Large-scale Disasters Research Articles

Autonomous Wristband Placement in a Moving Hand for Victims in Search and Rescue Scenarios With a Mobile Manipulator

In this letter, we present an autonomous method for the placement of a sensorized wristband to victims in a Search-And-Rescue (SAR) scenario. For this purpose, an all-terrain mobile robot includes a mobile manipulator, which End-Effector (EE) is equipped with a detachable sensorized wristband. The wristband consists of two links with a shared shaft and a spring. This configuration allows the wristband to maintain fixed to the EE while moving and get placed around the victim's forearm once the contact is produced. The method has two differentiated phases: i) The visual moving hand tracking phase, where a 3D vision system detects the victim's hand pose. At the same time, the robotic manipulator tracks it with a Model Predictive Controller (MPC). ii) The haptic force-controlled phase, where the wristband gets placed around the victim's forearm controlling the forces exerted. The wristband design is also discussed, considering the magnitude of the force needed for the attachment and the torque the wristband exerts to the forearm. Two experiments are carried out, one in the laboratory to evaluate the performance of the method and the second one in a SAR scenario, with the robotic manipulator integrated with the all-terrain mobile robot. Results show a 97.4% success in the wristband placement procedure and a good performance of the whole system in a large scale disaster exercise.

If not now, when? Climate disaster and the Green vote following the 2021 Germany floods

Can first-hand experience of a climate-related natural disaster make citizens more likely to vote in favour of progressive climate politics? Leveraging the rare occurrence of a large-scale disaster just two months before a federal election, we use a difference-in-differences design to study the short-term electoral effects of the devastating 2021 Germany floods on voter support for Germany’s major environmentalist party, the Green Party. Compared to other German voters, those living in areas affected by the floods were marginally (0.4–1.6 percentage points) more likely to vote for the Greens. The largest increases in Green vote share are observed in municipalities which were directly exposed to flooding. Contrary to expectation, we tend to find larger increases in Green Party support in the less severely affected areas. Despite substantial increases in turnout in affected areas, we find that the observed increase in vote share for the Greens was rather driven by a persuasion effect on voters who previously supported other parties. In the absence of evidence that the floods led to an increase in voters’ issue prioritisation of climate change, our results highlight the limited possibility for major natural disasters to induce increased localised support for Green parties.

Current Status and Issues of Information Sharing in Disaster Response in Japan: Information Linkage by “SIP4D”

Information sharing by related organizations is crucial in disaster response. In the event of a large-scale disaster that makes it difficult for the affected local government to respond independently, support from the central government and non-affected local governments is required. In such cases, effective activities by support organizations cannot be expected unless sufficient information is shared. The authors are currently working on the research and development of a platform for sharing information held by related organizations in a Japanese government project. In particular, they focus on the common operational picture (COP) necessary for effective activities by unifying the situational awareness of related organizations. This paper reports on the progress of the project, which commenced in 2014, and introduces current issues, directions for solving them, and efforts toward the advanced use of disaster information. Based on the results of a questionnaire distributed among local governments nationwide at the beginning of the project, the authors provide an overview of the state of disaster information system development in 2014. Based on the experience of actual disaster response, the authors will discuss the activity status of the Information Support Team (ISUT), the current status and issues of Shared Information Platform for Disaster Management (SIP4D), and NIED-Disaster Information-Sharing System (NIED-DISS).

