Disaster Response Research Articles

Operational Stress Injuries in Disaster Responders: A Framework for Supporting Mental Health in Climate Crisis.

The rising occurrence of natural disasters linked to climate change has drawn the attention of disaster response leaders to the significance of addressing operational stress injuries among disaster response personnel. We define and describe operational stress injuries in this workforce and explore theoretical frameworks that can inform the development of programs and interventions to mitigate these effects. We aim to establish a conceptual framework for understanding an operational stress injury by introducing a model specific to disaster responders. We also emphasize strategies that can be employed within a stress injury awareness framework to promote the mental well-being of those who respond to climate-related disasters.

Advanced spatial query processing in IoT through mobile agent integration

In IoT environments, various applications and protocols rely on spatial query processing to obtain, analyze, and interpret information from sensor nodes based on their location within a specific area. It enables the efficient monitoring and management of spatial data, which is crucial for applications such as environmental monitoring, smart cities, and disaster response. By querying the location and status of sensor nodes, spatial query processing facilitates real-time decision-making, optimizes resource allocation, and enhances the overall performance of the IoT system.This paper focuses on Window queries, a key spatial query type that retrieves data from sensors within a defined two-dimensional zone. The research aims to improve spatial query processing in IoT environments by proposing a Window query processing method that balances various performance factors such as energy efficiency, latency, accuracy, and query success rate. We introduce a novel approach using mobile agents to enhance spatial query processing by addressing the constraints of sensor networks. Simulation results from the NS-2 simulator demonstrate the effectiveness of our method across different parameters and environments.

Editorial: Citizen Initiatives for Global Solidarity (CIGS) in disaster and humanitarian response

Editorial: Citizen Initiatives for Global Solidarity (CIGS) in disaster and humanitarian response

Perceived Responses of International Tourists to Transportation and Tourism Services During Typhoons Faxai and Hagibis in Japan

There is a limited understanding on the information-seeking behavior of international tourists during disaster response scenarios due to the lack of empirical studies on crisis communication in Japan. This study clarifies the topics generated from both international tourists and official Twitter accounts by applying the embedding Bidirectional Encoder Representations from Transformers (BERT) topic model and examines the temporal sentiment changes toward transportation and tourism using the sentiment scores obtained from topic-based Valence Aware Dictionary and sEntiment Reasoner (VADER) sentiment analysis during disasters. A case study was conducted using Twitter data on Typhoons Faxai and Hagibis, which struck Japan in 2019. This study found differences in the topics generated among international tourists and officials in response and a continuous negative sentiment toward specific transportation services. The managerial implications of these findings regarding the use of social media in crisis communication in tourism are also discussed.

Special Issue on Robotics and Mechatronics Technology for Aerial Robots

Aerial robot technologies, such as drones and unmanned aerial vehicles, are rapidly advancing in various domains. These robots now possess enhanced autonomous flying abilities and use sensor technology, Global Navigation Satellite System (GNSS), Light Detection and Ranging (LiDAR) system, and cameras to avoid obstacles, automatically land, and track flights. Evolving technologies in airspace management and route optimization ensure that flights are efficient and safe. Aerial robots have a wide range of applications, such as agriculture, disaster response, environmental monitoring, and inspection. These devices excel at precise data collection thanks to their high-resolution cameras and high-precision sensors. Swarm robotics, which allows collaboration between multiple aerial and ground robots, is also progressing. This enables quick coverage of large areas, which benefits disaster relief and land management. In addition, research and development of flying robots modeled after flying animals is underway. The evolution of aerial robots results in their efficient use in a variety of fields and new applications. This special issue includes the latest research papers and development reports on aerial robotic technology from the aforementioned perspectives. We hope that this special issue will pique researchers’ and engineers’ interest in aerial robots, thereby encouraging additional research and development. Finally, we would like to express our gratitude to all authors, all reviewers, the editorial board of the Journal of Robotics and Mechatronics, and Fuji Technology Press Ltd.

