Showing papers on "Natural disaster published in 2022"

Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review

Abstract: Natural disasters cause enormous damage and losses every year, both economic and in terms of human lives. It is essential to develop systems to predict disasters and to generate and disseminate timely warnings. Recently, technologies such as the Internet of Things solutions have been integrated into alert systems to provide an effective method to gather environmental data and produce alerts. This work reviews the literature regarding Internet of Things solutions in the field of Early Warning for different natural disasters: floods, earthquakes, tsunamis, and landslides. The aim of the paper is to describe the adopted IoT architectures, define the constraints and the requirements of an Early Warning system, and systematically determine which are the most used solutions in the four use cases examined. This review also highlights the main gaps in literature and provides suggestions to satisfy the requirements for each use case based on the articles and solutions reviewed, particularly stressing the advantages of integrating a Fog/Edge layer in the developed IoT architectures.

Factors affecting perceived effectiveness of Typhoon Vamco (Ulysses) flood disaster response among Filipinos in Luzon, Philippines: An integration of protection motivation theory and extended theory of planned behavior

Abstract: Typhoon Vamco (Ulysses) is considered one of the most devastating typhoons in the Philippines in 2020. It caused fatalities, property destructions, and catastrophic flooding in Metro Manila and the nearby provinces. The purpose of this study was to assess the factors affecting the perceived effectiveness of the 2020 typhoon Vamco (Ulysses) flood disaster response among Filipinos by integrating the Protection Motivation Theory (PMT) and extended Theory of Planned Behavior (TPB). 567 Filipinos answered an online survey questionnaire with 75 questions (63 indicators and 11 latent variables). Structural equation modeling (SEM) showed that geographical perspective (GL) and typhoon – flood experience and knowledge (TPE) had significant effects on Perceived Severity (PS) and Perceived Vulnerability (PV), which subsequently led to Perceived Behavioral Control (PBC), Subjective Norms (SN), and Attitude (ATB). PBC, SN, and ATB were also found to have significant effects on Intention to Follow, which subsequently led to Behavior and Perceived Effectiveness from the 2020 Typhoon Vamco (Ulysses) flood disaster risk response. Interestingly, government support (GR) was insignificant to an effective flood disaster response, and 52.38% of respondents perceived that the typhoon had more severe effects than the flood after the typhoon. This study is the first study that analyzed the perceived effectiveness of disaster response towards 2020 Typhoon Vamco. The findings will be very beneficial for academicians and policymakers as it provides a robust model and results for experts to analyze natural disasters to develop optimum disaster risk responses in mitigating the severe effects of typhoon floods. Finally, the SEM construct can be broadened and adapted to flood disaster response effectiveness applicable in other natural disaster-prone countries.

Determination of factors affecting the response efficacy of Filipinos under Typhoon Conson 2021 (Jolina): An extended protection motivation theory approach

Abstract: The response efficacy measures can be utilized to assess a person's beliefs as to whether the recommended action step will actually minimize the impact of a natural disaster such as a typhoon. This study examines the response efficacy of Filipinos under Typhoon Conson 2021 (Jolina) using the Extended Protection Motivation Theory (PMT) approach. To accurately measure the factors and their relationships to response efficacy, an online questionnaire was developed and distributed using a convenience sampling method to 388 Filipinos a few days before the typhoon hit the Philippines. Several latent variables in PMT such as understanding of typhoon, perceived severity, response cost, self-efficacy, and response efficacy together with some additional latent variables such as typhoon experience, geographical perspective, and perceived susceptibility were analyzed simultaneously. Structural Equation Modeling (SEM) showed that perceived severity of the typhoon and self-efficacy were the key factors affecting the response efficacy of Filipinos in preparing for typhoon Jolina. Moreover, it was also found that understanding typhoons, self-efficacy, perceived susceptibility, and past typhoon experience indirectly affected response efficacy. The results of this study could be utilized by future researchers and planners of natural disasters to find ways of enhancing the response efficacy in preparing for typhoons. Finally, the findings of this study can also be utilized as a theoretical framework for government worldwide in designing and implementing strategies and policies for natural disaster risk protection.

