previously proposed method for X-band SAR (TerraSAR-X) data. This study focused on Sendai City and Watari town in Miyagi Prefecture, where many houses were washed away during the 2011 Tohoku earthquake and tsunami. We verified that the function we developed produced good performance in estimating the number of washed-away buildings, corresponding with ground truth data with a Pearson correlation coefficient of 0.97. Verification was conducted in another study area, which yielded a Pearson correlation coefficient of 0.87.

This study aims to compress web news, delivered as a big-data source after disasters. In this paper, article clustering, which is a combination of conventional means and an algorithm that selects the representative articles of each cluster, is designed and adopted. Experiments are conducted by evaluators. The proposed algorithm is in accord with the evaluators for 50s% of the clustering and for about 30s% to 40s% of the representative-article selection.

Earthquake-induced building damage assessment is an indispensable prerequisite for disaster impact assessment, and the increasing availability of high-resolution Synthetic Aperture Radar (SAR) imagery has made it possible to construct damaged building inventories soon after earthquakes strike. However, the shortage of pre-seismic SAR datasets and the lack of available building footprint data pose challenges for rapid building damage assessment. Taking advantage of recent advances in machine learning algorithms, this study proposes an object-based building damage assessment methodology that uses only post-event SAR imagery. A Random Forest machine learning-based object classification, a simplified approach to the extraction of built-up areas, was developed and tested on two ALOS2/PALSAR-2 dual polarimetric SAR images acquired in affected areas soon after the 2015 Nepal earthquake. In addition, a series of texture metrics as well as the random scattering metric and reflection symmetry metric were found to significantly enhance classification accuracy. The feature selection was found to have a positive effect on overall performance. Moreover, the proposed Random Forest framework resulted in overall accuracies of 93% with a kappa coefficient of 0.885 when the object scale of 60 × 60 pixels and 15 features were adopted. A comparative experiment with the k-nearest neighbor framework demonstrated that the Random Forest framework is a significant step toward the achievement of a balanced, two-class classification.

This paper reports the latest outcomes of the project “Establishing the Advanced Disaster Reduction Management System by Fusion of Real-time Disaster Simulation and Big Data Assimilation” that started in 2014. The objectives of targeting various kinds of damage due to earthquakes and tsunami, fusion of large-scale high-resolution numerical simulation, effective processing and analysis of big data from various observations, and data assimilation were achieved. The outcomes will be utilized to create the world’s first real-time simulation and big data analysis basis that would potentially assist with designing preliminary measures based on quantitative data and disaster responses to a disaster. Case studies using recent disasters were used in this endeavor and validation were performed. In the future, environments that rapidly provide information on possible damage situations in real time for public agencies, corporations, and citizens facing a catastrophic disaster in Japan will be developed by integrating these studies.

A project titled “Establishing the advanced disaster reduction management system by fusion of real-time disaster simulation and big data assimilation,” was launched as Core Research for Evolutional Science and Technology (CREST) by the Japan Science and Technology Agency (JST). Intended to save as many lives as possible in future national crises involving earthquake and tsunami disasters, the project works on a disaster mitigation system of the big data era, based on cooperation of large-scale, high-resolution, real-time numerical simulations and assimilation of real-time observation data. The world’s most advanced specialists in disaster simulation, disaster management, mathematical science, and information science work together to create the world’s first analysis platform for real-time simulation and big data that effectively processes, analyzes, and assimilates data obtained through various observations. Based on quantitative data, the platform designs proactive measures and supports disaster operations immediately after disaster occurrence. The project was launched in 2014 and is working on the following issues at present.Sophistication and fusion of simulations and damage prediction models using observational big data: Development of a real-time simulation core system that predicts the time evolution of disaster effect by assimilating of location information, fire information, and building collapse information which are obtained from mobile terminals, satellite images, aerial images, and other new observation data in addition to sensing data obtained by the undersea high-density seismic observation network.Latent structure analysis and major disaster scenario creation based on a huge amount of simulation results: Development of an analysis and extraction method for the latent structure of a huge amount of disaster scenarios generated by simulation, and creation of severe scenarios with minimum “unexpectedness” by controlling disaster scenario explosion (an explosive increase in the number of predicted scenarios).Establishment of an earthquake and tsunami disaster mitigation big data analysis platform: Development of an earthquake and tsunami disaster mitigation big data analysis platform that realizes analyses of a huge number of disaster scenarios and increases in speed of data assimilation, and clarifies the requirements for operation of the platform as a disaster mitigation system.The project was launched in 2014 as a 5-year project. It consists of element technology development and system fusion, feasibility study as a next-generation disaster mitigation system (validation with/without introduction of the developed real-time simulation and big data analysis platform) in the affected areas of the Great East Japan Earthquake, and test operations in affected areas of the Tokyo metropolitan earthquake and the Nankai Trough earthquake.

