Showing papers on "Natural disaster published in 2023"

What do we know about consumers’ ontological security in disaster scenarios?

Abstract: While prior consumer studies have adopted various theoretical perspectives to explain individuals' reactions to disasters, scant attention has been paid to the role of ontological security in shaping those responses. This study attempts to fill this knowledge gap by qualitatively exploring ontological security in two contexts: man-made and natural disasters. To this end, we conducted 35 focus groups in the UK, Germany, and France to address how people reacted to terrorist attacks and the COVID-19 pandemic respectively. Through thematic analysis, three themes emerged: fear versus anxiety, oneself versus others, and materialistic versus experiential purchases. Man-made disasters appear to elicit fear, concern for self, and a preference for materialistic purchases, whereas natural disasters seem to trigger anxiety, concern for others, and a preference for experiential purchases. Both types of disasters seem to evoke a desire to escape from reality. In closing, we discuss both transitory and prolonged threats to ontological security and how they shape individuals' behaviours while restoring their security.

CEO’s Childhood Experience of Natural Disaster and CSR Activities

Abstract: Interest in the drivers of firms' corporate social responsibility (CSR) is growing. However, little is known about the influence of a CEO's childhood experience of natural disasters on CSR. Using archival data, we explore this relationship by offering three mechanisms that may account for how the CEO's childhood experience of natural disaster is related to their CSR. More specifically, while prior research has established a positive relationship based on the post-traumatic growth theory, we show that the dual mechanisms of prosocial values and a CEO's risk aversion explain the positive relationship. We further find that the positive relationship is stronger (1) when CEOs have longer career horizons and (2) when community social capital is high. This study contributes to both research and managerial implications on the topics of CEO's childhood experience and CSR. In particular, this study advances the upper echelon theory by revealing that a CEO's childhood experience of natural disaster is a useful yet relatively underexplored variable that can help explain the substantial variations in firms' CSR. Moreover, we emphasize that a CEO's career horizons and level of community social capital are important variables that further amplify the effect of a CEO's childhood experience of natural disaster on the firm's CSR commitment.

Community Perspectives on Simulation and Data Needs for the Study of Natural Hazard Impacts and Recovery

Abstract: With the aim of fostering the development of robust tools to simulate the impact of natural hazards on structures, lifelines, and communities, the Natural Hazards Engineering Research Infrastructure Computational Modeling and Simulation Center gathered 60 researchers, developers, and practitioners working in natural hazards engineering (NHE) for a workshop to prioritize research questions and identify community needs for data and computational simulation capabilities. Participants used their wide-ranging expertise in earthquake, coastal, and wind hazards from engineering, planning, data sciences, and social sciences perspectives to identify five major thrusts of recommended future work, including detailed suggestions for each: (1) development of housing and household recovery models; (2) integration of existing models into flexible computational workflows; (3) investment in the collection of high-value open data; (4) commitment to sharing and utilizing high-value data; and (5) development of versatile, multidisciplinary testbed studies. Participant responses and workshop data were analyzed with the help of an ontology that the authors designed to support data classification in a broad range of NHE applications. The paper also includes observations and suggestions for planning and conducting interactive workshops of this type.

Factors affecting the intention to prepare for tsunami in Thailand

Abstract: The threat brought by natural disasters such as tsunamis is evident since the major chaotic event happened in 2004. Related studies have dealt with the mapping and routes for efficient evacuation but limited works of literature considered the intention to prepare for tsunamis. This study aimed to analyze different factors affecting the intention to prepare for tsunamis through the integration of the Theory of Planned Behavior and Protection Motivation Theory. A total of 736 valid responses collected via convenience sampling answered a self-administered cross-sectional online survey to measure the behavioral aspects holistically. Through the use of structural equation modeling, factors such as understanding tsunami affecting perceived severity (PS) and perceived vulnerability (PV) were seen to be the most significant relationship. In addition, attitude on intention to prepare showed a significant direct relationship, which led to an understanding of tsunami having an indirect effect on intention to prepare through PS, PV, and attitude. It could be deduced that when people understand the impact, severity, vulnerability, and aftermath brought by tsunamis, individuals would have a high positive relationship to attitude to prepare for tsunamis. This study presented practical and government implications for promoting mitigation and preparedness among people for tsunami. Moreover, the findings of this study may be applied to other countries that may be affected by tsunamis. This study is considered the first study that measured intention to prepare for tsunami holistically. Lastly, this study contributes to people's intention towards preparation for natural disasters which can be applied and extended for other calamities worldwide.

