The DIM2SEA research project aims to increase urban resilience to large-scale disasters. We are engaged in developing a prototype Dynamic Integrated Model for Disaster Management and Socioeconomic Analysis (DIM2SEA) that will give disaster officials, stakeholders, urban engineers and planners an analytic tool for mitigating some of the worst excesses of catastrophic events. This is achieved by harnessing state-of-the-art developments in damage assessment, spatial simulation modeling, and Geographic Information System (GIS). At the heart of DIM2SEA is an agent-based model combined with post-disaster damage assessment and socioeconomic impact models. The large amounts of simulated spatial and temporal data generated by the agent-based models are fused with the socioeconomic profiles of the target population to generate a multidimensional database of inherently “synthetic” big data. Progress in the following areas is reported here: (1) Synthetic population generation from census tract data into agent profiling and spatial allocation, (2) developing scenarios of building damage due to earthquakes and tsunamis, (3) building debris scattering estimation and road network disruption, (4) logistics regarding post-disaster relief distribution, (5) the labor market in post-disaster urban dynamics, and (6) household insurance behavior as a reflection of urban resilience.
This paper presents two opposite perspectives on the labor market in the aftermath of a disaster. The first posits a production sector that is non-tradeable and a labor market with total mobility. This is modeled using agent based simulation. The second presents a production sector that is fully tradeable and a labor market that is perfectly immobile. This is modeled using traditional micro-economic modeling and numerical simulation. Outcomes from the two approaches are compared. In the no-disaster case, participation rates and wages under both approaches settle down to a low-level equilibrium albeit at different rates. In the case of a disaster, outcomes are very different. Under the agent based model labor market mobility results in solutions being found outside the area. In the micro-economic approach workers absorb the recovery process within the area readjusting their demand for labor. When population movement is introduced the system reorganizes at a new equilibrium. The results highlight first, the importance of labor mobility and flexibility and second, the divergent absorption costs in determining the long-term outcomes of a disaster.
Disaster occurs frequently around the world and is no more considered an isolated event. Thus, disaster management has become a hot issue in urban development and human security. However, selecting suitable measures in disaster preparedness is a complex task. Some mitigation measures, which include making dams and updating building codes, prove ineffective and can themselves become a source of added vulnerability. Therefore, the importance of preparedness cannot be ignored, and the relief distribution aspect needs to be appraised as a measure of disaster preparedness. This study integrates disaster preparedness and response. Many parameters cannot be known before a disaster, while relief demand is a key parameter in disaster preparedness modelling. A decision maker needs to identify the properties of relief demand. Seven influencing factors of relief demand are identified. Then, a facility location model is proposed, and the model results are analysed. The results show that the hub-and-spoke distribution system brings higher social benefits.
A methodology to create synthetic earthquake-induced building damage states for urban areas in Japan under specific earthquake events is presented in this paper. The methodology is based on empirical fragility curves for Japanese buildings, the building database of the study area and the strong motion magnitude spatial distribution. A stochastic approach was then used to allocate damage states to buildings. The synthetic building damage scenario is intended to be used as a platform for performing computational simulations of evacuation and relief distribution under multiple damage scenarios. The methodology is applied to an area within Mashiki town, Kumamoto Prefecture, which was affected by the Mw 7.0 2016 Kumamoto earthquake. The comparison of the synthetic buildings damage scenario with the surveyed data inventory shows good agreement in terms of the aggregate statistics.
Demand for household insurance is intuitively perceived as contributing to household and community resilience. However the causality in this relationship is not clear. This paper examines household insurance expenditure and the generation of urban resilience as jointly determined. Potential endogeneity is purged by estimating this relationship as a system and using an instrumental variable approach. Empirical analysis based on aggregated Israeli household expenditure data is used. Results show that instrumenting makes a difference, that a distinction needs to be drawn between personal resilience and environmental resilience and that insurance coverage has an independent effect on resilience different to that of classic social (personal) and economic (property and place-based) characteristics. The policy context of the findings are discussed.
Remote sensing satellite imagery plays an important role in estimating collapsed buildings in the aftermath of a large-scale disaster. However, some previous methodologies are restricted to using specific radar sensors. Others methods, such as machine learning algorithms, require training data, which are extremely difficult to obtain immediately after a disaster. This paper proposes a novel method to extract collapsed buildings based on the integration of satellite imagery, the spatial distribution of a demand parameter, fragility functions, and a geospatial building inventory. The proposed method is applicable regardless of the type of radar sensor and does not require any training data. The method was applied to extract buildings that collapsed during the 2011 Great East Japan Tsunami. The results showed that the proposed method is effective and consistent with the surveyed building damage data.
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.