Emergency Island Medical Care: Ⅰ. October 2013 Izu Oshima Landslide Disaster

In this study, we explored the initial response of the fire and disaster prevention organization and the medical activities of the Oshima Medical Center － which is the only local medical center － during the landslide disaster that occurred in the Motomachi area of Izu Oshima on October 16, 2013. Owing to the prediction of the course of the typhoon, the Oshima Fire Department and disaster prevention-related organizations had begun preparations to deal with the consequences of the typhoon a day before the landslide occurred. On the day of the disaster, the Oshima Medical Center handled 24 cases of the disaster, 8 of which were urgently transported by helicopter to Tokyo Metropolitan Hiroo Hospital, a main island hospital in Tokyo. Furthermore, on October 20, 14 inpatients from the Oshima Medical Center were transported to Tokyo Metropolitan Hiroo Hospital by a Self-Defense Forces (SDF) aircraft as a measure against evacuation advisories, owing to rainfall. All the transported cases improved and were eventually discharged from the hospital. Thereafter, the Oshima Medical Center successfully evacuated hemodialysis patients from the island and shifted the medical center itself. It was a large-scale disaster leaving in its trail 36 dead, 3 missing, 22 injured, and 239 homes damaged. The Oshima Medical Center escaped the disaster and significantly contributed to the swift execution of disaster medical care. However, overall, glitches in the communication system and the collaboration between organizations during the disaster were exposed. Assuming that various natural disasters will continue to hit us, it is important to prepare sufficiently during normal times to ensure the quality and quantity of medical activities.

Providing community services for persons with disabilities during the COVID-19 pandemic: A scoping review.

Community organisations and municipalities support people with disabilities by providing resources and services that are essential for their engagement in the community. Their services were particularly impacted by restrictions related to the COVID-19 pandemic. The aim of the study is to identify scientific literature that examines how community organisations and municipalities adapted services and resources provided to people with disabilities as a result of the COVID-19 pandemic. A scoping review was conducted by searching the databases Medline, Embase, CINAHL, PsycINFO and Web of Science Core Collection in January 2021. Fifteen studies were included from the initial search strategy of 7651 individual studies. Most of the studies were quantitative studies (73.3%; n=11) and aimed at describing the adaptations put in place during the COVID-19 pandemic (66.7%; n=10). Most services and resources involved some form of preventive healthcare (66.7%; n=10). The adaptation of modalities for delivering resources and services varied widely across organisations (e.g. online or a combination of online and in-person) but mostly led to an improvement of the studied outcome (e.g. social skills, quality of life). Barriers (e.g. need for a reliable internet connection, lack of technology literacy from the member) and facilitators (e.g. flexibility and planning from the organisations) for these adaptations have been identified, but there is little information surrounding their cost. The results highlight that the delivery of online services has increased since the inception of the COVID-19 pandemic with valuable outcomes. However, further research is needed to better identify the barriers, facilitators and outcomes of remote services to better face future large-scale disasters like the COVID-19 pandemic and to better support individuals who cannot reach in-person services.

Spatial-Temporal Characteristics of Multi-Hazard Resilience in Ecologically Fragile Areas of Southwest China: A Case Study in Aba.

Aba’s topography, weather, and climate make it prone to landslides, mudslides, and other natural disasters, which limit economic and social growth. Assessing and improving regional resilience is important to mitigate natural disasters and achieve sustainable development. In this paper, the entropy weight method is used to calculate the resilience of Aba under multi-hazard stress from 2010 to 2018 by combining the existing framework with the disaster resilience of the place (DROP) model. Then spatial-temporal characteristics are analyzed based on the coefficient of variation and exploratory spatial data analysis (ESDA). Finally, partial least squares (PLS) regression is used to identify the key influences on disaster resilience. The results show that (1) the disaster resilience in Aba increased from 2010 to 2018 but dropped in 2013 and 2017 due to large-scale disasters. (2) There are temporal and spatial differences in the level of development in each of the Aba counties. From 2010 to 2016, disaster resilience shows a significant positive spatial association and high-high (HH) aggregation in the east and low-low (LL) aggregation in the west. Then the spatial aggregation weakened after 2017. This paper proposes integrating regional development, strengthening the development level building, and emphasizing disaster management for Aba.

The 2018 Japan Floods increased prescriptions of anti-dementia drugs among disaster victims: a retrospective cohort study based on a national health insurance database