Rapid identification of damaged buildings using incremental learning with transferred data from historical natural disaster cases

Abstract: The accurate extraction of building damage after destructive natural disasters is critical for disaster rescue and assessment. To achieve a rapid disaster response, training a model from scratch using enough ground-truth data collected in situ is not feasible. Often, in disaster situations, it is ineffective to directly apply an existing model due to the vast diversity among buildings worldwide, the limited number of label samples for training, and the different sources of remote sensing images between the pre- and post-disaster. To solve this problem, we present an incremental learning framework for the rapid identification of collapsed buildings triggered by sudden natural disasters. Specifically, end-to-end gradient boosting networks are improved into an incremental learning framework for an emergency response, where the historical natural disaster data are transferred into the same style of images that were captured shortly after a disaster event by using cycle-consistent generative adversarial networks. The proposed method is tested on two cases, i.e., the Haiti earthquake in January 2010 and the Nepal earthquake in April 2015, achieving Kappa accuracies of 0.70 and 0.68, respectively. The optimization of building damage extraction can be completed within 8 h after the disaster using the transferred data. The experimental results show that the proposed method is an effective way to evaluate the building damage triggered by natural disasters with different source remote sensing images. The code of this work and the data of the test cases are available at https://github.com/gjy-Ari/Incre-Trans.

The impact of climate-related disasters on children’s communication and wellbeing: Addressing Sustainable Development Goals

Abstract: Abstract Purpose Natural disasters can significantly impact children’s health, development, and wellbeing, as well as their access to education and support services (including speech-language pathology). Children’s needs are often overlooked in the urgent aftermath of natural disasters. This is especially true for children with communication difficulties. This commentary explores the impacts of bushfire on Australian children, to propose a sustainable, community-based approach to supporting children’s health, wellbeing, and communication. Result The Royal Far West Bushfire Recovery Program, a multidisciplinary allied health program, supported children's recovery, resilience, and development in the aftermath of Australia’s Black Summer bushfires in 2019–2020. Children learnt coping strategies and were more able to communicate with adults and peers about their feelings and experiences, but residual impacts of bushfires remained for some children. Allied health telepractice services, including speech-language pathology, enhanced access for vulnerable children, highlighting the potential for technology to provide high-quality services to support recovery, particularly in remote areas. Conclusion Climate change increases the frequency and severity of bushfires and other natural disasters with significant consequences for vulnerable and at-risk communities. Children with communication needs are particularly vulnerable during and following these disasters. High quality, evidence-based interventions are needed to support the health, wellbeing, and communication needs of children, with opportunities for involvement of speech-language pathologists. This commentary paper focusses on SDG 1, SDG 3, SDG 4, SDG 9, SDG 10, SDG 11, SDG 13, and SDG 15.

The health burden of natural and technological disasters in Brazil from 2013 to 2021.

Abstract: Disasters deeply impact the health of the affected population and the economy of a country. The health burden of disasters in Brazil is underestimated and more studies are needed to underpin policies and actions for disaster risk reduction. This study analyzes and describes disasters that occurred in Brazil from 2013 to 2021. The Integrated Disaster Information System (S2iD) was accessed to obtain demographic data, disaster data according to Brazilian Classification and Codification of Disasters (COBRADE), and health outcome data (number of dead, injured, sick, unsheltered, displaced, and missing individuals and other outcomes). Database preparation and analysis were performed in Tableau. In total, 98.62% (50,481) of the disasters registered in Brazil from 2013 to 2021 are natural, with a significant increase in 2020 and 2021 due to the COVID-19 pandemic, a biological disaster. This disaster group also caused the highest number of deaths (321,111), as well as injured (208,720) and sick (7,041,099) people. By analyzing data for each geographic region, we observed differences regarding disasters frequency and their health outcomes. In Brazil, climatological disasters are the most frequent (23,452 events) and occur mainly in the Northeast region. Geological disasters have the highest lethality, which are more common in the Southeast; however, the most common disasters in the South and Southeast are those of the meteorological and hydrological groups. Therefore, since the greatest health outcomes are associated with disasters predicted in time and space, public policies for the prevention and management of disasters can reduce the impacts of these events.

