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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 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]

Although the 2004 Indian Ocean tsunami occurred several years ago and all the building repair and infrastructure reconstruction needed in Thailand to repair the damage caused by the tsunami are complete, there is still a need for damage assessment methods that can be used in future tsunami damage assessments in Thailand and that can possibly be applied to other countries. This study summarizes three methods for assessing tsunami damage, "tsunami damage criteria," "tsunami damage ratios" and "tsunami fragility curves," based on damage data from the 2004 Indian Ocean tsunami in Thailand, and these methods are compared using other tsunami events. Using the data from a field survey of damaged buildings, tsunami damage criteria were summarized for each degree of damage as a function of inundation depth for different building types. Tsunami damage ratios were summarized using building damage data obtained from surveys in the field and reconstruction cost estimates provided by the Thai government. The fragility curves developed were validated based on building performance data obtained from full-scale experiments on buildings and columns. Despite differences in the tsunami characteristics (inundation depth, current velocity and hydrodynamic force), the damage probabilities were nearly the same. The summarized methods might be useful for future tsunami damage assessments and loss estimation in Thailand and serve as guidelines for tsunami damage assessment in other countries. [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]

SUMMARY Characteristics of the tsunami decay processes are examined following the definition and discussion of the major terms involved: 'moving root mean squared amplitude', 'tsunami coda' and 'non-dimensional tsunami amplitude' (NDA). Tsunami waveform data observed at tidal stations in Japan during two Kuril Island earthquake tsunamis in 2006 and 2007 are analysed. The tsunami codas of these events showed the following characteristics. (1) The decay process determined by the tsunami coda of the 2006 tsunami implies that the decay time constant at a tidal station depends on which ocean the station is facing. (2) Fluctuations of the NDAs in the tsunami codas are stochastically approximated by either the Rayleigh distribution or the normal distribution and the distributions of the NDAs at most tidal stations are similar. (3) Differences in the decay processes between these two tsunami events were small with regard to the averaged decay time constants and the distributions of the NDAs. These findings may be the first step towards improved forecasting of the tsunami decay process. [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]

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]

On 28 September 2018, a large earthquake of magnitude 7.5 occurred at 72 km away from the city of Palu, Central Sulawesi, Indonesia. Following the main shock, a series of tsunami attacked the eastern coasts of central Sulawesi and the severe damages were reported along the coast of Palu Bay.In the aftermath of the event, since the rupture was occurred along Palu-Koro strike-slip fault, the tsunami impact was expected to be relatively low compared with the other past tsunami events caused by the thrust fault rupture. However, the reported damage, more than 70,000 houses were reported to be damaged, was much more severe than expected because of the combination of strong ground motion, landslides, mudflows and tsunamis.After the event occurred, as a part of the Indonesian government's reconnaissance, we conducted a post-tsunami field survey to identify the tsunami impact. The survey was conducted on 21 and 22 October, 2018, and aimed to measure the extent of tsunami inland penetration with RTK-GPS, flow depths and to inspect the structural damage. The survey also aimed to collect the ground truth information for satellite remote sensing and survivor video analysis to understand how the tsunami attacked and devastated the coastal areas of central Palu.From the survey at the central Palu, we found that the major tsunami impact was concentrated within about 200 m inland from the shoreline. The spatial distribution of tsunami flow depths was scattered in the narrow range of tsunami inundation zone of the central Palu coast; 1-3m flow depths in average, 6 m the maximum at the central Palu.Some survivor videos provided important information on tsunami attack, especially on the time series of the first and second tsunami attacks. From the video analysis, we found that the second tsunami hit Palu Grand Mall with the splash height of almost 8 m above the sea level, and the difference of 1st. and 2nd. tsunami attack was likely to be 2 minutes. This implies that these tsunamis were generated by a phenomenon with short time scale or different sources. [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]

