Types Of Disasters Research Articles

Flooding exposure accelerated biological aging: a population-based study in the UK

Floods have been the most common type of disaster and are expected to increase in frequency and intensity due to climate change. Although there is growing evidence on the impacts of floods on human health, none has so far investigated the association between flooding exposure and biological aging acceleration. We collected data from 364 841 participants from the UK Biobank project. Flooding data before baseline were retrieved from the Dartmouth Flood Observatory and linked to each participant. Cumulative flooding exposure within six years before the baseline was calculated. We calculated the two biological aging measures at baseline: PhenoAge and Klemera-Doubal method biological age (KDM-BA) and assessed their associations with flooding exposure using mixed-effects linear regression models. We observed that participants exposed to higher levels of floods were more likely to have accelerated biological aging. The risks associated with flooding exposure could last for several years, with the highest cumulative effect observed over 0–4 years. In the fully adjusted model, per interquartile increase in cumulative flood exposure was associated with an increase of 0.24 years (95% CI: 0.14, 0.34) in PhenoAge acceleration and 0.14 years (95% CI: 0.07, 0.21) in KDM-BA acceleration over lag 0–4 years. The associations were consistent regardless of lifestyles, demographics, and socio-economic status. Our findings suggest that exposure to floods may lead to accelerated biological aging. Our work provides the basis for further understanding of the flood-related health impacts and suggests that public health policies and adaptation measures should be initiated in the short-, medium- and even long-term after flooding.

A Multi-Model Approach for Disaster-Related Tweets

This research centers around utilizing Natural Language Processing (NLP) techniques to analyze disaster-related tweets. The rising impact of global temperature shifts, leading to irregular weather patterns and increased water levels, has amplified the susceptibility to natural disasters. NLP offers a method for quickly identifying tweets about disasters, extracting crucial information, and identifying the types, locations, intensities, and effects of each type of disaster. This study uses a range of machine learning and neural network models and does a thorough comparison analysis to determine the best effective method for catastrophe recognition. Three well-known techniques, in-cluding the Multinomial Naive Bayes Classifier, the Passive Aggressive Classi-fier, and BERT (Bidirectional Encoder Representations from Transformers) were carefully examined with the ultimate goal of discovering the best strategy for correctly recognising disasters within the context of tweets. Among the three models, BERT achieved the highest performance in analyzing disaster-related tweets with an accuracy of 94.75%.

Analisis Kesiapsiagaan Masyarakat dalam Menghadapi Bencana di Desa Cikunir, Tasikmalaya, Jawa Barat 2023

Disaster is an event or series of events that threaten and disrupt the lives and livelihoods of communities, caused by natural and/or non-natural factors as well as human factors, resulting in loss of human lives, environmental damage, property loss, and psychological impact. The aim of this research is to assess the readiness of the community in Cikunir Village, Tasikmalaya Regency, regarding disasters. This research uses a descriptive method with a quantitative approach. The findings conclude that the community in Cikunir Village is not prepared for the possibility of various disasters, whether natural or non-natural. Therefore, efforts should be made to enhance community preparedness. Recommendations for this research include increasing public awareness through more intensive educational campaigns, addressing various types of disasters, early warning signs, and potential impacts. Local government should ensure the availability of information through various channels, involve community leaders, and develop effective outreach strategies. Keywords: Preparedness, disaster, vulnerability

Environmental communication and disaster mitigation by mobile application and website

Background: Indonesia is a disaster-prone area, situated in the equatorial zone where several tectonic plates converge, and the Ring of Fire runs through. Indonesia requires an effective disaster mitigation system to minimize risks and losses during disasters. Meteorological, Climatological, and Geophysical Agency (BMKG) is a non-departmental government institution. Through its mobile application and official website, BMKG actively provides various information accessible to all Indonesians. Purpose: The research aimed to understand how BMKG conducts environmental communication and disaster mitigation through a mobile application and website. Methods: This research is descriptive qualitative using a constructive paradigm. The data was collected using documentation and observation studies. The authors carried out content analysis using Philipp Mayring’s methods on the “Info BMKG” application and the website http://www.bmkg.go.id/. Results: The BMKG application and website cover some aspects of environmental communication, including rhetoric, media, public participation, and education campaigns, delivering a range of environmental messages. They facilitate disaster mitigation by providing information and vulnerability maps for different types of disasters but lack details on regulatory measures and planning for disaster-prone areas. Conclusion: The BMKG application and website effectively communicate environmental and disaster-related information but need to improve coverage, speed of notifications, and include more details on regulation and planning for disaster-prone areas. Implications: This study suggests the need to enhance the platforms’ content and user education. Besides, it emphasizes theoretical implications for communication, public participation, and media theories in the context of disaster risk reduction.

