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Tsunamis, though infrequent, wield devastating power on coastal regions, necessitating accurate simulation techniques to comprehend their impact and enhance disaster preparedness. Despite significant advances in modern computational fluid dynamics, these simulations continue to pose considerable challenges, including numerical intricacies and substantial computational costs. Owing to its remarkable computational efficiency and high performance, the lattice Boltzmann method (LBM) is being increasingly used for simulating large-scale tsunamis. This review paper systematically surveys the application of the free-surface flow model using the LBM to simulate tsunamis, covering its theoretical underpinnings, numerical implementation, and validation against real-world events. Furthermore, the paper discusses challenges, limitations, and ongoing developments in the application of the LBM for tsunami simulations, elucidating avenues for future research. By amalgamating insights from diverse studies, this review underscores the role of the LBM as a tool for advancing our understanding of tsunamis and increasing coastal resilience. HIGHLIGHTS: Lattice Boltzmann method investigated for tsunami simulations Challenges, limitations, and ongoing developments in tsunami simulation discussed Systematical survey of the application of the free-surface flow model Theoretical underpinning, numerical implementation, and real-world validation [ABSTRACT FROM AUTHOR]

In this paper, we propose a digital model to run an evacuation simulation that reflects the road network blockage caused by the landslide and river flooding damage in Marumori-machi, Miyagi Prefecture, which was severely damaged by Typhoon No. 19 in 2019. In particular, we propose an evacuation agent simulation model that can be extended in the future to scenarios related to disaster response decisions, education, and awareness on the part of residents and can reproduce the evacuation agent situation of a real disaster. The method adjusts a set of parameters of vehicles and pedestrian agents to reproduce the evacuation situation. Then, using the parameter set, we perform the agent simulations under different scenarios varying the time of disaster occurrence and evacuation. Finally, we evaluate the changes in the number of people who have completed their evacuation, the number of victims, etc. The results of the scenarios with different disaster occurrence times showed that the final evacuation rate situation improved by at least 1% (about 170 people who completed evacuation) during daylight time compared to nighttime. The relationship between sunset time and the start of evacuation was qualitatively and quantitatively demonstrated to be supported. It was also confirmed that the evacuation situation did not change much with the time of the evacuation announcement. These results show trends limited to the present study's disasters and scenarios and do not necessarily provide generalized findings for disaster response. However, the results indicate that applying the proposed methodology to a greater number of disasters and scenario conditions could lead to better analysis and optimization of disaster response. Interviews with government disaster management officials in the target areas suggest that confirming the effectiveness of disaster response while visualizing the distribution of disaster risk in the areas from a bird's eye view, as in this study, could enhance existing response plans. This approach may also present information comprehensibly for staff and residents who did not experience the disaster firsthand, simulating the experience for better understanding. [ABSTRACT FROM AUTHOR]

This study proposes a new method for the uncertainty estimation of coseismic slip distribution on the plate interface deduced from real-time global navigation satellite system (GNSS) data and explores its application for tsunami inundation prediction. Jointly developed by the Geospatial Information Authority of Japan and Tohoku University, REGARD (REal-time GEONET Analysis system for Rapid Deformation monitoring) estimates coseismic fault models (a single rectangular fault model and slip distribution model) in real time to support tsunami prediction. The estimated results are adopted as part of the Disaster Information System, which is used by the Cabinet Office of the Government of Japan to assess tsunami inundation and damage. However, the REGARD system currently struggles to estimate the quantitative uncertainty of the estimated result, although the obtained result should contain both observation and modeling errors caused by the model settings. Understanding such quantitative uncertainties based on the input data is essential for utilizing this resource for disaster response. We developed an algorithm that estimates the coseismic slip distribution and its uncertainties using Markov chain Monte Carlo methods. We focused on the Nankai Trough of southwest Japan, where megathrust earthquakes have repeatedly occurred, and used simulation data to assume a Hoei-type earthquake. We divided the 2951 rectangular subfaults on the plate interface and designed a multistage sampling flow with stepwise perturbation groups. As a result, we successfully estimated the slip distribution and its uncertainty at the 95% confidence interval of the posterior probability density function. Furthermore, we developed a new visualization procedure that shows the risk of tsunami inundation and the probability on a map. Under the algorithm, we regarded the Markov chain Monte Carlo samples as individual fault models and clustered them using the k-means approach to obtain different tsunami source scenarios. We then calculated the parallel tsunami inundations and integrated the results on the map. This map, which expresses the uncertainties of tsunami inundation caused by uncertainties in the coseismic fault estimation, offers quantitative and real time insights into possible worst-case scenarios. [ABSTRACT FROM AUTHOR]