Disasters collide at the intersection of extreme weather and infectious diseases.

Abstract: Natural disasters interact to affect the resilience and prosperity of communities and disproportionately affect low income families and communities of colour. However, due to lack of a common theoretical framework, these are rarely quantified. Observing severe weather events (e.g. hurricanes and tornadoes) and epidemics (e.g. COVID-19) unfolding in southeastern US communities led us to conjecture that interactions among catastrophic disturbances might be much more considerable than previously recognized. For instance, hurricane evacuations increase human aggregation, a factor that affects the transmission of acute infections like SARS-CoV-2. Similarly, weather damage to health infrastructure can reduce a community's ability to provide services to people who are ill. As globalization and human population and movement continue to increase and weather events are becoming more intense, such complex interactions are expected to magnify and significantly impact environmental and human health.

Enhancing FAIR Data Services in Agricultural Disaster: A Review

Abstract: The agriculture sector is highly vulnerable to natural disasters and climate change, leading to severe impacts on food security, economic stability, and rural livelihoods. The use of geospatial information and technology has been recognized as a valuable tool to help farmers reduce the adverse impacts of natural disasters on agriculture. Remote sensing and GIS are gaining traction as ways to improve agricultural disaster response due to recent advancements in spatial resolution, accessibility, and affordability. This paper presents a comprehensive overview of the FAIR agricultural disaster services. It holistically introduces the current status, case studies, technologies, and challenges, and it provides a big picture of exploring geospatial applications for agricultural disaster “from farm to space”. The review begins with an overview of the governments and organizations worldwide. We present the major international and national initiatives relevant to the agricultural disaster context. The second part of this review illustrates recent research on remote sensing-based agricultural disaster monitoring, with a special focus on drought and flood events. Traditional, integrative, and machine learning-based methods are highlighted in this section. We then examine the role of spatial data infrastructure and research on agricultural disaster services and systems. The generic lifecycle of agricultural disasters is briefly introduced. Eventually, we discuss the grand challenges and emerging opportunities that range from analysis-ready data to decision-ready services, providing guidance on the foreseeable future.

Flood or Non-Flooded: A Comparative Study of State-of-the-Art Models for Flood Image Classification Using the FloodNet Dataset with Uncertainty Offset Analysis

Abstract: Natural disasters, such as floods, can cause significant damage to both the environment and human life. Rapid and accurate identification of affected areas is crucial for effective disaster response and recovery efforts. In this paper, we aimed to evaluate the performance of state-of-the-art (SOTA) computer vision models for flood image classification, by utilizing a semi-supervised learning approach on a dataset named FloodNet. To achieve this, we trained son 11 state-of-the-art (SOTA) models and modified them to suit the classification task at hand. Furthermore, we also introduced a technique of varying the uncertainty offset λ in the models to analyze its impact on the performance. The models were evaluated using standard classification metrics such as Loss, Accuracy, F1 Score, Precision, Recall, and ROC-AUC. The results of this study provide a quantitative comparison of the performance of different CNN architectures for flood image classification, as well as the impact of different uncertainty offset λ. These findings can aid in the development of more accurate and efficient disaster response and recovery systems, which could help in minimizing the impact of natural disasters.