Implementing Swarm Robotics for Coordinated Multi-Agent Systems in Search and Rescue Operations to Improve Efficiency and Success Rates in Disaster Response

When there is a crisis, search and rescue efforts that are quick and successful can mean the difference between life and death. But standard search and rescue methods often have a hard time in emergency areas because they are so complicated and hard to plan for. This study suggests using swarm robots for organized multi-agent systems in search and rescue operations to deal with these problems. The objective is to form these operations more effective and effective. Swarm robots employments the thoughts of self-organization and decentralized control that came from studying social bug colonies to form frameworks with numerous free specialists that can act as a entirety. These bots, which are as a rule exceptionally basic and as it were have fundamental sense and talking capacities, work together without any issues to reach their objectives. These mechanical frameworks are solid, adaptable, and adaptable since they utilize the control of swarm insights. These are all imperative qualities for dealing with changing and perilous fiasco settings. One of the finest things almost utilizing swarm robots for look and protect is that it lets you rapidly cover huge zones. Conventional strategies that depend on a little bunch of organized robots or human labourers may not be able to look expansive calamity scenes rapidly. Swarm robots, on the other hand, lets you send out numerous units that can investigate the area at the same time, making it simpler to reach and quicker to reply. Swarm mechanical technology is additionally more strong when things go off-base or the world changes since it isn't controlled . The system can change and reform without centralized control if individual workers run into problems or run into problems. This keeps the search and recovery efforts going.

State of continental discharge estimation and modelling: challenges and opportunities for Africa

ABSTRACT Africa’s diverse climates and sparse hydro-meteorological networks create significant challenges in accurately estimating river discharge. Discharge data are crucial for managing water resources and predicting extremes. Our review assesses the data gap, existing methods, and technologies for river discharge estimation in Africa. Limited gauging networks on rivers, including in 63 transboundary basins, hinder accurate discharge modelling, affecting resource management and disaster response. Despite the potential of remote sensing, Geographic Information System (GIS), satellite imagery, and machine learning, their large-scale application for river discharge monitoring in Africa is limited. We propose the use of a monitoring system involving local communities in data collection and decision making, supported by global data centres, enhanced regional data sharing, and strengthened transboundary cooperation. For example, incorporating water data products, including discharge data, in data cubes, such as Digital Earth Africa, could improve monitoring. Strategic investments in hydro-meteorological instrumentation are crucial for strengthening climate resilience.

Resource management in disaster relief: a bibliometric and content-analysis-based literature review

AbstractDisasters cause huge economic losses, affect the lives of many people, and severely damage the environment. Effective resource management during disaster preparedness and response phases improves distribution efforts and service levels and, hence, accelerates the disaster relief operations. Resource management in response to catastrophe has received increasing research attention in recent years, but no review paper focuses on this specific topic. Thus, the main purpose of this paper is to review the existing literature on resource management for disaster relief published in English in peer-reviewed journals in order to fill the gap. We apply bibliometric, network, and content analyses in our review to identify popular research topics, classify the literature into research clusters, and analyze the interrelationships between these research clusters. The second purpose of this paper is to identify gaps and trends in existing research. Finally, we propose six future research directions that provide a roadmap for resource management research for disaster relief.

Fusing remote and social sensing data for flood impact mapping

AbstractThe absence of comprehensive situational awareness information poses a significant challenge for humanitarian organizations during their response efforts. We present Flood Insights, an end‐to‐end system, that ingests data from multiple nontraditional data sources such as remote sensing, social sensing, and geospatial data. We employ state‐of‐the‐art natural language processing and computer vision models to identify flood exposure, ground‐level damage and flood reports, and most importantly, urgent needs of affected people. We deploy and test the system during a recent real‐world catastrophe, the 2022 Pakistan floods, to surface critical situational and damage information at the district level. We validated the system's effectiveness through various statistical analyses using official ground‐truth data, showcasing its strong performance and explanatory power of integrating multiple data sources. Moreover, the system was commended by the United Nations Development Programme stationed in Pakistan, as well as local authorities, for pinpointing hard‐hit districts and enhancing disaster response.