Remote Sensing Methods for Flood Prediction: A Review

Abstract: Floods are a major cause of loss of lives, destruction of infrastructure, and massive damage to a country’s economy. Floods, being natural disasters, cannot be prevented completely; therefore, precautionary measures must be taken by the government, concerned organizations such as the United Nations Office for Disaster Risk Reduction and Office for the coordination of Human Affairs, and the community to control its disastrous effects. To minimize hazards and to provide an emergency response at the time of natural calamity, various measures must be taken by the disaster management authorities before the flood incident. This involves the use of the latest cutting-edge technologies which predict the occurrence of disaster as early as possible such that proper response strategies can be adopted before the disaster. Floods are uncertain depending on several climatic and environmental factors, and therefore are difficult to predict. Hence, improvement in the adoption of the latest technology to move towards automated disaster prediction and forecasting is a must. This study reviews the adoption of remote sensing methods for predicting floods and thus focuses on the pre-disaster phase of the disaster management process for the past 20 years. A classification framework is presented which classifies the remote sensing technologies being used for flood prediction into three types, which are: multispectral, radar, and light detection and ranging (LIDAR). Further categorization is performed based on the method used for data analysis. The technologies are examined based on their relevance to flood prediction, flood risk assessment, and hazard analysis. Some gaps and limitations present in each of the reviewed technologies have been identified. A flood prediction and extent mapping model are then proposed to overcome the current gaps. The compiled results demonstrate the state of each technology’s practice and usage in flood prediction.

Global climate change and human health: pathways and possible solutions

Abstract: Global warming has been changing the planet’s climate pattern, leading to increasing frequency, intensity and duration of extreme weather events and natural disasters. These climate-changing events affect various health outcomes adversely through complicated pathways. This paper reviews the main signs of climate change so far, e.g., suboptimal ambient temperature, sea-level rise and other conditions, and depicts the interactive pathways between different climate-changing events such as suboptimal temperature, wildfires, and floods with a broad range of health outcomes. Meanwhile, the modifying effect of socioeconomic, demographic and environmental factors on the pathways is summarised, such that the youth, elderly, females, poor and those living in coastal regions are particularly susceptible to climate change. Although Earth as a whole is expected to suffer from climate change, this review article discusses some potential benefits for certain regions, e.g., a more liveable environment and sufficient food supply. Finally, we summarise certain mitigation and adaptation strategies against climate change and how these strategies may benefit human health in other ways. This review article provides a comprehensive and concise introduction of the pathways between climate change and human health and possible solutions, which may map directions for future research.

Automated Disaster Monitoring From Social Media Posts Using AI-Based Location Intelligence and Sentiment Analysis

Abstract: Worldwide disasters like bushfires, earthquakes, floods, cyclones, and heatwaves have affected the lives of social media users in an unprecedented manner. They are constantly posting their level of negativity over the disaster situations at their location of interest. Understanding location-oriented sentiments about disaster situation is of prime importance for political leaders, and strategic decision-makers. To this end, we present a new fully automated algorithm based on artificial intelligence (AI) and natural language processing (NLP), for extraction of location-oriented public sentiments on global disaster situation. We designed the proposed system to obtain exhaustive knowledge and insights on social media feeds related to disaster in 110 languages through AI- and NLP-based sentiment analysis, named entity recognition (NER), anomaly detection, regression, and Getis Ord Gi* algorithms. We deployed and tested this algorithm on live Twitter feeds from 28 September to 6 October 2021. Tweets with 67 515 entities in 39 different languages were processed during this period. Our novel algorithm extracted 9727 location entities with greater than 70% confidence from live Twitter feed and displayed the locations of possible disasters with disaster intelligence. The rates of average precision, recall, and F₁-Score were measured to be 0.93, 0.88, and 0.90, respectively. Overall, the fully automated disaster monitoring solution demonstrated 97% accuracy. To the best of our knowledge, this study is the first to report location intelligence with NER, sentiment analysis, regression and anomaly detection on social media messages related to disasters and has covered the largest set of languages.

Multinational enterprises and natural disasters: Challenges and opportunities for IB research

Abstract: The purpose of this paper is to encourage and to extend research on natural disasters and international business (IB). More specifically, we review the characteristics of natural disasters and the unique challenges they pose to the business environment and examine how they differ from other types of disasters/crises often researched in the IB literature. Next, we investigate the applicability and challenges of core IB theories to the study of natural disasters. By extending new internalization theory (NIT) to overcome challenges of bounded rationality and reliability, we identify effective strategies for managing the threat of natural disasters through establishing multi-sector partnerships and alternative supply chains. Integrating research on the characteristics of natural disasters and the insights from NIT, we propose natural disaster management strategies for multinational enterprises (MNEs) based on varying degrees of geographic scope of natural disasters and MNEs. This paper concludes with proposing new research opportunities for IB scholars in disaster preparedness, cross-organizational collaborations, and supply chain management.

