Search

In this study, we develop a single-phase free-surface flow model with the improved lattice kinetic scheme (LKS) and the volume of fluid (VOF) method. We implement the piecewise linear interface calculation (PLIC) algorithm to reconstruct the interface. The gas phase velocity is extrapolated from the liquid phase velocity using a velocity extension method and the signed distance function (SDF) to advect the VOF function. We calculate the SDF with the level-set reinitialisation method, and the VOF function is used as the initial profile. We discuss the advantages of the improved LKS in terms of the free-surface boundary condition by means of distribution functions, which conflict in interface cells in conventional streaming and collision processes in the lattice Boltzmann method (LBM). The proposed free-surface model is verified with well-known advection benchmark problems. We also simulate a static water problem and dam break flow. The proposed model accurately calculates the dam break surge front. Although the surge front position depends on the spatial resolution in the conventional LBM free-surface model, the improved LKS results are approximately the same regardless of the resolution. This is a critical difference between the proposed and conventional LBM models and represents a unique feature of the improved LKS. • A single-phase free-surface flow model by the improved LKS is developed. • Conflicts of the distribution function do not arise because the boundary conditions are given as the macroscopic variables. • The velocity field is extended by the SDF and the advection of the VOF function is calculated by the PLIC approach. • The proposed model is stable without the turbulence model and agrees well with the experimental results. [ABSTRACT FROM AUTHOR]

The devastating consequences of tsunamis on coastal infrastructure have highlighted the urgent need for effective disaster risk reduction strategies. To mitigate tsunami disasters, the insurance industry plays a vital role in implementing risk transfer measures by providing financial protection against asset damage. However, the current research on catastrophe insurance policies for coastal infrastructure lacks consideration of climate change effects. It is essential to take into account the non-stationary effects of sea-level rise to develop long-term tsunami disaster mitigation measures and promote socioeconomic resilience in coastal communities. This paper aims to provide an insurance portfolio optimization framework for coastal residential buildings subjected to tsunamis considering non-stationary sea-level rise effects based on a stochastic simulation approach. A spatiotemporal probabilistic sea-level rise hazard assessment is carried out by utilizing available climate models and considering several emission scenarios. Tsunami propagation analyses under various sea-level rise cases are performed to evaluate the time-variant tsunami hazard curves based on Monte Carlo simulation. A life cycle-based stochastic insurance claim model associated with the cumulative loss of building assets is developed based on a non-stationary compound renewal process. Finally, a sample average approximation method is leveraged to estimate the optimum basic insurance premium rate by maximizing the insurer's profit under a cost-constrained insurance purchase decision of homeowners. As a case study, the proposed framework is applied to multiple municipalities situated in the tsunami-prone region of Kochi Prefecture, Japan. Sea-level rise substantially decreases the maximum profits of tsunami insurers and increases the premium rate and ruin probability. [ABSTRACT FROM AUTHOR]

The numerical solution to the conservative Allen–Cahn (AC) equation obtained by the lattice Boltzmann method (LBM) is problematic due to the creation of an unphysical phase in the bulk region. In this study, we propose an approach to suppress this unexpected behaviour by filtering the phase-field function. In the AC equation simulations, the phase-field function typically oscillates in the bulk region and overshoots or undershoots near the interface. The aim of the filtering approach is to create a smooth interface profile through several filtering iterations and improve the calculation accuracy of the interfacial normal. We demonstrate that the filtering process effectively suppresses the creation of an unphysical phase through Zalesak's disc rotation and single vortex tests. The filtering process improves the accuracy of the calculated interface tension under the same thickness for a stationary droplet benchmark test. A comparative study with the Cahn-Hilliard (CH) equation is conducted for a single rising bubble flow. The characteristics of both equations are discussed. Furthermore, the filtered conservative AC equation is found to preserve the shape of the rising bubble well. [Display omitted] • A filtering approach is proposed to the LBM for the conservative Allen–Cahn equation. • The filtering effectively prevents the creation of the unphysical phase in the bulk region. • The effect of thickness on the diffusion of the phase-field function is studied. • The filtering improves the calculation accuracy of the interface tension. • A comparison with the Cahn-Hilliard equation is conducted for the rising bubble flow. [ABSTRACT FROM AUTHOR]