Self-care strategies used by disaster responders after the 2023 earthquake in Turkey and Syria: a mixed methods study

BackgroundDisaster responders are exposed to several physical and mental health risks. This study aimed to describe self-care strategies used by disaster responders after the earthquake in Syria and eastern Turkey in February 2023.MethodsA study specific web-based questionnaire survey was used to collect quantitative and qualitative data according to a convergent mixed methods approach. Data from 252 disaster responders responding to the earthquakes in Turkey and Syria were analyzed using both descriptive and analytical statistics and summative content analysis of free-text answers. Data were collected in March to July, 2023.ResultsThe most used self-care strategies included resting, social support from colleagues in the field, extra intake of food or drink, and intake of medicines. The recovery strategies varied due to previous disaster response experience, indicating that supportive self-care strategies can be developed or learned.ConclusionGiven the extreme conditions and limited possibilities of external support, sufficient self-care is an essential competence among disaster responders. Self-care strategies can be both external processed such as intake of medicines, social support from others, and internal processes such as personal reflection. Providing oneself with self-care activities seems to be a skill developed with increasing experience supported by pre-deployment training. Therefore, to enhance resilience, self-care strategies should be encompassed in pre-disaster response training.

Get ready: disaster preparedness and response for people with chronic illnesses living in hazard-prone Petone, Aotearoa New Zealand

PurposeThe purpose of this research is to understand how people living with chronic illness understand their healthcare needs, particularly as they can be disrupted by natural hazards events (e.g. earthquakes, flooding and landslides), which can result in chronic health conditions becoming acute. Research has shown that, because of such disruption, people with chronic illness face the greatest risk of adverse health outcomes during a disaster, placing significant demand on healthcare systems.Design/methodology/approachThe research draws on a narrative analysis of in-depth, semi-structured interviews with eight people who have a chronic illness and live in a hazard-prone location, namely Petone, Lower Hutt, Aotearoa New Zealand.FindingsTwo major narrative themes were identified: “Social Capital”, which included the three subthemes of community, social connection and support and family and friends. Tellers also experienced a strong sense of “Independence”, as demonstrated through seven subthemes that included declaration, defiance, rules, experience, acceptance, responsibility, self-surveillance and self-management.Practical implicationsPractice output from this research includes the development of fit for purpose emergency management policies and other educational resources to ensure equity, access and choice for people with a chronic illness and their communities.Social implicationsThis research produces in-depth knowledge and insight into the preparedness and response needs of people who live with a chronic illness. This is an under-research area in disaster management in Aotearoa New Zealand.Originality/valueRoutine medical treatment for people living with chronic illness can be disrupted by natural hazard events (e.g. earthquakes, flooding and landslides), which can result in chronic health conditions becoming acute. Research has shown that, because of such disruption, the chronically ill face the greatest risk of adverse health outcomes during a disaster, placing significant demand on healthcare. This research investigates how people living with chronic illness experience disaster preparedness and response while residing in a hazard-prone location, namely Petone, Lower Hutt, Aotearoa New Zealand. The study argues that it is imperative to acknowledge the experiences of people with chronic illness as they provide specific insights into their needs during a disaster.

Capacity and crisis: examining the state-level policy response to COVID-19 in Tamil Nadu, India.