Emergency responders require accurate and comprehensive data to make informed decisions. Moreover, the data should be acquired and analyzed swiftly to ensure an efficient response. One of the tasks at hand post-disaster is damage assessment within the impacted areas. In particular, building damage should be assessed to account for possible casualties, and displaced populations, to estimate long-term shelter capacities, and to assess the damage to services that depend on essential infrastructure (e.g. hospitals, schools, etc.). Remote sensing techniques, including satellite imagery, can be used to gathering such information so that the overall damage can be assessed. However, specific points of interest among the damaged buildings need higher resolution images and detailed information to assess the damage situation. These areas can be further assessed through unmanned aerial vehicles and 3D model reconstruction. This paper presents a multi-UAV coverage path planning method for the 3D reconstruction of postdisaster damaged buildings. The methodology has been implemented in NetLogo3D, a multi-agent model environment, and tested in a virtual built environment in Unity3D. The proposed method generates camera location points surrounding targeted damaged buildings. These camera location points are filtered to avoid collision and then sorted using the K-means or the Fuzzy C-means methods. After clustering camera location points and allocating these to each UAV unit, a route optimization process is conducted as a multiple traveling salesman problem. Final corrections are made to paths to avoid obstacles and give a resulting path for each UAV that balances the flight distance and time. The paper presents the details of the model and methodologies, and an examination of the texture resolution obtained from the proposed method and the conventional overhead flight with the nadir-looking method used in 3D mappings. The algorithm outperforms the conventional method in terms of the quality of the generated 3D model. [ABSTRACT FROM AUTHOR]

Rapid estimation of the coseismic fault model for medium-to-large-sized earthquakes is key for disaster response. To estimate the coseismic fault model for large earthquakes, the Geospatial Information Authority of Japan and Tohoku University have jointly developed a real-time GEONET analysis system for rapid deformation monitoring (REGARD). REGARD can estimate the single rectangular fault model and slip distribution along the assumed plate interface. The single rectangular fault model is useful as a first-order approximation of a medium-to-large earthquake. However, in its estimation, it is difficult to obtain accurate results for model parameters due to the strong effect of initial values. To solve this problem, this study proposes a new method to estimate the coseismic fault model and model uncertainties in real time based on the Bayesian inversion approach using the Markov Chain Monte Carlo (MCMC) method. The MCMC approach is computationally expensive and hyperparameters should be defined in advance via trial and error. The sampling efficiency was improved using a parallel tempering method, and an automatic definition method for hyperparameters was developed for real-time use. The calculation time was within 30 s for 1 × 106 samples using a typical single LINUX server, which can implement real-time analysis, similar to REGARD. The reliability of the developed method was evaluated using data from recent earthquakes (2016 Kumamoto and 2019 Yamagata-Oki earthquakes). Simulations of the earthquakes in the Sea of Japan were also conducted exhaustively. The results showed an advantage over the maximum likelihood approach with a priori information, which has initial value dependence in nonlinear problems. In terms of application to data with a small signal-to-noise ratio, the results suggest the possibility of using several conjugate fault models. There is a tradeoff between the fault area and slip amount, especially for offshore earthquakes, which means that quantification of the uncertainty enables us to evaluate the reliability of the fault model estimation results in real time. [ABSTRACT FROM AUTHOR]