Mining Spatial-Temporal Frequent Patterns of Natural Disasters in China Based on Textual Records

Natural disasters pose serious threats to human survival. With global warming, disaster chains related to extreme weather are becoming more common, making it increasingly urgent to understand the relationships between different types of natural disasters. However, there remains a lack of research on the frequent spatial-temporal intervals between different disaster events. In this study, we utilize textual records of natural disaster events to mine frequent spatial-temporal patterns of disasters in China. We first transform the discrete spatial-temporal disaster events into a graph structure. Due to the limit of computing power, we reduce the number of edges in the graph based on domain expertise. We then apply the GraMi frequent subgraph mining algorithm to the spatial-temporal disaster event graph, and the results reveal frequent spatial-temporal intervals between disasters and reflect the spatial-temporal changing pattern of disaster interactions. For example, the pattern of sandstorms happening after gales is mainly concentrated within 50 km and rarely happens at farther spatial distances, and the most common temporal interval is 1 day. The statistical results of this study provide data support for further understanding disaster association patterns and offer decision-making references for disaster prevention efforts.

Exploring and analyzing disaster coupling characteristics at the regional scale: evidence from Hubei Province

Disaster coupling can more easily lead to severe impacts. Existing studies on disasters have mostly focused on the regional distribution characteristics of individual disasters, neglecting the regional perspective of disaster coupling. This research paper addresses this gap by examining the coupling among three types of disasters (droughts, floods, and landslides) and their regional characteristics. To accurately represent the scope of these disasters, annual historical disaster data at the county level were utilized. The spatiotemporal distribution data of the three disasters were analyzed to determine the foundation of disaster coupling formation. Association rules were used to mine all possible disaster combinations in each county, which were then categorized into three disaster combination situations based on the types of different counties. Three types of disaster coupling were identified: chain, alternating, and compound, based on the formation process of disaster coupling. The disaster coupling existing in the disaster combinations was determined, and the characteristics of regional disaster coupling were further analyzed using quantitative and spatial distribution characteristics. In Hubei Province, chain-type disaster coupling primarily occurs in the western and eastern regions, while alternating-type disaster coupling may occur throughout the entire province, serving as the predominant type of disaster coupling in the central region. Furthermore, compound-type disaster coupling mainly occurs in western Hubei. It is not only related to independent disasters but also to single-type disaster coupling. This study reflects the characteristics of disaster coupling distribution in the region, making disaster prevention and control more targeted.

Memory-aware continual learning with multi-modal social media streams for unsupervised disaster classification

Social media has emerged as a major hub for information dissemination during disasters. It serves as a central repository for massive real-time updates about disasters, facilitating the organization of humanitarian assistance. Disaster types classification, which identifies and categorizes various types of disasters on social media, has become a fundamental technique for rapid disaster response. However, current disaster classification methods suffer from the following two shortcomings: (1) They rely on manual annotation to train the models, which is expensive and time-consuming in rapidly updating social media; (2) During the disaster, reports on social media are progressively enriched from initial text to multi-modal mix containing image, audio, and video. Existing methods cannot handle these incremental multi-modal data. To address these challenges, we propose a Memory-aware Continual Learning (MaCL) method, which accomplishes unsupervised disaster classification by flexibly handling the multi-modal streams on social media in a continual learning manner. First, a modal-shared contrastive encoder is constructed to mine the correlation information among the textual and visual modalities on social media. We theoretically demonstrate that mining correlation information is approximately equivalent to preserving predictive information in the original data. Then, we design a Correlation Memory Bank (CMB) to store the features of current modalities. As new modalities emerge, the correlation information between the new modality and its global neighbors in the CMB is maximized. This strategy enables the correlation information to guide the classification of the newly arrived modalities. Finally, the new correlation information generated by the CMB continuously updates and accumulates, leading to improve disaster classification performance across all modalities. Extensive experiments on seven real-world datasets validate the effectiveness of the proposed MaCL.