BACKGROUND AND AIM: The occurrence of weather-related disasters is increasing with the progression of global climate change. Natural disasters increase the risk of impaired cognitive function of elderly victims. It is not clear what effects such natural disasters have on the prescriptions of drug treatment for dementia. Thus, we evaluated the effect of the 2018 Japan Floods, the second-largest water-related disaster in Japan, on the prescriptions of anti-dementia drugs (ADD) for elderly people. Methods: From the National Database of Health Insurance Claims, we extracted prescription data in Hiroshima, Okayama and Ehime prefectures for one year before and after the disaster. From the database, 1,710,119 people aged 65 years or over were selected as the study participants. Sex- and age-adjusted odds ratios (ORs) of victims for new ADD prescriptions were calculated using logistic regression models. A difference-in-difference analysis was conducted to take into account the trends for the ORs before the disaster. Furthermore, among continuous ADD users, the effects of disaster on the increasing trend for ADD prescriptions (daily dose or number of drug types) was also evaluated. Results: Of 1,710,119 people enrolled, 15,994 (0.9%) were recorded as a disaster-victims, and 112,289 (6.6%) were prescribed ADD. Among original non-users, victims were more likely to start using ADD after the disaster than non-victims [adjusted OR=1.33 (95% CI:1.16–1.52)]. Among continuous users, an increasing trend for ADD prescriptions was more often observed for victims than non-victims [1.61 (1.13–2.31)]. These associations were maintained even after considering the pre-disaster trend of ADD use. Conclusions: The natural disaster results in an increased number of users of anti-dementia medications. Special attention to such neurological shifts and welfare support is needed for elderly victims of a large-scale natural disaster.

Victim identification from the September 11, 2001 attack on the World Trade Center: Past trends and future projections

Victim identification following mass fatality events is critically important. Extensive traumatic injuries and body fragmentation add complexity to this process. World Trade Center (WTC) identification efforts have been ongoing for over 20 years and this study tracks identification trends from the 2753 known WTC victims and the 21,905 recovered remains. For identified victims, data include the number of remains identified, date(s) of the identification(s), and identification modalities. Results show a heavy reliance on DNA due to body fragmentation. Other modalities played an important role initially, but DNA eventually became the singular identification modality. For large-scale disasters involving significant body fragmentation, aggressive DNA testing strategies are critical for victim identification. Over time, the number of linked remains (portions of previously identified individuals) will greatly outnumber the new identifications (first-time identifications). A novel approach using statistical modeling from ecology studies was applied to estimate future WTC identification rates using Identification Accumulation Curve extrapolation with the Good-Toulmin estimator. Projections indicate there will be 76 first-time identifications (95% CI: 49–117) through the successful DNA testing of 3404 unidentified, fragmentary remains. The remainder of the identifications would be additional portions of previously identified victims. These results may be instructional for management of other large-scale, protracted victim identification efforts.

A Semantic Data-Based Distributed Computing Framework to Accelerate Digital Twin Services for Large-Scale Disasters.

As natural disasters become extensive, due to various environmental problems, such as the global warming, it is difficult for the disaster management systems to rapidly provide disaster prediction services, due to complex natural phenomena. Digital twins can effectively provide the services using high-fidelity disaster models and real-time observational data with distributed computing schemes. However, the previous schemes take little account of the correlations between environmental data of disasters, such as landscapes and weather. This causes inaccurate computing load predictions resulting in unbalanced load partitioning, which increases the prediction service times of the disaster management agencies. In this paper, we propose a novel distributed computing framework to accelerate the prediction services through semantic analyses of correlations between the environmental data. The framework combines the data into disaster semantic data to represent the initial disaster states, such as the sizes of wildfire burn scars and fuel models. With the semantic data, the framework predicts computing loads using the convolutional neural network-based algorithm, partitions the simulation model into balanced sub-models, and allocates the sub-models into distributed computing nodes. As a result, the proposal shows up to 38.5% of the prediction time decreases, compared to the previous schemes.

Decentralized Energy and Water Networks for Community Resilience against Natural Disasters

Large-scale natural disasters can severely damage the energy and water infrastructure, leading to disruption of services. In addition to raising possible health risks, lack of access to electricity and water can impede or prolong recovery from the disaster. To be resilient against such events, the electric power grid and the water distribution network must be able to continue operating with minimal impact on end-users and with constricted costs. Naturally, one approach is to reinforce the energy and water infrastructure against natural hazards. However, this may be cost-prohibitive or even infeasible. An alternative solution is to allocate sufficient localized resources such that these networks can continue operating at a decentralized scale until the main network is repaired and restored. In this paper, a solution is proposed to design a localized water and energy system that can serve a community affected by a natural disaster, with little external support. An optimization model is developed to optimally allocate resources, e.g., distributed energy resources and water storage capacity, based on the needs of the community and subject to operational constraints. Such decentralized systems can significantly improve the resilience of the energy and water networks and assist affected communities in the aftermath of disaster events.