On September 1, 1923, the Great Kanto Earthquake struck the Tokyo metropolitan area of Japan. It was an extremely powerful earthquake that caused a great fire. The death toll reached approximately 105,000, and the economic loss is estimated to have exceeded 30% of the Japanese gross national product at that time. For September 2023, the Journal of Disaster Research (JDR) has planned a special issue to commemorate 100 years since the Great Kanto Earthquake. While previous special issues by the JDR have focused on specific disasters, this special issue will focus on the lessons and findings from the catastrophe and will cover even the progress of disaster research since then. We received fourteen important and thought-provoking manuscripts not only on scientific and engineering aspects but also on social and cultural aspects, including comparisons with other disasters, historical views, reconstruction issues, and future perspectives. These fourteen articles can be categorized into the three groups described below. The first four articles are the English translations of articles that originally appeared in "Koho Bosai," the bimonthly journal on natural disaster reduction that is complied and published by the Disaster Management Section, Cabinet Office, Government of Japan. The JDR believes that these four articles provide a concise English description of various aspects of the Great Kanto Earthquake disasters. Dr. Takemura summarizes the seismic features of the 1923 Kanto Earthquake. Dr. Sekizawa summarizes the large scale urban fires that it caused. Dr. Suzuki describes various aspects of the emergency responses. Dr. Murosaki details the recovery project in Tokyo. Those articles discussed various lessons learned from the 1923 Kanto Earthquake and emphasized the importance of transferring the lessons toward future disaster mitigation. The next six papers were originally works studying various aspects of the Great Kanto Earthquake disasters. Dr. Midorikawa reviews the strong ground motion of the 1923 Kanto Earthquake. Dr. Kaneko evaluates the resulting tsunami. Mr. Mammen sheds new light on the relationship between Charles A. Beard and Goto Shinpei in terms of the recovery. Dr. Albini studies the voices of foreign residents who left impressive disaster processes at that time. Dr. Murao reviews the urbanization of Tokyo after the Kanto Earthquake. Dr. Shima studies the response of the Tokyo Electric Light Company, Inc. to the 1923 Great Kanto Earthquake. The last four papers are works on various aspects of disaster risk reduction, but all of these works were inspired by the 1923 Great Kanto Earthquake in one way or another. Dr. Shaw provides a framework for repositioning earthquake risk reduction. Dr. Shimbo explores the Phase Free Concept. Dr. Shoji focuses on the possibility of Medium-Wave AM Radio Broadcasting. Dr. Yamaguchi studies the Risk Communication Method. The Editorial Board of the JDR thanks all of these contributors and hopes that these articles serve as great sources for further research in disaster risk reduction. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

As confirmed by the extreme tsunami events over the last decade (the 2004 Indian Ocean, 2010 Chile and 2011 Japan tsunami events), mitigation measures and effective evacuation planning are needed to reduce disaster risks. Modeling tsunami evacuations is an alternative means to analyze evacuation plans and possible scenarios of evacuees' behaviors. In this paper, practical applications of an agent-based tsunami evacuation model are presented to demonstrate the contributions that agent-based modeling has added to tsunami evacuation simulations and tsunami mitigation efforts. A brief review of previous agent-based evacuation models in the literature is given to highlight recent progress in agent-based methods. Finally, challenges are noted for bridging gaps between geoscience and social science within the agent-based approach for modeling tsunami evacuations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