Flood risk management in the Yangtze River basin —Comparison of 1998 and 2020 events

Abstract: China is a country that is significantly affected by and sensitive to global climate change. Floods are one of the major natural disasters in China, and they occur with high frequency and wide impact in the country, causing serious losses. Since the 1990s, they have become more frequent. China has made remarkable achievements in flood risk management, but the problems and challenges of this in the context of climate change and urbanization are still serious and require in-depth analysis and targeted adaptations. During the summer of 2020, southern China suffered from catastrophic flooding; however, the losses from this flooding were much lower than those of previous major floods. Herein, the flood disasters of the Yangtze River Basin in China in 1998 and 2020 are compared and analyzed from atmospheric, hydrological, socioeconomic, and disaster-loss perspectives and the reasons behind the observed differences are examined and discussed. The findings indicate that risk-management capabilities, such as engineering defense capabilities, environmental recovery capabilities, forecasting and early-warning capabilities, and emergency response capabilities, have achieved remarkable results. The results show that disaster loss has been largely reduced because of China's achievements in disaster risk reduction measures. The problems and challenges faced by China's flood risk management are analyzed, and detailed watershed comprehensive flood risk management recommendations are put forward to reduce the losses caused by flooding.

Detecting Natural Hazard-Related Disaster Impacts with Social Media Analytics: The Case of Australian States and Territories

Abstract: Natural hazard-related disasters are disruptive events with significant impact on people, communities, buildings, infrastructure, animals, agriculture, and environmental assets. The exponentially increasing anthropogenic activities on the planet have aggregated the climate change and consequently increased the frequency and severity of these natural hazard-related disasters, and consequential damages in cities. The digital technological advancements, such as monitoring systems based on fusion of sensors and machine learning, in early detection, warning and disaster response systems are being implemented as part of the disaster management practice in many countries and presented useful results. Along with these promising technologies, crowdsourced social media disaster big data analytics has also started to be utilized. This study aims to form an understanding of how social media analytics can be utilized to assist government authorities in estimating the damages linked to natural hazard-related disaster impacts on urban centers in the age of climate change. To this end, this study analyzes crowdsourced disaster big data from Twitter users in the testbed case study of Australian states and territories. The methodological approach of this study employs the social media analytics method and conducts sentiment and content analyses of location-based Twitter messages (n = 131,673) from Australia. The study informs authorities on an innovative way to analyze the geographic distribution, occurrence frequency of various disasters and their damages based on the geo-tweets analysis.

Determining factors affecting preparedness beliefs among Filipinos on Taal volcano eruption in Luzon, Philippines

Abstract: Volcanic eruption is a known natural disaster threat in the Philippines, and lack of disaster preparedness may lead to immense property damages and human casualties. This study aimed to determine the relevant factors affecting Filipinos’ preparedness beliefs in the risk of the Taal volcano eruption by integrating the Protection Motivation Theory and the extended Theory of Planned Behavior. A total of 653 individuals living in nearby urban and rural areas around the Taal volcano answered the self-administered questionnaire with 61 questions (61 indicators and 11 latent variables). Using structural equation modeling (SEM), results showed that Perceived risk proximity, Media, and Hazard knowledge had substantial effects on Perceived severity and Perceived vulnerability. Perceived severity and Perceived vulnerability consequently have positive direct impact on Perceived behavioral control, Risk avoidance norms, and Attitude toward the behavior, which were found to have a direct significance to Intention to evacuate, Preparedness behaviors and Preparedness beliefs in the threat of Taal volcano eruption. The results will contribute to researchers and policymakers in developing disaster mitigation plans to minimize the effects of volcano-related disasters and build community resilience to natural disasters. Furthermore, the SEM constructs can be extended and modified to analyze the preparedness in other third world countries prone to natural disasters.

Seismic resilience assessment of urban interdependent lifeline networks

Abstract: With increasing interdependence of various lifeline networks, natural disasters, especially seismic disasters damage will further degrade overall network performance. Hence, based on the concept of risk, reliability and vulnerability, some scholars assessed the resilience of multi-layer lifeline networks. However few studies considered bidirectional interdependence of lifelines, which would immensely exacerbates the consequences of seismic disaster. And the existing research of resilience assessment generally assumed that lifeline networks can recover to their initial performance rather than limited recovery which is more practical. Therefore, this study presents a framework and method for assessing the resilience of lifeline networks. And a performance response function for interdependent lifelines is proposed based on its functional characteristics. Then, we use an IEEE 30-bus and Belgium 20-node gas interdependent network as examples to verify the effectiveness of the framework. The results showed that, for each network, the recovery resource and budget had various effects on the resilience. And our framework can provide a quick reference for optimal decision-making in various scenarios under seismic disasters.