This paper discusses the applicability of tsunami fragility curves to estimate the damage of buildings due to tsunami. Tsunami fragility curves have been developed empirically out of the damage data obtained from large scale tsunami disasters (Koshimura et al., 2009; Mas et al., 2012a; Suppasri et al., 2011, 2013). Fragility curves can provide an overall idea of the impact of a tsunami event, however, for disaster mitigation purposes a desired application for these curves is the assessment of possible future damage due to tsunami. Thus, this report aims on evaluating the applicability of tsunami fragility curves for building damage estimation. Our hypothesis is that a building damage estimation due to tsunami performed with fragility curves developed in the same location and urban characteristics (i.e. building type, layout, topography, etc.) should provide a high accuracy. This is expected since the data (event and buildings) used to develop the fragility curve contains the sample use for the evaluation.
Near real-time building damage mapping is an indispensable prerequisite for governments to make decisions for disaster relief. With high-resolution synthetic aperture radar (SAR) systems, such as TerraSAR-X, the provision of such products in a fast and effective way becomes possible. In this letter, a deep learning-based framework for rapid regional tsunami damage recognition using post-event SAR imagery is proposed. To perform such a rapid damage mapping, a series of tile-based image split analysis is employed to generate the data set. Next, a selection algorithm with the SqueezeNet network is developed to swiftly distinguish between built-up (BU) and nonbuilt-up regions. Finally, a recognition algorithm with a modified wide residual network is developed to classify the BU regions into wash away, collapsed, and slightly damaged regions. Experiments performed on the TerraSAR-X data from the 2011 Tohoku earthquake and tsunami in Japan show a BU region extraction accuracy of 80.4% and a damage-level recognition accuracy of 74.8%, respectively. Our framework takes around 2 h to train on a new region, and only several minutes for prediction.
This paper takes the 2015 Nepal earthquake as a case study to explore the use of post-event dual polarimetric synthetic aperture radar images for earthquake damage assessment. The radar scattering characteristics of damaged and undamaged urban areas were compared by using polarimetric features derived from PALSAR-2 and Sentinel-1 images, and the results showed that distinguishing between damaged and undamaged urban areas with a single polarimetric feature is challenging. A split-based image analysis, feature selection, and supervised classification were employed on a PALSAR-2 image. The texture features derived from the intensity of cross-polarization show higher correlations with the damage class. Additionally, feature selection revealed a positive influence on the overall performance. Employing 70% of the data for training and 30% data for testing, the support vector machine classifier achieved an accuracy of 80.5% compared with the reference data generated from the damage map that was provided by the United Nations Operational Satellite Applications Programme.
The spatial distribution of the coseismic displacements that occurred along the Futagawa fault during the 2016 Kumamoto earthquake of M<sub>w</sub> 7.0 was estimated using airborne light detection and ranging (lidar) data. In this study, a pair of digital surface models (DSMs) obtained from the high-density lidar data before and after the mainshock on 16 April 2016 were used. A window matching search approach based on the correlation coefficient between the two DSMs was used to estimate the geodetic displacement in the near-field region. The results showed good agreements with the geodetic displacements calculated from strong-motion acceleration records and coincided with the fault line surveyed by the Geological Survey of Japan.
Six buildings were overturned in the town of Onagawa during the 2011 Great East Japan tsunami. This study investigates the possible failure mechanisms of building overturning during tsunami flow. The tsunami inundation depth and flow velocity at each overturned building were recalculated by using a tsunami numerical simulation and verified using a recorded video. The overturning moment is a result of hydrodynamic and buoyancy forces, whereas the resisting moment is a result of building self-weight and pile resistance force. This study aimed to demonstrate that the building foundation design is critical for preventing buildings from overturning. The analysis results suggest that buoyancy force can generate a larger overturning moment than hydrodynamic force and the failure of a pile foundation could occur during both ground shaking and tsunami flow. For the pile foundation, pile resistance force plays a significant role due to both tension and shear capacities at the pile head and skin friction capacity between the pile and soil, which can be calculated from 18 soil boring data in Onagawa using a conventional method in the AIJ standards. In addition, soil liquefaction can reduce skin friction capacity between the pile and soil resulting in a decrease of the resisting moment from pile resistance force.