Identifying obstacles encountered at different stages of the disaster management cycle (DMC) and its implications for rural flooding in Pakistan

Abstract: The world has seen a number of natural hazards, but among them, floods are perhaps the most frequent devastating natural hazard, resulting in more human causalities and financial losses. Rural inundation has become an issue of concern in various parts of the world, including Pakistan. Over the past few decades, it has been hard for local institutions and rural populations to recover from the trauma inflicted by these events. The disaster risk management cycle is a well-known tool for coping with disasters and their consequences. Yet, the DRM cycle efficacy has been questioned in various rural settings. Thus, this paper applied a programmatic strategy to understand the challenges disaster management authorities and communities face in managing flood risks through the conventional disaster management cycle in Khyber Pakhtunkhwa province, Pakistan. The study objective was accomplished by using both qualitative and exploratory research designs. Four communities (namely, Peshawar, Charsadda, Nowshera, and Dera Ismail Khan) with a historical record of flooding were chosen for focus group discussion (32 in total) using a purposive sampling method. Additionally, 31 key informant interviews were undertaken from pertinent local disaster risk management institutions. We employed a thematic analysis to classify responses and obstacles into the various stages of the disaster management cycle. The findings of this study from interviews and focus groups provided some new insight into the conventional DRM cycle. The issues and challenges encountered by institutions and the community members were divided into four stages: 1-mitigation, 2-preparedness, 3-rescue and relief (R&R), and 4-rehabilitation and recovery (R&R). Based on the findings, it seems that local disaster management institutions still rely on reactive strategies and deal with flood hazards on an ad hoc basis. Poor governance and a lack of responses for present development trajectories were also highlighted as reasons why flood risk management is still challenging. There is an urgent need to perform susceptibility and risk assessments for multiple hazards and develop specialized plans that follow disaster risk reduction principles and adaptation to climate change. This study recommends incorporating resilience and adaptation to climate change into the current disaster management cycle to prevent or reduce future hazards and risks in rural areas.

Migration Responses to Earthquakes: Evidence from Italy

Abstract: Abstract In this paper, we analyze the migration responses to natural disasters by focusing on the three most devastating earthquakes in Italy in recent decades: L’Aquila 2009, Emilia Romagna 2012, and Central Italy 2016. Using municipality-level data for 2002–2019 and adopting a new difference-in-difference approach with multiple periods and multiple groups, we evaluate the causal effect of these events on internal and international inbound and outbound migration of both Italian and foreign citizens. The results suggest that, despite the massive destruction, there is no evidence that these earthquakes significantly impacted the migration of Italian citizens. We only found evidence of the effect of the earthquake in L’Aquila on the short-distance migration of foreign citizens.

Social Vulnerability Evaluation of Natural Disasters and Its Spatiotemporal Evolution in Zhejiang Province, China

Abstract: Natural disasters present a significant challenge to the productivity of Zhejiang Province. This paper is the first to evaluate social vulnerability to natural disasters in Zhejiang Province and provides a scientific foundation for disaster prevention, mitigation, and risk management. In this paper, we construct an indicator system for evaluating social vulnerability of natural disasters in Zhejiang Province through demand analysis, frequency analysis, and applicability analysis. The methodology employed in this paper reduces errors arising from subjective indicator selection and provides a reference for future international research on evaluating social vulnerability to natural disasters. This study analyzes the spatiotemporal evolution of social vulnerability to natural disasters in 11 cities from 2011 to 2020. The results indicate an overall downward trend of social vulnerability to natural disasters in Zhejiang. Social vulnerability to natural disasters exhibits significant spatial variability. The evaluation can help to bridge the knowledge gap regarding the social vulnerability of Zhejiang Province to natural disasters. The analysis of the spatiotemporal evolution of social vulnerability provides insights into the contributing factors to vulnerability and the effectiveness of past disaster management strategies. The findings of this study can serve as a valuable reference for future research in Zhejiang Province and other regions facing similar challenges. The results can contribute to the advancement of comprehensive knowledge of social vulnerability to natural disasters, which can inform the development of policies and strategies aimed at mitigating disaster risk and promoting effective disaster management globally.