There are increasing expectations that social sensing, especially the analysis of social media text as a source of information for COP (Common Operational Picture), is useful for decision-making about responses to disasters. This paper reports on a geo-information and content analysis of three million Twitter texts sampled from Japanese Twitter accounts for one month before and after the 2011 Great East Japan Earthquake disaster. The results are as follows. 1) The number of Twitter texts that include geotag (latitude and longitude information) is too small for reliable analysis. However, a method of detecting the tweet’s location from the tweet’s text using GeoNLP (an automatic technology to tag geo-information from natural language text) is able to identify geo-information, and we have confirmed that many tweets were sent from stricken areas. 2) A comparison of Twitter data distribution before and after the disaster occurred does not identify clearly which areas were significantly affected by the disaster. 3) There were very few Twitter texts that included information about the damage in affected areas and their support needs.

The object-based method we developed to estimate building damage uses high-resolution synthetic aperture radar (TerraSAR-X) data from the 2011 Tohoku earthquake and tsunami. The damage function we developed involves the relationship between changes in the sigma nought values of pre- and postevent TerraSAR-X data and the damage ratio of washed-away buildings. We confirmed that the function performed as expected by estimating the number of washed-away buildings in homogeneous areas, agreeing well with ground truth data verified by a Pearson fs correlation coefficient of 0.99. The same damage function applied at another test site yielded a Pearson's correlation coefficient of 0.98. These results are sufficient to ensure transferability. We then simplified and semiautomated these processes in an ArcGIS environment, estimating building damage in the city of Sendai within 26 minutes.

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.

Based on a comprehensive study of the lessons from the 2011 Great East Japan earthquake and tsunami that devastatedmuch of the northeast Japan (Tohoku), we discuss insights into reconstruction and tsunamiresilient communities. Through field measurement, remote sensing, and numerical modeling approaches with spatial information sciences, we clarified the hydrodynamic characteristics of the tsunami inundation flow, the tsunami fragility curves. As observed in devastated areas and the analysis of the tsunami fragility curves, buildings were especially vulnerable when the local flow depth exceeded 2 m, while a 6 m flow depth would cause everything to be washed away. The findings and results lead to implications for land use management and relocation planning for reconstructing tsunami-resilient coastal communities. As a case study, we conducted the numerical tsunami modeling in Sendai city to evaluate the performance of the reconstruction plan based on the land use and coastal protection scenarios.

The 2011 Great East Japan earthquake and tsunami was a magnitude 9.0 Mw event that destroyed most structural tsunami countermeasures. However, approximately 90% of the estimated population at risk from the tsunami survived due to a rapid evacuation to higher ground or inland. Thus, tsunami evacuation is the most effective measure to reduce casualties. In this paper, we applied a new developed evacuation model integrated with the numerical simulation of tsunami for casualty estimation. This tool is to support decisions in disaster management and disaster prevention education. The model was developed in NetLogo, a multi-agent programming language and modeling environment for simulating complex phenomena. Geographic Information Systems (GIS) datasets are used as spatial input information for road and shelter locations. The TUNAMI model of Tohoku University is used for the integration of tsunami numerical simulation results. In this paper, the study is performed in a tsunami threatened urban area of Callao, Peru, called La Punta. Results show the various contributions of the model to disaster management and scenario analysis. Among the contributions are the casualty estimation in a tsunami risk area and the analysis of the spatial distribution of vertical evacuation shelters.

Several large earthquakes have recently occurred along the Sumatra-Java subduction zone, the 2004 great Sumatra-Andaman earthquake, the 2005 great Nias earthquake, the 2006 West Java tsunami earthquake, the 2007 great Bengkulu earthquake, and the 2010Mentawai tsunami earthquakes. Serious tsunami disasters were caused by the great underthrust earthquakes which ruptured the plate interface near the trench such as the 2004 Sumatra-Andaman, 2006West Java, 2010Mentawai earthquakes. At Palabuhanratu, maximum tsunami height distribution and inundation areas were computed from expected fault models located near the Java trench. The results shows that the most populated areas of Palabuhanratu would be severely damaged by the expected tsunami caused by the fault model of Mw 8.5. After discussing tsunami disaster mitigation measures with the local government, the result of tsunami inundation area in this study were used to decide tsunami evacuation areas and evacuation routes. The local government also installed tsunami evacuation sign boards near the coast.

I am honored to receive the Third JDR Award, and I am very grateful to the editorial committee members and staff of the Journal of Disaster Research (JDR). I also wish to express my gratitude to all the authors who contributed to the papers in three special issues entitled “Disaster and Big Data,” which were published in 2016, 2017, and 2018. I really enjoyed the opportunities I had in guest editing the 36 excellent papers in the special issues. My approximately 30 publications in the JDR include the outcomes from the international joint research projects of SATREPS Indonesia, Peru, and Chile, so I would like to share the delight of receiving the JDR award with all the research members of the projects and thank them for their fruitful collaboration. The Journal of Disaster Research all across the wide and comprehensive scope of its outstanding research has provided topical and valuable insights as well as significant implications for disaster reduction and management. I do hope I have contributed to making the JDR an excellent publication of valuable disaster research findings from around the world.