The capacity of government agencies to develop effective policy responses to external shocks is an important area of focus for health policy processes, as illustrated by the COVID-19 pandemic. However, few empirical studies exploring sub-national capacity of governments and the influence of institutional, organizational and political factors in shaping the policy response to complex emergencies have been conducted. The purpose of this study is to examine the governance capacity to develop and implement a policy response to a major health emergency-COVID-19-in Tamil Nadu, India, and to understand the factors shaping governance capacity during the first and second waves (2020-2021). Tamil Nadu offers a useful case for exploring governance capacity due to its longstanding public health institutions and previous experiences with disaster and outbreak response. We utilized three sources of data: (1) a review of key policy documents (n = 164); (2) a review of English-language media articles in the Indian press (n = 336); and (3) in-depth interviews with senior decision-makers, technical experts and other stakeholders (n = 10). We present four key findings from this analysis. First, Tamil Nadu's institutional framework enabled state-level governance capacity during an emergency of massive complexity, allowing for flexibility and nimbleness to adapt to evolving dynamics of centralization and decentralization over the course of the pandemic. Second, the ability to integrate public health expertise was circumscribed at important phases. Third, while coordination with external experts was utilized extensively, engagement with civil society groups was perceived as limited. Fourth, the electoral cycle was perceived by some to have constrained governance capacity at a critical point in the pandemic. By analysing the dynamics of state-level capacity in Tamil Nadu during a complex emergency, this study provides important learnings for other contexts globally regarding the drivers shaping capacity to develop and implement policy responses to crises.

Comparative Analysis of Electric and Conventional Vehicles Performance in the Evacuation Process of Mount Semeru Eruption Victims Based on Geographic Information Systems

The Lumajang Regency is highly vulnerable to various natural disasters, particularly the potential eruption of Mount Semeru. In disaster response efforts, the local government needs to prepare effective and efficient evacuation routes, taking into account the magnitude of the eruption impact in the Semeru disaster-prone area. This research focuses on evacuating vulnerable residents using electric and conventional vehicles. This study is categorized as a vehicle routing problem with energy constraint (VRPEC) because the electric vehicles utilized in this research do not require recharging during their operational process, ensuring rapid evacuation as it is essential. By utilizing Geographic Information Systems (GIS)-based optimization, the best route to evacuate all victims within 12 h is determined. This study involves developing scenarios considering the number of vehicles and their travel distances. There are also evacuation guidelines, including the implementation of priority points and evacuation zone usage. The research results indicate that scenarios EV 5, 8, and 10 are the most optimal for using electric vehicles. Meanwhile, the optimal scenario for conventional vehicles is scenario 5. This analysis shows that implementing electric vehicle scenarios is superior to conventional vehicle scenarios in terms of the total time required to evacuate all victims.

Selecting facility location of Gendarmerie Search and Rescue (GSR) Units; An analysis of efficiency in disaster response

Disasters, referred to as events that result in physical, economic, and social losses for individuals and disrupt the daily activities of human communities, necessitate ongoing preparedness due to their unpredictable nature. Swift response during and after a disaster is crucial for preserving human life. Hence, it is imperative to initiate planning immediately following a disaster to ensure readiness for various tasks. Given these factors, search and rescue units must carefully select a base location that enables them to promptly reach affected areas.Disasters exhibit unique characteristics across different regions of Türkiye. While some regions are prone to earthquakes, others face the risks of landslides, avalanches, or floods. Consequently, the required measures for disaster management vary from region to region. Nevertheless, when the term “disaster” is mentioned in Türkiye, earthquakes often come to mind due to their frequent occurrence and significant impact. The Gendarmerie Search and Rescue (GSR) units have been actively responding to these earthquakes, renowned for their exemplary institutional discipline and working methods. This study aims to examine the operations and deployment locations of GSR units, which play a crucial role in mitigating the impact of frequent earthquakes in Türkiye, utilizing a SWOT analysis. Additionally, a Multi-Criteria Decision Making-based mathematical model will be employed to optimize task activities and to select the most suitable facility locations for GSR units. The use of mathematical modeling in this context ensures that GSR units are strategically positioned to maximize their operational effectiveness and minimize response times. The results will be evaluated through sensitivity analysis.