ABSTRACT Udo, K.; Takeda, Y., and Koshimura, S., 2018. Detection of coastal damage using differences in automatically generated digital surface models (DSMs): Application to the 2011 Off the Pacific Coast of Tohoku Earthquake Tsunami. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 696–700. Coconut Creek (Florida), ISSN 0749-0208. The detection of coastal damage due to the 2011 Off the Pacific Coast of Tohoku Earthquake Tsunami on the southern Sendai Coast was attempted via state-of-the-art remote sensing technology using ALOS PRISM satellite images with a spatial resolution of 2.5 m. Digital surface models (DSMs) were generated semi-automatically, and their accuracies were confirmed via comparisons with digital elevation models generated from aerial laser measurements. The differences between the DSMs prior to and after the tsunami were analyzed statistically for each type of land cover: residential districts, rice fields, coastal forests, and beaches. It was demonstrated that the damage to the coastal forests and residential districts could be detected though the damage in the low land area was difficult to be detected because of its small undulations in topography. Low pass filtering was employed to remove the jitter noise; however, the major characteristics such as damage in the coastal forests and residential areas were not changed by the filtering; that is, the major statistical characteristics could be detected without filtering. [ABSTRACT FROM AUTHOR]

Udo, K.; Takeda, Y., and Koshimura, S., 2018. Detection of coastal damage using differences in automatically generated digital surface models (DSMs): Application to the 2011 Off the Pacific Coast of Tohoku Earthquake Tsunami. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 696–700. Coconut Creek (Florida), ISSN 0749-0208. The detection of coastal damage due to the 2011 Off the Pacific Coast of Tohoku Earthquake Tsunami on the southern Sendai Coast was attempted via state-of-the-art remote sensing technology using ALOS PRISM satellite images with a spatial resolution of 2.5 m. Digital surface models (DSMs) were generated semi-automatically, and their accuracies were confirmed via comparisons with digital elevation models generated from aerial laser measurements. The differences between the DSMs prior to and after the tsunami were analyzed statistically for each type of land cover: residential districts, rice fields, coastal forests, and beaches. It was demonstrated that the damage to the coastal forests and residential districts could be detected though the damage in the low land area was difficult to be detected because of its small undulations in topography. Low pass filtering was employed to remove the jitter noise; however, the major characteristics such as damage in the coastal forests and residential areas were not changed by the filtering; that is, the major statistical characteristics could be detected without filtering. [ABSTRACT FROM AUTHOR]

A powerful tsunami triggered by the Mw 9.0 Tohoku earthquake struck the northern Pacific coast of Japan in 2011, destroying several coastal communities in Iwate Prefecture, Miyagi Prefecture, and Fukushima Prefecture. Here, we investigate a new spatio-temporal slip model (model B) developed by the Cabinet Office of the Government of Japan. This slip model was compared against the non-uniform slip model estimated using tsunami waveform data (model A). We focused our analysis on two areas that were destroyed by the tsunami. The town of Onagawa and the coastal Sendai Plain area were selected because they were located in front of the epicentre, where the most significant slips were registered. Our simulation results revealed that the spatio-temporal slip distribution better replicated the observed data. Regarding the tsunami waveforms from the coastal tide gauge station and offshore stations, the Cabinet Office's slip model showed an approximately 30% better accuracy relative to the non-uniform slip model. Furthermore, by comparing the local inundation features at two locations with unique topographic and coastal morphological characteristics, we also found that model B better replicated the measured inundation depths. Finally, considering that tsunami-induced damage is a direct function of various inundation features such as the flow depth and flow velocity, this new slip model can generate more realistic damage scenarios for future tsunami assessments. [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]

Assessing the hazard and damage of a potential tsunami is an ongoing challenge in tsunami research. This study begins by simulating tsunami hazards using historical events. A tsunami propagation model is used to obtain the estimated maximum tsunami height along the west coast of Thailand for a rough return period, and rupture locations that have the potential to generate catastrophic tsunamis in Thailand for a specific return period are proposed. A tsunami inundation model is then performed to quantify each building's maximum inundation depth, using high-resolution satellite images to extract each building's location for the areas of interest located in southern Thailand. Nam Khem village and Patong beach are selected as study areas to represent village communities and tourist attractions, respectively. The model results are then used to obtain the numbers of exposed inhabitants and buildings for each earthquake return period. The developed tsunami fragility curves are applied to these figures to determine the number of potentially damaged buildings. The analysis suggests that the propagation model can be used to obtain rough estimations because it provided results similar to those of the inundation model. However, material type must be considered when fragility curves are used in a different country (i.e. reinforced concrete buildings in Thailand from the 2004 tsunami and wooden houses in Japan from the 2011 East Japan tsunami). [ABSTRACT FROM AUTHOR]