Eco-Friendly Disaster Evacuation Framework for 6G Connected and Autonomous Vehicular Networks

Crowd and evacuation management have been active areas of research and study over recent years. Connected Autonomous Vehicles (CAVs) could provide safe and vital life-saving reinforcement when evacuating populations from a large-scale disaster site. However, navigation in disaster-affected environments is vulnerable to the presence of unpredictable moving obstacles. Existing solutions perform optimal route determination while considering the presence of a single rescue vehicle. To optimize path planning and enhance energy efficiency in the presence of a fleet of rescue vehicles and Unmanned Aerial Vehicles (UAVs), multiple sources and target destinations, we propose a Multi-Agent Deep Reinforcement Dijkstra (MADRD) algorithm in a sixth generation (6G) assisted environment to achieve cooperative navigation in the Route Planning Framework. The MADRD algorithm allows vehicles to learn from the experiences and behavior of other CAVs to obtain an optimal path from the source to the affected target site. Route selection and throttle parameters are optimized to achieve efficient fuel consumption, reduced travel time and lower traffic congestion. We simulate the Route Planning Framework on a large-scale real-world road network. The experimental results indicate that MADRD achieves 40% less fuel consumption, 45% higher road throughput and 34% less evacuation time than its state-of-the-art counterparts.

EVALUATION OF LOCATION SELECTION CRITERIA FOR COORDINATION MANAGEMENT CENTERS AND LOGISTIC SUPPORT UNITS IN DISASTER AREAS WITH AHP METHOD

In recent years, human beings and our planet have suffered great losses in the frequent disasters. Effective and timely intervention is of utmost importance in all large-scale disasters, whether natural or man-made. In this article, a study has been conducted on a model in which the location selection criteria of the management and support centers, where the coordination works as well as the management and administration are carried out in disaster areas, are evaluated by the Multi-Criteria Decision Making (MCDM) method. For this, an in-depth literature analysis was carried out at the first stage, and then all the findings obtained as a result of the literature research were presented to the professionals related to the subject, and expert opinion was sought. In the light of expert opinion, the location selection criteria for the coordination management center and logistic support units in disaster areas were determined, and a model proposal was made, in which the importance values ​​were weighted by using one of the MCDM methods, The Analytic Hierarchy Process (AHP), which is widely used.

Utilization of Mental Health Support Systems in the Aftermath of Disasters in Japan: Statistical Data of the Miyagi Disaster Mental Health Care Center.

Large-scale natural disasters have a significant effect on residents’ mental health. The Miyagi Disaster Mental Health Care Center (DMHCC) was established as a long-term mental health care center in response to the 2011 Great East Japan Earthquake and Tsunami (GEJE). Although six DMHCCs have been established in Japan, their exact role and functioning are still unclear. This study aimed to explore which population used the center in each recovery phase. Logistic regression was performed to identify the residents’ characteristics according to the consultation pathways using the data collected by the Miyagi DMHCC. These data included personal information of the residents who were supported by the center from 2013 to 2018. The working-age unemployed men sought help by themselves, and the isolated older females were supported by home visits through the health survey. Long-term mental health care centers should observe community recovery and provide appropriate support. The implications of this result and future research directions are discussed.

Evaluation of community resilience to debris-flow disasters: A case study of Nantou, Taiwan