When flooding occurs, Synthetic Aperture Radar (SAR) imagery is often used to identify flood extent and the affected buildings for two reasons: (i) for early disaster response, such as rescue operations, and (ii) for flood risk analysis. Furthermore, the application of machine learning has been valuable for the identification of damaged buildings. However, the performance of machine learning depends on the number and quality of training data, which is scarce in the aftermath of a large scale disaster. To address this issue, we propose the use of fragmentary but reliable news media photographs at the time of a disaster and use them to detect the whole extent of the flooded buildings. As an experimental test, the flood occurred in the town of Mabi, Japan, in 2018 is used. Five hand-engineered features were extracted from SAR images acquired before and after the disaster. The training data were collected based on news photos. The date release of the photographs were considered to assess the potential role of news information as a source of training data. Then, a discriminant function was calibrated using the training data and the support vector machine method. We found that news information taken within 24 h of a disaster can classify flooded and nonflooded buildings with about 80% accuracy. The results were also compared with a standard unsupervised learning method and confirmed that training data generated from news media photographs improves the accuracy obtained from unsupervised classification methods. We also provide a discussion on the potential role of news media as a source of reliable information to be used as training data and other activities associated to early disaster response. [ABSTRACT FROM AUTHOR]

Tsunamis are rare events compared with the other natural disasters, but once it happens, it can be extremely devastating to the coastal communities. Extensive inland penetration of tsunamis may cause the difficulties of understanding its impact in the aftermath of its generation. Therefore the social needs to technologies of detecting the wide impact of great tsunamis have been increased. Recent advances of remote sensing and technologies of image analysis meet the above needs and lead to more rapid and efficient understanding of tsunami affected areas. This paper provides a review of how remote sensing methods have developed to contribute to post-tsunami disaster response. The evaluations in the performances of the remote sensing methods are discussed according to the needs of tsunami disaster response with future perspective. [ABSTRACT FROM AUTHOR]

The rapid and accurate mapping of large-scale landslides and other mass movement disasters is crucial for prompt disaster response efforts and immediate recovery planning. As such, remote sensing information, especially from synthetic aperture radar (SAR) sensors, has significant advantages over cloud-covered optical imagery and conventional field survey campaigns. In this work, we introduced an integrated pixel-object image analysis framework for landslide recognition using SAR data. The robustness of our proposed methodology was demonstrated by mapping two different source-induced landslide events, namely, the debris flows following the torrential rainfall that fell over Hiroshima, Japan, in early July 2018 and the coseismic landslide that followed the 2018 Mw6.7 Hokkaido earthquake. For both events, only a pair of SAR images acquired before and after each disaster by the Advanced Land Observing Satellite-2 (ALOS-2) was used. Additional information, such as digital elevation model (DEM) and land cover information, was employed only to constrain the damage detected in the affected areas. We verified the accuracy of our method by comparing it with the available reference data. The detection results showed an acceptable correlation with the reference data in terms of the locations of damage. Numerical evaluations indicated that our methodology could detect landslides with an accuracy exceeding 80%. In addition, the kappa coefficients for the Hiroshima and Hokkaido events were 0.30 and 0.47, respectively. [ABSTRACT FROM AUTHOR]