A project titled “Establishing the advanced disaster reduction management system by fusion of real-time disaster simulation and big data assimilation,” was launched as Core Research for Evolutional Science and Technology (CREST) by the Japan Science and Technology Agency (JST). Intended to save as many lives as possible in future national crises involving earthquake and tsunami disasters, the project works on a disaster mitigation system of the big data era, based on cooperation of large-scale, high-resolution, real-time numerical simulations and assimilation of real-time observation data. The world’s most advanced specialists in disaster simulation, disaster management, mathematical science, and information science work together to create the world’s first analysis platform for real-time simulation and big data that effectively processes, analyzes, and assimilates data obtained through various observations. Based on quantitative data, the platform designs proactive measures and supports disaster operations immediately after disaster occurrence. The project was launched in 2014 and is working on the following issues at present. Sophistication and fusion of simulations and damage prediction models using observational big data: Development of a real-time simulation core system that predicts the time evolution of disaster effect by assimilating of location information, fire information, and building collapse information which are obtained from mobile terminals, satellite images, aerial images, and other new observation data in addition to sensing data obtained by the undersea high-density seismic observation network. Latent structure analysis and major disaster scenario creation based on a huge amount of simulation results: Development of an analysis and extraction method for the latent structure of a huge amount of disaster scenarios generated by simulation, and creation of severe scenarios with minimum “unexpectedness” by controlling disaster scenario explosion (an explosive increase in the number of predicted scenarios). Establishment of an earthquake and tsunami disaster mitigation big data analysis platform: Development of an earthquake and tsunami disaster mitigation big data analysis platform that realizes analyses of a huge number of disaster scenarios and increases in speed of data assimilation, and clarifies the requirements for operation of the platform as a disaster mitigation system. The project was launched in 2014 as a 5-year project. It consists of element technology development and system fusion, feasibility study as a next-generation disaster mitigation system (validation with/without introduction of the developed real-time simulation and big data analysis platform) in the affected areas of the Great East Japan Earthquake, and test operations in affected areas of the Tokyo metropolitan earthquake and the Nankai Trough earthquake.
The 2011 Tohoku Tsunami is considered to be one of the most tragic events in Japan’s disaster history, and represents an important milestone for the research community regarding the investigation of the characteristics of tsunami inundation. A thorough analysis of tsunami inundation was conducted using numerical modeling, and measurements from a video recorded from the rooftop of a building in Onagawa in Miyagi Prefecture. In this study, we analyze the destruction of buildings using numerical simulations and tsunami fragility functions. Numerical results for the locations at which the tsunami eyewitness video was recorded are compared with measurements. In addition, we considered the effect of the breakwater in Onagawa bay to evaluate its contribution to reducing overland tsunami inundation depths. The results of our simulations show that the maximum inundation depth due to the first incoming wave was over 16 m, and over 500 buildings were washed away with this first wave. This result is consistent with the video data. Further, we found that the breakwater, which was not originally designed against tsunami waves, reduced the maximum tsunami inundation depth at least by 2.0 m in Onagawa town.
An agent based model for assessing the welfare impacts of urban disasters is presented. This couples a population allocation algorithm with a simulation platform. The fully articulated model fuses both bottom-up (locational choice for workplace, residence and daily activities) and top-down (land use and housing price) protocols. This study moves beyond current research by addressing economic welfare consequences of urban disasters. The resilience capabilities of different income groups are identified. This is illustrated for the Jerusalem central business district. Empirical results at the micro-scale suggest that physical destruction leads to a zero-sum game within the housing market in which wealthier residents hold an advantage over the poor. This results in the transformation of neighborhoods and displacement of poor and vulnerable populations. Low income groups lose both physical ground and the social support systems that go with location. Policy implications of these findings are discussed.
We revisited the lessons of the 2011 Great East Japan Earthquake Tsunami disaster specifically on the response and impact, and discussed the paradigm shift of Japan's tsunami disaster management policies and the perspectives for reconstruction. Revisiting the modern histories of Tohoku tsunami disasters and pre-2011 tsunami countermeasures, we clarified how Japan's coastal communities have prepared for tsunamis. The discussion mainly focuses on structural measures such as seawalls and breakwaters and non-structural measures of hazard map and evacuation. The responses to the 2011 event are discussed specifically on the tsunami warning system and efforts to identify the tsunami impacts. The nation-wide post-tsunami survey results shed light on the mechanisms of structural destruction, tsunami loads and structural vulnerability to inform structural rehabilitation measures and land-use planning. Remarkable paradigm shifts in designing coastal protection and disaster mitigation measures were introduced, leading with a new concept of potential tsunami levels: Prevention (Level 1) and Mitigation (Level 2) levels according to the level of ‘protection’. The seawall is designed with reference to Level 1 tsunami scenario, while comprehensive disaster management measures should refer to Level 2 tsunami for protection of human lives and reducing potential losses and damage. Throughout the case study in Sendai city, the proposed reconstruction plan was evaluated from the tsunami engineering point of view to discuss how the post 2011 paradigm was implemented in coastal communities for future disaster mitigation. The analysis revealed that Sendai city's multiple protection measures for Level 2 tsunami will contribute to a substantial reduction of the tsunami inundation zone and potential losses, combined with an effective tsunami evacuation plan.