Taiwan is located in the subtropical monsoon region. Typhoons, earthquakes, and rainfall have historically been the most serious natural disasters affecting the region, especially its geologically sensitive mountain settlements. The heavy rain associated with typhoons can readily cause large-scale compound disasters such as landslides, rockfalls, and debris flows, and thus can cause sudden blockages to waterways. The existing body of research on earth and rock disaster prevention mostly considers the potential of occurrence and the degree of harm, but pays relatively scant attention to community resilience. To fully understand the potential risks of hillside communities exposed to debris-flow disasters, and the occurrence of unavoidable disasters, it is essential to gauge each community's pre-disaster preparedness, as well as its disaster response and post-disaster recovery capabilities. Accordingly, through a rigorous literature review, the Fuzzy Delphi method (FDM) and the analytical hierarchy process method (AHP), this study develops a resilience assessment framework. The development process indicated that the most important factor in community resilience is the community's environment, followed by adjustment and learning: i.e., the residents' cognition and response mechanisms to debris-flow disasters. Other key assessment indicators include the degree of potential hazard, the ratio of landslide area to exposed area, the local history of such disasters, risk perceptions, and population density. The framework is therefore valuable for enhancing community resilience and disaster preparedness, and reducing the impact of disasters.

Mechanical behavior of concrete pipes with erosion voids and the effectiveness evaluation of the polyurethane grouting

Research on pipes ringed by erosion voids is mainly concerned with circumferential mechanical behavior but ignores longitudinal bending responses induced by the relative motion of the pipe joints at voids. There has been limited innovative experimental investigation into pipe behavior that assesses the effectiveness of polyurethane (PU) grouting in rehabilitating erosion voids. Mechanical behavior tests of concrete pipes of intact, with an erosion void at the invert, and PU grouted were performed using a self-developed large-scale disaster testing system, and the measured results were numerically verified. Results show that the peaks of the positive and negative longitudinal bending moments of the intact pipes and pipes with a void at the invert appear at SgL2 and SgR2, SgL3 and SgR3, respectively, and the positive longitudinal bending moment is about 1.6–3.0 times higher than the negative ones. Voids at the invert have a significant effect on the longitudinal bending moments of the pipe segments located on the adjacent sides of the void. The longitudinal bending moments of the pipes with different void widths decrease with the increase of void width at SgL1 and SgR1 and increase with the increase of void width at SgL2 and SgR2, SgL3 and SgR3. The longitudinal bending moments of the pipes with different void lengths increase with the increase of void length in the whole longitudinal length. The circumferential bending moments at the bell-and-spigot decrease as the void width and void length increase. The maximum increases in the vertical displacements and maximum shear displacements of the pipes with different void widths and void lengths are 63–150% and 150–221%, 225–325%, and 325–550%, respectively, compared with the intact pipes. The rehabilitation efficiencies of the longitudinal and circumferential bending moments and the vertical displacement ranged from 85–93% and 85–94%.

Modeling the impact of traffic management strategies on households' stated evacuation decisions

Evacuation traffic management has been implemented in large-scale disaster evacuations (such as hurricanes) to facilitate traffic flow and reduce travel delay. The outcomes of these strategies were quantified via traffic simulation but were assumed to have no/limited impacts on households' evacuation-related decisions. This study analyzed and modeled the impact of these strategies on five evacuation related household choices (evacuate/stay, departure time, route, vehicle, and destination) separately based on 415 responses collected from a stated preference survey. The survey was conducted in 2017 in coastal areas near Hampton Roads. Traffic management is likely to motivate some (32%) households to evacuate instead of sheltering in place. In addition, not all households take the interstates with traffic management even though route choice is the most likely to be affected by traffic management. Households need more information for their departure time and destination choices in response to traffic management since they are more likely to feel uncertain of the impact of the strategies on these decisions. Such uncertainty in departure time and destination choice is especially true for those who evacuate late and for those who choose accommodations other than the home of friends/relatives. Emergency management agencies should also be aware that some households may intentionally depart before traffic management starts. Among the five choices, vehicle use is the choice that is least likely to be affected. All the above-mentioned findings potentially affect parameter specifications in evacuation traffic simulation studies. This study then used multinomial logit models to estimate the impacts of traffic management on each of the five evacuation related choices. The model estimation results can help improve evacuation demand predictions and guide evacuation information dissemination.