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.

Tsunami fragility (fragility curve, or fragility function) is a new measure, we propose, for estimating structural damage and fatalities due to tsunami attack, by integrating satellite remote sensing, field survey, numerical modeling, and historical data analysis with geographic information system (GIS). Tsunami fragility is expressed as the structural damage probability or fatality ratio related to hydrodynamic features of tsunami inundation flow, such as inundation depth, current velocity and hydrodynamic force. It expands the capability of estimating potential tsunami damage in a quantitative manner.

The Lisbon Earthquake of November 1, 1755, one of the most catastrophic events that has ever occurred in Portugal, Spain, or Morocco, caused severe damage and many casualties. The tsunami generated by this earthquake is well documented in historical accounts, it was reported throughout the North Atlantic Ocean, as it reached not only Portugal, Spain, and Morocco, but also the Madeira and Azores Archipelagos, England, Ireland, and the Caribbean. In spite of the importance of this event, the source of the tsunami remains unknown. In this paper, the authors reevaluate some of the historical tsunami travel times obtained by previous authors. Based on these times, wave ray analysis is used to determine the location of the tsunami source area. These results, combined with turbidites obtained by previous authors at the Tagus and Horseshoe Abyssal Plains, lead to the conclusion that the source of the 1755 Lisbon Tsunami could be located in the area of the Gorringe Bank. Then, a hydrodynamic simulation is carried out with this area presupposed as the source. The numerical model results provide good agreement when compared with both historical and sedimental records. However, in the past, the Gorringe Bank has been dismissed as the source of this tsunami for several reasons. Therefore, these issues are discussed and discredited. As a consequence of all these facts, it can be concluded that the origin of the 1755 Lisbon Earthquake and Tsunami could be located in the area of the Gorringe Bank.

Chain disasters involving flammable materials colliding with tsunami-driven objects are not well understood. Despite the pressing need for knowledge on effects, scenarios have not been clarified. We reviewed the process of complex tsunami disasters, focusing on the spread of large fires caused by the destruction of oil storage tanks and tsunami inundations during the 1964 Niigata earthquake. We propose a model for simulating oil spread on tsunami inundation flow using a 2-layer hydrodynamic model preliminarily tested in a simple numerical wave tank. We discuss our results and projected perspectives.

The typical mechanism behind the generation and propagation of the 2004 Indian ocean tsunami is introduced through computer graphics, showing how it propagated across the ocean. The damage it caused in countries on the Indian ocean is summarized to suggest the lessons to be leaned in mitigating similar disasters in the future. And we investigated its impact on not only coastal community but also the environment, including coral and vegetation by a field survey and cover research required in tsunami engineering.

6 years have passed since the 2011 Great East Japan earthquake. Many new findings, insights and suggestions have been made and were implemented in disaster observation, sensing, simulation, and damage determination. The challenges for disaster mitigation against future catastrophic natural disasters, such as the Tokyo metropolitan earthquake and Nankai Trough earthquake, are how we share the visions of the possible impacts and prepare for mitigating the losses and damages, and how we enhance society’s disaster resilience. A huge amount of information called “disaster big data” obtained, which are related to the dynamic flow of a large number of people, vehicles and goods inside and outside the affected areas. This has dramatically facilitated our understanding of how our society has responded to the unprecedented catastrophes. The key question is how we use big data in establishing the social systems that respond promptly, sensibly and effectively to natural disasters, and in withstanding the adversities with resilience. Researchers with various expertise are working together under the 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 earthquake and tsunami that occur and progress in a chained or compound manner and to create new technologies to lead responses and disaster mitigation measures that encourages the society to get over the disaster. This special issue titled “Disaster and Big Data Part 2,” including 13 papers, aims to share the recent progress of the project as the sequel of Part 1 published in March 2016. As an editor 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.

In the years that have passed since the 2011 Great East Japan earthquake, many new findings, insights and suggestions have been made in disaster observation, sensing, simulation, and damage determination on the damage scene. Based on the lessons, challenges for disaster mitigation against future catastrophic natural disasters such as the anticipated Tokyo metropolitan and Nankai Trough earthquakes are made on how we will share visions of potential impact and how we will maximize society's disaster resilience. Much of the ``disaster big data" obtained is related to the dynamic flow of large populations, vehicles and goods inside and outside affected areas. This has dramatically facilitated our understanding of how society has responded to unprecedented catastrophes. The key question is how we will use big data in establishing social systems that respond promptly, sensibly and effectively to natural disasters how this understanding will affect adversity and resilience. Researchers from a wide variety of fields are now working together under the collaborative JST CREST project entitled ``Establishing the most advanced disaster reduction management system by fusion of real-time disaster simulation and big data assimilation." One objective of this project is to identify potential disaster scenarios related to earthquake and tsunami progress in a chained or compound manner and to create new techniques for responsive disaster mitigation measures enabling society to recover. This special issue on disaster and big data consists of 11 papers detailing the recent progress of this project. As an editor 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.