When a large-scale flood disaster occurs, it is important to identify the flood areas in a short time in order to effectively support the affected areas afterwards. Synthetic Aperture Radar (SAR) is promising for flood detection. A number of change detection methods have been proposed to detect flooded areas with pre- and post-event SAR data. However, it remains difficult to detect flooded areas in built-up areas due to the complicated scattering of microwaves. To solve this issue, in this paper we propose the idea of analyzing the local changes in pre- and post-event SAR data as well as the larger-scale changes, which may improve accuracy for detecting floods in built-up areas. Therefore, we aimed at evaluating the effectiveness of multi-scale SAR analysis for flood detection in built-up areas using ALOS-2/PALSAR-2 data. First, several features were determined by calculating standard deviation images, difference images, and correlation coefficient images with several sizes of kernels. Then, segmentation on both small and large scales was applied to the correlation coefficient image and calculated explanatory variables with the features at each segment. Finally, machine learning models were tested for their flood detection performance in built-up areas by comparing a small-scale approach and multi-scale approach. Ten-fold cross-validation was used to validate the model, showing that highest accuracy was offered by the AdaBoost model, which improved the F1 Score from 0.89 in the small-scale analysis to 0.98 in the multi-scale analysis. The main contribution of this manuscript is that, from our results, it can be inferred that multi-scale analysis shows better performance in the quantitative detection of floods in built-up areas. [ABSTRACT FROM AUTHOR]

In this study, we present an optimization method for determining a cost‐effective sparse configuration for tsunami gauges to realize the reconstruction of high‐resolution wave height distribution throughout the target region based on the concept of super‐resolution. This optimization method consists of three procedures. First, we generate time series data of tsunami wave heights at synthetic gauges by conducting numerical simulations of various earthquake and tsunami scenarios at the target site. Next, we apply proper orthogonal decomposition to the synthetic tsunami data to extract the spatial features of the wave height distribution. Finally, according to these spatial features, an optimization process is performed to determine a sparse configuration of synthetic gauges. In the optimization, the optimal gauges are sequentially selected from the set of synthetic gauges to reconstruct the wave height distribution with the highest accuracy. Targeting hypothetical Nankai Trough earthquakes and tsunamis, we determine the optimal configuration near Shikoku and demonstrate the wave height reconstruction capability of the approach by comparing the performance of networks with optimally and randomly placed gauges. The results indicate that coastal gauges contribute more to improving the reconstruction accuracy and that a configuration with 21 optimal gauges has satisfactory performance. In addition, we assess the performance of the existing NOWPHAS network installed in the Shikoku region and find that the reconstruction performance of the existing network is equivalent to that of the optimal gauge network. Plain Language Summary: This study introduces a method of optimizing the sparse locations where actual tsunami gauges should be installed to obtain information on tsunami wave heights at any given point. By optimizing the locations of the observation points, it is possible to extend observations recorded at only a small number of points to obtain a good approximation to data at other points where the tsunami was not directly observed. First, numerical simulations are performed based on assumed earthquake and tsunami scenarios to generate synthetic time series data of tsunami waves. Then, by applying proper orthogonal decomposition to the obtained synthetic data, the characteristics of the tsunami wave height distribution are extracted. Finally, these characteristics are used to perform optimization by sequentially selecting the best gauges from among a set of candidate points to reconstruct wave height information for the entire target area, thereby determining the placements of a limited number of gauges. In a numerical demonstration example simulating a Nankai Trough earthquake and tsunami, the placement of gauges off the coast of Shikoku is optimized, and it is shown that the wave heights at arbitrary points over the entire area can be reproduced using data from at least 21 optimally placed gauges. Key Points: An optimization method implementing a greedy algorithm is presented to design a cost‐effective observational network of tsunami gaugesHigh‐resolution information about tsunami wave height is constructed from sparse observations based on the concept of super‐resolutionThe optimization process considering the existing observation network locations identifies their sufficiency or deficiency [ABSTRACT FROM AUTHOR]

Tsunami fragility functions (TFF) are statistical models that relate a tsunami intensity measure to a given building damage state, expressed as cumulative probability. Advances in computational and data retrieval speeds, coupled with novel deep learning applications to disaster science, have shifted research focus away from statistical estimators. TFFs offer a "disaster signature" with comparative value, though these models are seldom applied to generate damage estimates. With applicability in mind, we challenge this notion and investigate a portion of TFF literature, selecting three TFFs and two application methodologies to generate a building damage estimation baseline. Further, we propose a simple machine learning method, trained on physical parameters inspired by, but expanded beyond, TFF intensity measures. We test these three methods on the 2011 Ishinomaki dataset after the Great East Japan Earthquake and Tsunami in both binary and multi-class cases. We explore: (1) the quality of building damage estimation using TFF application methods; (2) whether TFF can generalize to out-of-domain building damage datasets; (3) a novel machine learning approach to perform the same task. Our findings suggest that: both TFF methods and our model have the potential to achieve good binary results; TFF methods struggle with multiple classes and out-of-domain tasks, while our proposed method appears to generalize better. [ABSTRACT FROM AUTHOR]