1-G experimental study of the collapse effects on buried steel gas pipelines

Collapse disasters are commonly occurring geological disasters around buried gas pipelines and severely threaten the safety of pipeline operations. In this study, to investigate the soil deformation characteristics and mechanical response law of the pipeline under collapse, an experimental box (6 m × 1 m × 1.2 m) was built, and 1-G collapse experiments with different pipe diameters and burial depths were performed. The soil deformation laws, change in earth pressure around the pipeline, and mechanical response of the pipeline during the expansion of the collapse range were obtained by comparing and analysing the experimental results. Pipeline collapse disasters can be divided into two stages: before and after large-scale collapse disasters. The burial depth has an influence on pipeline collapse disasters mainly in a small-scale collapse range. As the collapse range increases, the influence of the burial depth on the strain distribution of the pipeline gradually decreases. However, an increase in pipe diameter can lead to a significant increase in the large-scale collapse range. Large-diameter pipelines bear greater strain than small-diameter pipelines after a large-scale collapse. This study provides data support for further research and serves as a valuable reference for pipeline safety evaluation in collapse disasters.

Measure of Utilizing Space Database Information for Improvement of Efficient Disaster Management (Focusing on Nuclear Power Plant Accidents)

The damage caused by disasters is increasing worldwide, with hundreds of thousands of deaths due to the occurrence of complex large-scale disasters such as the 2010 Haiti earthquake and the 2004 Indian tsunami. South Korea has also experienced human casualties and damage to property caused by large-scale disasters in the past 10 years. Accordingly, a disaster-appropriate response measure is needed. Thus, we conducted this study to present a measure of utilizing spatial database and image information to improve the efficiency of disaster management that is operated based on the country’s existing national disaster management system. We present an efficient disaster response measure that differs from the existing collection-, reporting-, and propagation-oriented operating methods of disaster information through the use of spatial database and image-based information that can be combined with mandatory information with regard to nuclear power plant accidents. Thus, this study contributes to deriving a system that could collect and provide information rapidly at the time of disaster by defining the attribute and spatial information required at the time of disaster during nuclear power plant accidents and by deriving available systems and providing institutions.

A Community Impact Scale for Regional Disaster Planning with Transportation Disruption

This paper proposes a simple analytical scheme and associated qualitative impact scales that capture the spatially varying effects of a regional disaster. Large-scale disasters that affect many towns and cities pose particular challenges for emergency response planning. For example, disruption to transportation systems can impede regional supply chains of critical goods, thereby exacerbating the impacts suffered locally in communities. Conventional metrics of disaster severity, such as number of casualties or intensity of ground shaking, do not adequately capture how community impacts and needs may vary across the affected region, and they do not typically consider regional transportation disruption. Using a series of impact scales, the approach in this paper captures essential attributes of three broad components related to community impacts from a regional disaster—local disaster impacts in a community, regional transportation disruption to the community, and the community’s coping capacity—and aggregates them to an overall metric of community impact. The approach can be implemented with widely varying degrees of data availability, as demonstrated in two case applications. Both cases involve an M9 Cascadia subduction zone earthquake affecting a broad region of coastal British Columbia, Canada. The first application illustrates how in a pre-event planning situation, modeled results can be used to anticipate which communities are at greatest risk, and to help prioritize mitigation and emergency response planning. The second case demonstrates how in the immediate aftermath of a disaster, the approach can be used with limited information to help prioritize response and recovery activities.

A Productive Tension Decoupled: An Institutional Account of the Boeing 737 MAX Disaster

Two fatal crashes of Boeing 737 MAX airplanes occurred on October 29, 2018 and March 10, 2019 killing 346 people, but what exactly were the forces that led to the commercialization of such an obviously flawed design? We provide an institutional logics perspective of this large-scale failure, which complements existing accounts of disaster. After Boeing´s merger with McDonnell Douglas, and because of growing competitive pressure from Airbus, the corporate logic of managerial rationalism became dominant and the professional logic of scientific control became subordinated and decoupled from central organizational aims. Internal processes within Boeing reinforced this subordination as the professional logic simply became a means to obtaining the ends of corporate efficiency and cost control by obtaining regulatory approval and thus external legitimacy. We show how altering the balance of institutional logics may trigger a series of changes that accentuates the subordination, resulting in an increased likelihood of disaster. Complexity in this organizational context benefitted from a productive tension between logics, which was relaxed through a decoupling strategy with disastrous consequences. This story about organizational risk and large-scale disaster has implications for research on complexity, decoupling, and the architecture of institutional logics as means and ends.