Earth observation (EO) technologies, such as optical imaging and synthetic aperture radar (SAR), provide excellent means to continuously monitor ever-growing urban environments. Notably, in the case of large-scale disasters (e.g., tsunamis and earthquakes), in which a response is highly time-critical, images from both data modalities can complement each other to accurately convey the full damage condition in the disaster aftermath. However, due to several factors, such as weather and satellite coverage, which data modality will be the first available for rapid disaster response efforts is often uncertain. Hence, novel methodologies that can utilize all accessible EO datasets are essential for disaster management. In this study, we developed a global multimodal and multitemporal dataset for building damage mapping. We included building damage characteristics from three disaster types, namely, earthquakes, tsunamis, and typhoons, and considered three building damage categories. The global dataset contains high-resolution (HR) optical imagery and high-to-moderate-resolution SAR data acquired before and after each disaster. Using this comprehensive dataset, we analyzed five data modality scenarios for damage mapping: single-mode (optical and SAR datasets), cross-modal (pre-disaster optical and post-disaster SAR datasets), and mode fusion scenarios. We defined a damage mapping framework for semantic segmentation of damaged buildings based on a deep convolutional neural network (CNN) algorithm. We also compared our approach to another state-of-the-art model for damage mapping. The results indicated that our dataset, together with a deep learning network, enabled acceptable predictions for all the data modality scenarios. We also found that the results from cross-modal mapping were comparable to the results obtained from a fusion sensor and optical mode analysis. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of debris estimation which is one of the most important initial challenges in the wake of a disaster like the Great East Japan Earthquake and Tsunami. Reasonable estimates of the debris have to be made available to decision makers as quickly as possible. Current approaches to obtain this information are far from being optimal as they usually rely on manual interpretation of optical imagery. We have developed a novel approach for the estimation of tsunami debris pile heights and volumes for improved emergency response. The method is based on a stereo-synthetic aperture radar (stereo-SAR) approach for very high-resolution polarimetric SAR. An advanced gradient-based optical-flow estimation technique is applied for optimal image coregistration of the low-coherence non-interferometric data resulting from the illumination from opposite directions and in different polarizations. By applying model based decomposition of the coherency matrix, only the odd bounce scattering contributions are used to optimize echo time computation. The method exclusively considers the relative height differences from the top of the piles to their base to achieve a very fine resolution in height estimation. To define the base, a reference point on non-debris-covered ground surface is located adjacent to the debris pile targets by exploiting the polarimetric scattering information. The proposed technique is validated using in situ data of real tsunami debris taken on a temporary debris management site in the tsunami affected area near Sendai city, Japan. The estimated height error is smaller than 0.6 m RMSE. The good quality of derived pile heights allows for a voxel-based estimation of debris volumes with a RMSE of 1099 m 3 . Advantages of the proposed method are fast computation time, and robust height and volume estimation of debris piles without the need for pre-event data or auxiliary information like DEM, topographic maps or GCPs. [ABSTRACT FROM AUTHOR]

Change detection between images is a procedure used in many applications of remote sensing data. Among these applications, the identification of damaged infrastructures in urban areas due to a large-scale disaster is a task that is crucial for distributing relief, quantifying losses, and rescue purposes. A crucial consideration for change detection is that the images must be co-registered precisely to avoid errors resulting from misalignments. An essential consideration is that some large-magnitude earthquakes produce very complex distortions of the ground surface; therefore, a pair of images recorded before and after a particular earthquake cannot be co-registered accurately. In this study, we intend to identify changes between images that are not co-registered. The proposed procedure is based on the use of phase correlation, which shows different patterns in changed and non-changed areas. A careful study of the properties of phase correlation suggests that it is robust against misalignments between images. However, previous studies showed that, in areas with no-changes, the signal power in the phase correlation is not concentrated in a single component, but rather in several components. Thus, we study the performance of the ℓ 1 -regularized logistic regression classifier to identify the relevant components of phase correlation and learn to detect non-changed and changes areas. An empirical evaluation consisting of identifying the changes between pre-event and post-event images corresponding to the 2018 Sulawesi Indonesia earthquake-tsunami was performed for this purpose. Pairs of visible and near-infrared (VNIR) spectral bands of medium-resolution were used to compute the phase correlation to set feature space. The phase correlation-based feature space consisted of 484 features. We evaluate the proposed procedure using a damage inventory performed from visual inspection of optical images of 0.5-m resolution. A third-party provided the referred inventory. Because of the limitation of medium-resolution imagery, the different damage levels in the damage inventory were merged into a binary class: "changed" and "non-changed". The results demonstrate that the proposed procedure efficiently reproduced 85 ± 6% of the damage inventory. Furthermore, our results identified tsunami-affected areas that were not previously identified by visual inspection. • Phase correlation and sparse model to identify changes in urban areas • The procedure does not require image registration. • Designed for areas with very complex ground deformation due to earthquakes • The procedure achieved an averaged overall accuracy of 85%. [ABSTRACT FROM AUTHOR]