Reshaping disaster management: An integrated community‐led approach

AbstractThe management of disasters has traditionally involved public, private, and nongovernmental organisations working together. While scholars have examined the value of collaborations among these entities, less is known about how to successfully engage and empower communities in disaster management. Based on network governance theory, this article contributes to the growing body of public management literature on community engagement by presenting findings from an Australian research initiative conducted after the 2019/20 Black Summer bushfires in New South Wales. Through workshops and semi‐structured interviews with a total of 58 members from local communities and emergency agencies, this paper identifies differing perspectives on power distribution among stakeholders, indicating complexities in achieving an integrated and community‐led disaster management approach. The findings underscore the need to shift from exclusively centralised to more inclusive systems, recognising the unique contributions of nonofficial community‐based groups. To address this, the study suggests: a funded community consultation committee, ensuring government and local community representation; collaborative debriefing sessions, leveraging technology for knowledge capture; and the adoption of different leadership styles able to identify, include, and integrate communities as both steerers and rowers within established hierarchical arrangements.Points for practitioners The current centralised emergency management system, which relies on recognised experts and state‐controlled facilities, limits the integration of nonofficial resources and community‐based knowledge. A shift towards a more community‐centric and integrated approach (collaborative polycentric governance) is needed to enhance disaster resilience and response in Australia. Different stages of disaster reduction could and should have different leadership styles: a transformational, collaborative community‐based style should be implemented before and after the disaster, while a transactional leadership style, more focused on restructuring the system or how it is applied, should be adopted during the disaster.

A tale of two disasters: Unpacking how social learning from the Ebola epidemic shaped COVID‐19 response in informal settlements in Freetown

AbstractPrior disaster experiences often provide lessons for communities to respond to new disasters. In informal communities prone to disasters but conditioned within reactive disaster management regimes, residents and Community‐Based Organizations (CBOs) play immense roles in disaster risk reduction and response. In Freetown, Sierra Leone, limited government disaster capacities during health disasters like Ebola and the COVID‐19 pandemic occasioned community action led by residents and CBOs. This paper, utilizing qualitative data, draws on the social learning theory to unpack how residents’ and CBOs’ experiences with the Ebola epidemic provided profound lessons that contributed to the COVID‐19 pandemic response in two informal communities. Findings highlight how prior disaster experiences consolidate memory and generate attention, retention, motivation, and reproduction of lessons for future disasters. The paper also fronts how lessons from peoples’ lived experiences with disasters can offer pluralistic ways of knowing about disasters and responding to future risks through co‐productive and equitable strategies.

Green Management for Disasters Using an Inflatable Tent Prototype with a Solar Power Plant

Given the ever-increasing number of natural disasters, disaster management must be understood and implemented by all parties in a manner that includes green management and technology. This research begins with the problem of disaster preparedness management which currently uses less environmentally friendly facilities and infrastructure, including using tents with conventional structures and technological systems that are less flexible and require electricity with a gasoline-fueled generator. The aim of this research is to create a green technology-based disaster response facility in the form of an inflatable tent prototype for disaster preparedness using solar electricity. The method used in this study is experimental, designing and manufacturing prototype inflatable tents that use solar power plants. Tests are carried out on several variables: strength of tarpaulins, speed in construction and disassembly, comfort of air temperature, and performance of solar power generation. The results are of the experimental method are: the design and application of two prototype inflatable tents dimensions 6x9 square meters with a capacity of 20 patients, a 3,600 WP power plant using solar power, and a VRLA battery with a voltage capacity of 48 volts and a current of 200 Ah that is capable storing 4,800 watts of electricity. Thus, these devices meet electricity needs for air conditioning, lighting, and medical equipment in inflatable tents without using electricity from the state electricity company or gasoline generators. Keywords: disaster, inflatable tent, solar power generation