The lattice Boltzmann method (LBM) has recently attracted attention as a simple computational fluid dynamics (CFD) method that does not directly solve macroscopic equations. Due to the advantage of its local scheme, the LBM is expected to efficiently achieve highly accurate and high-resolution fluid simulations. In particular, the application of the LBM to disaster simulations has been studied since the proposal of the LBM for nonlinear shallow water equations (LABSWE). However, it is well known that the LBM is prone to computational instability in high Reynolds number flows. A tsunami inundating an urban area is considered a high Reynolds number flow because of its violent flow. The numerical stability in such flows must be enhanced for the application of the LBM to real-world tsunami simulations. For this purpose, it is practical to introduce the subgrid-scale (SGS) model into the multiple-relaxation-time (MRT) model. Notably, the MRT model is computationally more expensive than the conventional lattice Bhatnagar–Gross–Krook (BGK) model. In this study, we modified the SGS model for the MRT model. We verified the calculation accuracy by focusing on grid size differences in the classical benchmark problems in high Reynolds number flows. Finally, we simulated the 2011 tsunami off the Pacific coast caused by the Tohoku earthquake. The LBM simulations reproduced the tsunami propagation waveform and inundation depth with the computational accuracy equally as good as that of the finite difference method (FDM). • The tsunami by the 2011 Great East Japan Earthquake is simulated. • The proposed model is stable in the high Reynolds number flows. • A comparative study with the finite difference method is demonstrated. [ABSTRACT FROM AUTHOR]

The tsunami caused by the Tonga submarine volcanic eruption that occurred at 13:15 Japan Time (JST) on January 15, 2022, exposed a blind spot in Japan's tsunami monitoring and warning system, which was established in 1952 for local tsunamis and expanded to distant tsunamis after the 1960 Chile tsunami. This paper summarizes how the warning system responded to the unprecedented tsunami, the actual evacuation process, and the damage it caused in Japan. Initially, the tsunami from the volcanic eruption was expected to arrive at approximately midnight with amplitudes of less than 20 cm. However, a series of short waves arrived at approximately 21:00, a few hours earlier than expected. The early arrival of these sea waves coincided with a rapid increase in atmospheric pressure; then, the short-period component was predominant, and the wave height was amplified while forming wave groups. After a 1.2 m tsunami was observed in Amami City in southern Japan at 23:55 JST, the Japan Meteorological Agency issued a tsunami warning/advisory. The tsunami continued, and all advisories were cleared at 14:00 JST on January 16. Information about this tsunami and the response to it are summarized here, including the characteristics and issues of the actual tsunami evacuation situation in each region. There were no casualties, but the issues that emerged included difficulty evacuating on a winter night and traffic congestion due to evacuation by car and under the conditions of the COVID-19 pandemic. In the coastal area, damage to fishing boats and aquaculture facilities was reported due to the flow of the tsunami. In addition, damage to aquaculture facilities, including those producing oysters, scallops, seaweed and other marine products, decreased the supply of marine products, and the economic impact is likely to increase in the future. [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]

Emerging as powerful open-source software in recent years, "DualSPHysics" is receiving increased attention for its ability to simulate large-scale flow fields. In the context of applying open-source software, the differences in the numerical results due to different model parameters need to be investigated in detail. In this study, some benchmark problems have been solved with DualSPHysics to validate the estimation of wave impact pressure with violent breaking waves. We have demonstrated three main results: (i) as an alternative to the artificial viscosity traditionally used in DualSPHysics, a laminar viscosity model can also well reproduce the solutions to the existing benchmark problems in a violent flow field with the modified dynamic boundary condition; (ii) the dynamics of the gas phase is essential in the calculation of wave breaking with rapid gate opening; and (iii) if the density diffusion parameter is too large, the impact pressure may be underestimated. The practical contribution by this study is to find that DualSPHysics well reproduces complex breaking waves, including multi-phase gas-liquid flows, and that the wave impact pressure is accurate by comparison with existing experimental results. This allows us to understand the complex behavior of fluid-structure interactions in coastal engineering by means of DualSPHysics. [ABSTRACT FROM AUTHOR]