Multinational enterprises and natural disasters: Challenges and opportunities for IB research

Abstract: The purpose of this paper is to encourage and to extend research on natural disasters and international business (IB). More specifically, we review the characteristics of natural disasters and the unique challenges they pose to the business environment and examine how they differ from other types of disasters/crises often researched in the IB literature. Next, we investigate the applicability and challenges of core IB theories to the study of natural disasters. By extending new internalization theory (NIT) to overcome challenges of bounded rationality and reliability, we identify effective strategies for managing the threat of natural disasters through establishing multi-sector partnerships and alternative supply chains. Integrating research on the characteristics of natural disasters and the insights from NIT, we propose natural disaster management strategies for multinational enterprises (MNEs) based on varying degrees of geographic scope of natural disasters and MNEs. This paper concludes with proposing new research opportunities for IB scholars in disaster preparedness, cross-organizational collaborations, and supply chain management.

Business recovery from disasters: Lessons from natural hazards and the COVID-19 pandemic

Abstract: This paper compares economic recovery in the COVID-19 pandemic with other types of disasters, at the scale of businesses. As countries around the world struggle to emerge from the pandemic, studies of business impact and recovery have proliferated; however, pandemic research is often undertaken without the benefit of insights from long-standing research on past large-scale disruptive events, such as floods, storms, and earthquakes. This paper builds synergies between established knowledge on business recovery in disasters and emerging insights from the COVID-19 pandemic. It first proposes a disaster event taxonomy that allows the pandemic to be compared with natural hazard events from the perspective of economic disruption. The paper then identifies five key lessons on business recovery from disasters and compares them to empirical findings from the COVID-19 pandemic. For synthesis, a conceptual framework on business recovery is developed to support policy-makers to anticipate business recovery needs in economically disruptive events, including disasters. Findings from the pandemic largely resonate with those from disasters. Recovery tends to be more difficult for small businesses, those vulnerable to supply chain problems, those facing disrupted markets, and locally-oriented businesses in heavily impacted neighborhoods. Disaster assistance that is fast and less restrictive provides more effective support for business recovery. Some differences emerge, however: substantial business disruption in the pandemic derived from changes in demand due to regulatory measures as well as consumer behaviour; businesses in high-income neighborhoods and central business districts were especially affected; and traditional forms of financial assistance may need to be reconsidered.

Forced displacement in history: Some recent research

Abstract: Forced displacement as a consequence of wars, civil conflicts, or natural disasters does not only have con-temporaneous consequences but also long ‐ run reper-cussions. This eclectic overview summarises some recent research on forced displacement in economic history. While many of the episodes covered refer to Europe, this survey points to literature across all continents. It highlights new developments, and points to gaps in the literature.

Post-disaster recovery for family firms: The role of owner motivations, firm resources, and dynamic capabilities

Abstract: Natural disasters (e.g., earthquakes and pandemics) negatively affect firms and their stakeholders. These disasters disrupt the operations of firms and lives of people by generating a shock in the system. Small firms are especially vulnerable to the shocks and disturbances resulting from these disasters. Since small firms, especially family firms, are key economic contributors and agents of recovery in any community, understanding their post-disaster recovery processes is critical. Therefore, this study examines the post-disaster recovery processes of small family firms. We utilize a grounded theory approach to analyze and propose that resources and socioemotional wealth priorities influence the post-disaster recovery of small family firms. Utilizing the 8.8 Richter scale earthquake in Chile in 2010 as a natural disaster, we examine the eight-year lagged data of 20 small family firms with disrupted operations. Our findings have important implications for small firms experiencing the negative consequences of disruptions, including those experiencing the COVID-19 pandemic-induced disruption.

Exploring the Effects of Natural Capital Depletion and Natural Disasters on Happiness and Human Wellbeing: A Study in China

Abstract: Since recent climate change has caused more natural disasters (NDs) than ever before, there is a worldwide concern that this could have both short-term and long-term economic and health consequences. This is perhaps the first attempt to explore the effects of natural capital (NC) and NDs on the human health and wellbeing of China over the period 1993–2020. The study has compiled data from World Bank, World Value Survey, UNDP, EM-DAT, and IMF for analysis. The empirical analysis is done by using the autoregressive distributed lag model. Empirical results prove that NC has a positive and significant effect on happiness, health, and human wellbeing in the long run. The results also show that NDs significantly reduce happiness and human wellbeing in the long run. The results recommend some important policy implications.