An Overview of Emergency Communication Networks

Abstract: In recent years, major natural disasters and public safety accidents have frequently occurred worldwide. In order to deal with various disasters and accidents using rapidly deployable, reliable, efficient, and stable emergency communication networks, all countries in the world are strengthening and improving emergency communication network construction and related technology research. Motivated by these situations, in this paper, we provide a state-of-the-art survey of the current situation and development of emergency communication networks. In this detailed investigation, our primary focus is the extensive discussion of emergency communication network technology, including satellite networks, ad hoc networks, cellular networks, and wireless private networks. Then, we explore and analyze the networks currently applied in emergency rescue, such as the 370M narrowband private network, broadband cluster network, and 5G constellation plan. We propose a broadband-narrowband integrated emergency communication network to provide an effective solution for visual dispatch of emergency rescue services. The main findings derived from the comprehensive survey on the emergency communication network are then summarized, and possible research challenges are noted. Lastly, we complete this survey by shedding new light on future directions for the emergency communication network. In the future, the emergency network will develop in the direction of intelligence, integration, popularization, and lower cost, and space-air-ground-sea integrated networks. This survey provides a reference basis for the construction of networks to mitigate major natural disasters and public safety accidents.

Jenks Natural Breaks Classification을 이용한 전국 격자기반 재해영향도 산정 방안 연구

Abstract: This paper proposes a disaster risk assessment system for the natural disaster with Jenks Natural Breaks Classification. The exposure indicators and vulnerability indicators are organized to disaster risk factors for the each receptors. The receptors evaluated the risk assessment are 9 and composed of people, industry, public facilities, educational and research facilities, medical and welfare facilities, amenity facilities, agriculture, livestock industry, and roads. All indicators are composed of 107,555 grid-based data having the codes of the region in the country. With Jenks natural breaks classification, the disaster risk assessment criteria for each receptors were presented per the disaster risk grade (Level 1 ~ Level 4). All of the criteria were evaluated with the grid-base data per the receptors and the evaluated results were presented by the maps of Korea. Through this study, the disaster risk assessment criteria can be used as the reference and forecasting for the natural disaster in Korea.

A Smart Framework for Managing Natural Disasters Based on the IoT and ML

Abstract: Natural disasters greatly threaten our lives in addition to adversely affecting all activities. Unfortunately, most solutions currently used in flood management are suffering from many drawbacks related to latency and accuracy. Moreover, the previous solutions consider that the whole city has the same level of vulnerability to damage, while each area in the city may have different topologies and conditions. This study presents a new framework that collects data in real-time about bad weather, which may cause floods, where the framework has a proposed classification algorithm to process sensed data to determine the level of danger in each area of the city. In case of a threat, the framework will send early alerts to users and rescue teams. The framework depends on the Internet of Things (IoT) and fog computing coupled with multiple models of machine learning (Rain Forest, Decision Tree, K-Nearest Neighbor, Support Vector Machine, Logistic Regression, and Deep Learning) to enhance performance and reliability. In addition, the research suggests some assistant services. To prove the efficiency of the framework, we applied the proposed algorithm to real data for the city of Jeddah, Saudi Arabia, for the years 2009 to 2013 and for the years 2018 to 2022. Then, we depended on standard metrics (accuracy, precision, recall, F1-score, and ROC curve). The Rain Forest and Decision Tree achieved the highest accuracy, exceeding 99 percent, followed by the K-Nearest Neighbor. The framework will provide flood detection systems that can predict floods early, send a multi-level warning, and reduce financial, human, and infrastructural damage.

Local, regional, and global adaptations to a compound pandemic-weather stress event

Abstract: Global food security can be threatened by short-term extreme events that negatively impact food production, food purchasing power, and agricultural economic activity. At the same time, environmental pollutants like greenhouse gases (GHGs) can be reduced due to the same short-term extreme stressors. Stress events include pandemics like COVID-19 and widespread droughts like those experienced in 2015. Here we consider the question: what if COVID-19 had co-occurred with a 2015-like drought year? Using a coupled biophysical-economic modeling framework, we evaluate how this compound stress would alter both agricultural sector GHG emissions and change the number of undernourished people worldwide. We further consider three interdependent adaptation options: local water use for crop production, regional shifts in cropland area, and global trade of agricultural products. We find that GHG emissions decline due to reduced economic activity in the agricultural sector, but this is paired with large increases in undernourished populations in developing nations. Local and regional adaptations that make use of natural resources enable global-scale reductions in impacted populations via increased global trade.