Previous applications of machine learning in remote sensing for the identification of damaged buildings in the aftermath of a large-scale disaster have been successful. However, standard methods do not consider the complexity and costs of compiling a training data set after a large-scale disaster. In this article, we study disaster events in which the intensity can be modeled via numerical simulation and/or instrumentation. For such cases, two fully automatic procedures for the detection of severely damaged buildings are introduced. The fundamental assumption is that samples that are located in areas with low disaster intensity mainly represent nondamaged buildings. Furthermore, areas with moderate to strong disaster intensities likely contain damaged and nondamaged buildings. Under this assumption, a procedure that is based on the automatic selection of training samples for learning and calibrating the standard support vector machine classifier is utilized. The second procedure is based on the use of two regularization parameters to define the support vectors. These frameworks avoid the collection of labeled building samples via field surveys and/or visual inspection of optical images, which requires a significant amount of time. The performance of the proposed method is evaluated via application to three real cases: the 2011 Tohoku-Oki earthquake–tsunami, the 2016 Kumamoto earthquake, and the 2018 Okayama floods. The resulted accuracy ranges between 0.85 and 0.89, and thus, it shows that the result can be used for the rapid allocation of affected buildings. [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]

• A framework for time-variant tsunami hazard assessment considering nonstationary sea-level rise effects is established. • Non-Poisson renewal process models are developed to combine randomly recurring tsunamis with nonstationary sea-level rise. • The sea-level rise uncertainty is integrated during the tsunami hazard assessment according to total probability theorem. • Tsunami hazard increases considerably when considering nonstationary sea-level rise under non-Poisson earthquake events. Tsunami-induced disasters present a significant threat to coastal communities. Under the effects of sea-level rise due to climate change, tsunami occurrence frequency and intensities would increase in a nonstationary manner due to the long-term progressive trend in the variability in sea level. Therefore, the reliability of coastal infrastructure systems affected by tsunami hazard will diminish over time. A procedure for hazard assessment that integrates tsunamis occurring at random times with evolving sea-level rise should be established. This paper presents a novel framework for the time-variant assessment of tsunami hazard considering the effects of nonstationary sea-level rise due to climate change based on a non-Poisson stochastic renewal process. A time-variant probabilistic sea-level rise is modeled by utilizing various climate models and existing data reported in several studies. The conditional tsunami hazard curves considering sea-level rise are estimated by performing tsunami propagation analyses under different sea-level rise cases and by Monte Carlo simulation, considering the uncertainties associated with earthquake fault movements. Finally, the new concept of time-variant tsunami hazard assessment considering the nonstationary effects of sea-level rise is developed according to a non-Poisson stochastic renewal process. An illustrative example is provided by applying the proposed framework to several municipalities in the Kochi Prefecture of Japan that are subjected to the anticipated Nankai-Tonankai earthquake. The effects of sea-level rise on time-variant tsunami hazard under Poisson and non-Poisson processes of earthquake occurrence are discussed. [ABSTRACT FROM AUTHOR]

• A framework for incorporating uncertainties associated with sea-level rise in probabilistic tsunami hazard assessments (PTHAs) is established. • A practical approach for probabilistic sea-level rise hazard assessment is provided by utilizing the available climate models and data. • Tsunami hazard curves considering the effects of sea-level rise are estimated based on the total probability theorem. • Neglecting the effects of sea-level rise in a low-lying coastal plain can significantly underestimate tsunami hazards. Sea-level rise due to climate change could significantly exacerbate tsunami disasters since the sea level is a critical parameter affecting the intensity of tsunamis. Considering the impacts of future climate change on the ocean, a method to consider the effects of sea-level rise on the tsunami hazard intensity is needed to precisely predict future tsunami disasters. This paper presents a novel framework for probabilistic tsunami hazard assessment, considering the sea-level rise and associated uncertainties. A probabilistic assessment of the sea-level rise hazard is performed using the available data and climate models considering several climate change emission scenarios. Conditional tsunami hazard curves are estimated by conducting tsunami propagation simulations given a sea-level rise while considering the uncertainties associated with fault movement (i.e., rake angles and average stress drops). Radial-basis-function-based surrogate models and quasi-Monte-Carlo simulations are employed to obtain tsunami hazard curves. Finally, the tsunami hazard curves considering the effects of sea-level rise are estimated by convolving the corresponding regional sea-level rise hazard with the conditional tsunami hazard curves based on the total probability theorem. An illustrative example is provided, in which the proposed framework is applied to several municipalities in the Mie Prefecture of Japan that would be affected by tsunamis during the anticipated Nankai-Tonankai earthquake. The effects of sea-level rise on the tsunami hazard intensities associated with the municipalities are discussed. [ABSTRACT FROM AUTHOR]