In the Indian Ocean and the South China Sea, many hundreds of thousands of lives have been lost due to tsunami events, and almost half of the lives lost occurred following the 2004 Indian Ocean event. Potential tsunami case scenarios have been simulated in these regions by a number of researchers to calculate the hazard level. The hazard level is based on a variety of conditions, such as the tsunami height, the inundation area, and the arrival time. However, the current assessments of the hazard levels do not focus on the tsunami risk to a coastal population. This study proposes a new method to quantify the risk to the coastal population in the region that includes the Indian Ocean and the South China Sea. The method is simple and combines the use of readily available tsunami data, far-field tsunami simulation models to determine the regional risk and global population data. An earthquake-generated tsunami was simulated, following an earthquake that had a magnitude larger than 8.5 Mw and occurred along a potential subduction zone. The 2004 Indian Ocean event seemed to be a "worst case scenario"; however, it has been estimated that a potential tsunami, occurring in a coastal region with a high population density, could cause significantly greater casualties. [ABSTRACT FROM AUTHOR]

The study of tsunami hazards in Thailand has been an ongoing topic of research. However, the hazards from tsunami sources based on probabilistic study and population risk are still unclear. In this study, potential tsunami sources along rupture zones were selected. A series of far-field tsunami simulations were performed with scaled fault parameters based on fault lengths from 100-600 km. The results show that within a few centuries, the maximum tsunami height could be 2-5 m at the west coast and less than 2 m at the east coast. The potential tsunami exposure (PTE) of populations in an estimated inundation zone was calculated using global population data in relation to tsunami height. The results show that much attention should be paid to fault ruptures longer than 300 km (≈ 8.5 moment magnitude, Mw) that originate from 4°-6° N and 14°-17° N for the Sumatra subduction zone and the Manila trench, respectively. A quarter of a million people are at risk of exposure to a maximum 9 m tsunami height after 100 min of the arrival of the first wave at the Andaman coast. One million people near the Gulf of Thailand are at risk of a tsunami height less than 3 m after 9 hr. [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]

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]

Applications of machine learning on remote sensing data appear to be endless. Its use in damage identification for early response in the aftermath of a large-scale disaster has a specific issue. The collection of training data right after a disaster is costly, time-consuming, and many times impossible. This study analyzes a possible solution to the referred issue: the collection of training data from past disaster events to calibrate a discriminant function. Then the identification of affected areas in a current disaster can be performed in near real-time. The performance of a supervised machine learning classifier to learn from training data collected from the 2018 heavy rainfall at Okayama Prefecture, Japan, and to identify floods due to the typhoon Hagibis on 12 October 2019 at eastern Japan is reported in this paper. The results show a moderate agreement with flood maps provided by local governments and public institutions, and support the assumption that previous disaster information can be used to identify a current disaster in near-real time. [ABSTRACT FROM AUTHOR]

The tsunami disasters that occurred in Indonesia, Chile, and Japan have inflicted serious casualties and damaged social infrastructures. Tsunami forecasting systems are thus urgently required worldwide. We have developed a real-time tsunami inundation forecast system that can complete a tsunami inundation and damage forecast for coastal cities at the level of 10-m grid size in less than 20 min. As the tsunami inundation and damage simulation is a vectorizable memory-intensive program, we incorporate NEC’s vector supercomputer SX-ACE. In this paper, we present an overview of our system. In addition, we describe an implementation of the program on SX-ACE and evaluate its performance of SX-ACE in comparison with the cases using an Intel Xeon-based system and the K computer. Then, we clarify that the fulfillment of a real-time tsunami inundation forecast system requires a system with high-performance cores connected to the memory subsystem at a high memory bandwidth such as SX-ACE. [ABSTRACT FROM AUTHOR]

Although supervised machine learning classification techniques have been successfully applied to detect collapsed buildings, there is still a major problem that few publications have addressed. The success of supervised machine learning strongly depends on the availability of training samples. Unfortunately, in the aftermath of a large-scale disaster, training samples become available only after several weeks or even months. However, following a disaster, information on the damage situation is one of the most important necessities for rapid search-and-rescue efforts and relief distribution. In this paper, a modification of the supervised machine learning classification technique called logistic regression is presented. Here, the training samples are replaced with probabilistic information, which is calculated from the spatial distribution of the hazard under consideration and one or more fragility functions. Such damage probabilities can be collected almost in real time for specific disasters such as earthquakes and/or tsunamis. We present the application of the proposed method to the 2011 Great East Japan Earthquake and Tsunami for collapsed building detection. The results show good agreement with a field survey performed by the Ministry of Land, Infrastructure, Transport and Tourism, with an overall accuracy of over 80%. Thus, the proposed method can significantly contribute to a rapid estimation of the number and locations of collapsed buildings. [ABSTRACT FROM AUTHOR]