Effectiveness of Generative AI for Post-Earthquake Damage Assessment

After an earthquake, rapid assessment of building damage is crucial for emergency response, reconstruction planning, and public safety. This study evaluates the performance of various Generative Artificial Intelligence (GAI) models in analyzing post-earthquake images to classify structural damage according to the EMS-98 scale, ranging from minor damage to total destruction. Correct classification rates for masonry buildings varied from 28.6% to 64.3%, with mean damage grade errors between 0.50 and 0.79, while for reinforced concrete buildings, rates ranged from 37.5% to 75.0%, with errors between 0.50 and 0.88. Fine-tuning these models could substantially improve accuracy. The practical implications are significant: integrating accurate GAI models into disaster response protocols can drastically reduce the time and resources required for damage assessment compared to traditional methods. This acceleration enables emergency services to make faster, data-driven decisions, optimize resource allocation, and potentially save lives. Furthermore, the widespread adoption of GAI models can enhance resilience planning by providing valuable data for future infrastructure improvements. The results of this work demonstrate the promise of GAI models for rapid, automated, and precise damage evaluation, underscoring their potential as invaluable tools for engineers, policymakers, and emergency responders in post-earthquake scenarios.

Business continuity management deployment, community resilience, and organisational resilience in private industry response to 2011 Ayutthaya flooding

AbstractThis research aims to investigate the role of community resilience in the relationship between business continuity management and organisational resilience, the focus on a real‐world event (flooding) and its impact on a community provides a concrete context that adds relevance to the study. To complete this research, a quantitative research design was used. A firm‐level survey of private industrial firms in Ayutthaya Province (n = 211) was conducted. Variables included three clusters of business continuity management practices, community resilience, and organisational resilience. The data were then analysed using descriptive statistics and structural equation modelling. The results showed that business continuity management practices contributed to organisational resilience. There were also interaction effects between community resilience and business continuity management practices, indicating that community resilience moderated the effect of business continuity management practices on organisational resilience following the floods. The study is the first to test the role of community resilience in the effectiveness of business continuity management practices on organisational resilience. This contributes to the research by providing a better explanation of how communities and organisations interact in organisational disaster response. The study only investigated a single context of disaster response (firms previously affected by the 2011 Ayutthaya flooding). The research focuses on industrial manufacturing and service firms and does not include retail firms. The research was conducted in historic perspective. Development of organisational resilience is affected by community resilience. Therefore, supporting community resilience through infrastructure, institutions, and other mechanisms is key for post‐disaster economic recovery. This provides a further reason for developing community resilience.

A Diverse Knowledge Perception and Fusion network for detecting targets and key parts in UAV images

Detecting targets and their key parts in UAV images is crucial for both military and civilian applications, including optimizing damage assessment, evaluating infrastructure, and facilitating disaster response efforts. Traditional top-down approaches impose excessive constraints that struggle to address challenges such as variable definitions and quantities of key parts, potential target occlusion, and model redundancy. Conversely, end-to-end approaches often overlook the relationships between targets and key parts, resulting in low detection accuracy. Inspired by the remarkable human reasoning process, we propose the Diverse Knowledge Perception and Fusion (DKPF) network, which skillfully balances the trade-offs between stringent constraints and unconstrained methods while ensuring both detection precision and real-time performance. Specifically, our model integrates reasoning guided by three distinct forms of knowledge: contextual knowledge at the image level in an unsupervised manner; explicit semantic knowledge regarding the interactions between targets and key parts at the instance level; and implicit comprehensive knowledge about the relationships among different types of targets or key parts, such as shape similarity. These specific knowledge forms are extracted through a novel adaptive fusion strategy for multi-scale features, a binary region-to-region semantic knowledge graph, and a data-driven self-attention architecture, respectively. Experiments conducted on both simulated and real-world datasets reveal that our method significantly outperforms state-of-the-art techniques, regardless of the number of key parts in the target. Furthermore, extensive ablation studies and visualization analyses validate both the efficacy of our approach and the interpretability of the generated features.