This paper presents post-tsunami survey results describing the impacts of the 2011 East Japan tsunami in Yos Sudarso Bay, Papua, Indonesia. Although the far-field tsunami had a height of 0.8 m measured at a tide gauge inside the small U-shaped bay, it severely damaged four villages scattered along the bay. Detailed numerical analysis was carried out to explain the damages. We used a well-verified source model in the near- and far-field cases to model the propagation of the tsunami southward in the Pacific until reaching Papua Island, Indonesia. The numerical analyses demonstrate two causes of damage in the bay: the maximum tsunami velocity (3.5 m s–1) floated the houses in the villages of Tobati and Enggros and caused them to collapse, and water trapped by the hook-shaped peninsula amplified the tsunami height at the Holtekamp and Hanurata. [ABSTRACT FROM AUTHOR]

Identifying permanent water and temporary water in flood disasters efficiently has mainly relied on change detection method from multi-temporal remote sensing imageries, but estimating the water type in flood disaster events from only post-flood remote sensing imageries still remains challenging. Research progress in recent years has demonstrated the excellent potential of multi-source data fusion and deep learning algorithms in improving flood detection, while this field has only been studied initially due to the lack of large-scale labelled remote sensing images of flood events. Here, we present new deep learning algorithms and a multi-source data fusion driven flood inundation mapping approach by leveraging a large-scale publicly available Sen1Flood11 dataset consisting of roughly 4831 labelled Sentinel-1 SAR and Sentinel-2 optical imagery gathered from flood events worldwide in recent years. Specifically, we proposed an automatic segmentation method for surface water, permanent water, and temporary water identification, and all tasks share the same convolutional neural network architecture. We utilize focal loss to deal with the class (water/non-water) imbalance problem. Thorough ablation experiments and analysis confirmed the effectiveness of various proposed designs. In comparison experiments, the method proposed in this paper is superior to other classical models. Our model achieves a mean Intersection over Union (mIoU) of 52.99 % , Intersection over Union (IoU) of 52.30 % , and Overall Accuracy (OA) of 92.81 % on the Sen1Flood11 test set. On the Sen1Flood11 Bolivia test set, our model also achieves very high mIoU ( 47.88 % ), IoU ( 76.74 % ), and OA ( 95.59 % ) and shows good generalization ability. [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]

Most mainstream research on assessing building damage using satellite imagery is based on scattered datasets and lacks unified standards and methods to quantify and compare the performance of different models. To mitigate these problems, the present study develops a novel end-to-end benchmark model, termed the pyramid pooling module semi-Siamese network (PPM-SSNet), based on a large-scale xBD satellite imagery dataset. The high precision of the proposed model is achieved by adding residual blocks with dilated convolution and squeeze-and-excitation blocks into the network. Simultaneously, the highly automated process of satellite imagery input and damage classification result output is reached by employing concurrent learned attention mechanisms through a semi-Siamese network for end-to-end input and output purposes. Our proposed method achieves F1 scores of 0.90, 0.41, 0.65, and 0.70 for the undamaged, minor-damaged, major-damaged, and destroyed building classes, respectively. From the perspective of end-to-end methods, the ablation experiments and comparative analysis confirm the effectiveness and originality of the proposed method. Finally, the consistent prediction results of our model for data from the 2011 Great East Japan Earthquake verify the high performance of our model in terms of the domain shift problem, which implies that it is effective for evaluating future disasters. [ABSTRACT FROM AUTHOR]

Earth Observation satellite imaging helps building diagnosis during a disaster. Several models are put forward on the xBD dataset, which can be divided into two levels: the building level and the pixel level. Models from two levels evolve into several versions that will be reviewed in this paper. There are four key challenges hindering researchers from moving forward on this task, and this paper tries to give technical solutions. First, metrics on different levels could not be compared directly. We put forward a fairer metric and give a method to convert between metrics of two levels. Secondly, drone images may be another important source, but drone data may have only a post-disaster image. This paper shows and compares methods of directly detecting and generating. Thirdly, the class imbalance is a typical feature of the xBD dataset and leads to a bad F1 score for minor damage and major damage. This paper provides four specific data resampling strategies, which are Main-Label Over-Sampling (MLOS), Discrimination After Cropping (DAC), Dilation of Area with Minority (DAM) and Synthetic Minority Over-Sampling Technique (SMOTE), as well as cost-sensitive re-weighting schemes. Fourthly, faster prediction meets the need for a real-time situation. This paper recommends three specific methods, feature-map subtraction, parameter sharing, and knowledge distillation. Finally, we developed our AI-driven Damage Diagnose Platform (ADDP). This paper introduces the structure of ADDP and technical details. Customized settings, interface preview, and upload and download satellite images are major services our platform provides. [ABSTRACT FROM AUTHOR]

Wetlands provide society with a myriad of ecosystem services, such as water storage, food sources, and flood control. The ecosystem services provided by a wetland are largely dependent on its hydrological dynamics. Constant monitoring of the spatial extent of water surfaces and the duration of flooding of a wetland is necessary to understand the impact of drought on the ecosystem services a wetland provides. Synthetic aperture radar (SAR) has the potential to reveal wetland dynamics. Multitemporal SAR image analysis for wetland monitoring has been extensively studied based on the advances of modern SAR missions. Unfortunately, most previous studies utilized monopath SAR images, which result in limited success. Tracking changes in individual wetlands remains a challenging task because several environmental factors, such as wind-roughened water, degrade image quality. In general, the data acquisition frequency is an important factor in time series analysis. We propose a Gaussian process-based temporal interpolation (GPTI) method that enables the synergistic use of SAR images taken from multiple paths. The proposed model is applied to a series of Sentinel-1 images capturing wetlands in Okanogan County, Washington State. Our experimental analysis demonstrates that the multiple path analysis based on the proposed method can extract seasonal changes more accurately than a single path analysis. [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]

A rapid mapping of landslides following a disaster is important for coordinating emergency response and limiting rescue delays. A synthetic aperture radar (SAR) can provide a solution even in harsh weather and at night, due to its independence of weather and light, quick response, no contact and broad coverage. This study aimed to conduct a comprehensive exploration on the intensity and coherence information of three Advanced Land Observing Satellite-2 (ALOS-2) SAR images, for rapid massive landslide mapping in a pixel level, in order to provide a reference for future applications. Applied data were two pre-event and one post-event high-resolution ALOS-2 products. Studied area was in the east of Iburi, Hokkaido, Japan, where massive shallow landslides were triggered in the 2018 Hokkaido Eastern Iburi Earthquake. Potential parameters, including intensity difference (d), co-event correlation coefficient (r), correlation coefficient difference ( ∆ r ), co-event coherence (γ), and coherence difference ( ∆ γ ), were first selected and calculated based on a radar reflection mechanism, to facilitate rapid detection. Qualitative observation was then performed by overlapping ground truth landslides to calculated parameter images. Based on qualitative observation, an absolute value of d ( d a b s 1 ) was applied to facility analyses, and a new parameter ( d a b s 2 ) was proposed to avoid information loss in the calculation. After that, quantitative analyses of the six parameters ( d a b s 1 , d a b s 2 , r, ∆ r , γ and ∆ γ ) were performed by receiver operating characteristic. d a b s 2 and ∆ r were found to be favorable parameters, which had the highest AUC values of 0.82 and 0.75, and correctly classified 69.36% and 64.57% landslide and non-landslide pixels by appropriate thresholds. Finally, a discriminant function was developed, combining three relatively favorable parameters ( d a b s 2 , ∆ r , and ∆ γ ) with one in each type, and achieved an overall accuracy of 74.31% for landslide mapping. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) images have been used to map flooded areas with great success. Flooded areas are often identified by detecting changes between a pair of images recorded before and after a certain flood. During the 2018 Western Japan Floods, the change detection method generated significant misclassifications for agricultural targets. To evaluate whether such a situation could be repeated in future events, this paper examines and identifies the causes of the misclassifications. We concluded that the errors occurred because of the following. (i) The use of only a single pair of SAR images from before and after the floods. (ii) The unawareness of the dynamics of the backscattering intensity through time in agricultural areas. (iii) The effect of the wavelength on agricultural targets. Furthermore, it is highly probable that such conditions might occur in future events. Our conclusions are supported by a field survey of 35 paddy fields located within the misclassified area and the analysis of Sentinel-1 time series data. In addition, in this paper, we propose a new parameter, which we named "conditional coherence", that can be of help to overcome the referred issue. The new parameter is based on the physical mechanism of the backscattering on flooded and non-flooded agricultural targets. The performance of the conditional coherence as an input of discriminant functions to identify flooded and non-flooded agricultural targets is reported as well. [ABSTRACT FROM AUTHOR]

• Tsunami Analytical fragility curves for pre-code buildings were developed. • An integrated tool has been developed to construct probabilistic fragility curves. • Structural damage of buildings was accounted for the structural capacity curve. From the last large seismic movements generated in the Pacific Ocean, it has been demonstrated that tsunamis have a high potential of destruction of buildings on the coasts. In that sense, although along the Pacific Colombian coast several earthquakes that have generated tsunami were reported during the last century, there are no local data to quantify the physical damage that future events could produce on nearby cities. For the purpose of establishing tools to increase the risk management capabilities along the Colombian-Ecuadorian Pacific Coast, this paper summarizes a research aimed to describe a new integrated methodology to obtain fragility curves for local buildings at risk of a tsunami. This task was accomplished using computational nonlinear structural analysis, combined with a Monte Carlo statistical algorithm to obtain numerical probabilistic fragility curves. The structural capacity of buildings was obtained from nonlinear structural simulations using the finite element method, to calculate later the damage of each building. Two different damage models to estimate the probability of damage were tested in order to evaluate the one that better represents the structural behavior of buildings, in which the tsunami was considered in the structural model as forces scattered to different heights, thus representing different flow depths. As a result, a methodology was developed to calculate theoretical probabilistic fragility curves for the typical structures found in the Colombian Pacific coast, although this methodology could be expanded to any structural system located in any region around the world. As an illustrative example, the methodology proposed in this research was applied to a typical reinforced concrete building, obtained from a previous work in which all the Tumaco city buildings were characterized according to their structural features. [ABSTRACT FROM AUTHOR]

This work presents a detailed analysis of building damage recognition, employing multi-source data fusion and ensemble learning algorithms for rapid damage mapping tasks. A damage classification framework is introduced and tested to categorize the building damage following the recent 2018 Sulawesi earthquake and tsunami. Three robust ensemble learning classifiers were investigated for recognizing building damage from Synthetic Aperture Radar (SAR) and optical remote sensing datasets and their derived features. The contribution of each feature dataset was also explored, considering different combinations of sensors as well as their temporal information. SAR scenes acquired by the ALOS-2 PALSAR-2 and Sentinel-1 sensors were used. The optical Sentinel-2 and PlanetScope sensors were also included in this study. A non-local filter in the preprocessing phase was used to enhance the SAR features. Our results demonstrated that the canonical correlation forests classifier performs better in comparison to the other classifiers. In the data fusion analysis, Digital Elevation Model (DEM)- and SAR-derived features contributed the most in the overall damage classification. Our proposed mapping framework successfully classifies four levels of building damage (with overall accuracy >90%, average accuracy >67%). The proposed framework learned the damage patterns from a limited available human-interpreted building damage annotation and expands this information to map a larger affected area. This process including pre- and post-processing phases were completed in about 3 h after acquiring all raw datasets. [ABSTRACT FROM AUTHOR]

The fine resolution of synthetic aperture radar (SAR) images enables the rapid detection of severely damaged areas in the case of natural disasters. Developing an optimal model for detecting damage in multitemporal SAR intensity images has been a focus of research. Recent studies have shown that computing changes over a moving window that clusters neighboring pixels is effective in identifying damaged buildings. Unfortunately, classifying tsunami-induced building damage into detailed damage classes remains a challenge. The purpose of this paper is to present a novel multiclass classification model that considers a high-dimensional feature space derived from several sizes of pixel windows and to provide guidance on how to define a multiclass classification scheme for detecting tsunami-induced damage. The proposed model uses a support vector machine (SVM) to determine the parameters of the discriminant function. The generalization ability of the model was tested on the field survey of the 2011 Great East Japan Earthquake and Tsunami and on a pair of TerraSAR-X images. The results show that the combination of different sizes of pixel windows has better performance for multiclass classification using SAR images. In addition, we discuss the limitations and potential use of multiclass building damage classification based on performance and various classification schemes. Notably, our findings suggest that the detectable classes for tsunami damage appear to differ from the detectable classes for earthquake damage. For earthquake damage, it is well known that a lower damage grade can rarely be distinguished in SAR images. However, such a damage grade is apparently easy to identify from tsunami-induced damage grades in SAR images. Taking this characteristic into consideration, we have successfully defined a detectable three-class classification scheme. [ABSTRACT FROM AUTHOR]