Discussions on developing a contingency plan for landslide-associated disasters in developing countries – Perspectives from Belo Horizonte municipality, Brazil

Contingency plan is a fundamental tool for preparedness and response to disasters; however, the research focuses on adopting high technologies or considering a well-structured government. Therefore, this research aims to present the development of a contingency plan for landslides based on the perspective of a municipality in a developing country. The research considers the perspective of Belo Horizonte municipality in Brazil, as it is a high-risk prone area for landslides with limited discussion on the topic. The research applied a multi-criteria method to analyze 18 approaches for developing contingency plans for landslides, applying in the Belo Horizonte municipality the one ranking in first place (meeting 87 % of the 15 requirements selected for analysis). The results illustrated relevant analysis to developing the contingency plan for landslides, the need to consider a broader range area for the propagation pathways of landslides, and that the response time is longer than the acceptable time interval. The findings are relevant for researchers and professionals working on the topic in Belo Horizonte municipality, other cities in Brazil, and potentially other developing countries. Future research could discuss adopting advanced technologies to address the topic in developing countries and developing contingency plans for different disaster types.

COMPREHENSIVE DISCUSSION OF THE REPAIRABLE SINGLE SERVER CATASTROPHE AND MULTIPLE VACATION QUEUEING MODEL

The queueing system with two types of vacation policies and disasters occurring in every state are examined in this study. As soon as the system is empty, the server goes on a short-term vacation. If there are no active clients in the system when the server returns from a short-term vacation, then the server goes on a long-term vacation. Upon seeing at least one client in the queue during a short or long-term vacation, the server will turn on right away. Catastrophes can happen during short or long vacations and also when the server is active. All current clients are removed from the system when a disaster occurs. When the system has been fixed, it starts to work properly once again. After repair, the server immediately switches to working mode(model 1) or takes a short-term vacation (model 2). A steady-state solution is determined by using the PGF (Probability-Generating Function). The above two categories of after-repair will be examined in this paper. Additionally, numerical examples related to performance measures, cost models, and the steady analysis of the provided models are discussed. Management for networking disaster recovery gives suggestions for restarting regular operations and network services following a disaster.

Site Selection for Offshore Wind Power Farms with Natural Disaster Risk Assessment: A Case Study of the Waters off Taiwan’s West Coast

This research examines the risk of natural disasters for offshore wind turbines together with their potential wind energy capacity to help the site selection of offshore wind power farms. Through evaluations of expert questionnaires, we use the fuzzy analytic hierarchy process to weight how natural disasters damage the sub-assemblies of an offshore wind turbine, then obtain the natural disaster risk assessment model, and finally utilize ArcGIS Pro 3.2 to map the potential wind farm sites for the waters off Taiwan’s west coast. We identify that typhoons are the most threatening type of disaster to generators, rotor blades, and rotor hubs; earthquakes are the most threatening to towers; and lightning is the most threatening to transformers. For the whole wind turbine, wind is ironically the most threatening natural disaster, followed by lightning, sea waves, and then earthquakes. Lastly, we examine the results by overlapping the offshore wind farms developed and planned in Taiwan, which coincide with locations in relatively low risk and high wind speed areas.

Investigating the Response Time to Disasters in the Red Crescent Society by Province and Type of Disasters: A Descriptive Study

Investigating the Response Time to Disasters in the Red Crescent Society by Province and Type of Disasters: A Descriptive Study

The need for a whole-of-community, victim-centred approach to mass victimisation incident planning and response

There have been a large number of masscasualty incidents in recent years, including climate change-related disasters, mass shootings, terrorist attacks, transportation accidents and a global pandemic. Communities, families and friends have suffered grief and loss, while nations continue to bear the scars of trauma. Disasters caused by acts with criminality, although necessarily managed by the police for the investigative aspect, must be planned for, and responded to with victim-centred practices by the police, local government and other relevant community stakeholders for the duration of the response and recovery. Inconsistency and confusion over terminology and language in emergency management can lead to a lack of understanding about which stakeholders or agencies should be engaged in, and responsible for different aspects of the planning, preparedness, mitigation and response to a community disaster — regardless of what type of disaster it is, and irrespective of the disaster being caused by a person or persons with ill-intent. This paper discusses how a wholeof- community and victim-centred approach to criminal act disaster response should be applied to support those persons most adversely affected by the incident. It also promotes the application of victim-centred practices to ensure that the needs of victims are regarded ethically, and with compassion following any disaster caused by an act of criminality. The term ‘mass victimisation incident’ will be introduced and applied through a case study.

Analysis Effect of Parameters of Genetic Algorithm on a Model for Optimization Design of Sustainable Supply Chain Network Under Disruption Risks

Abstract Over the last decade, our world exposed to many types of unpredictable disasters (recently Coronavirus). These disasters have clearly shown the uncertainty and vulnerability of supply chain systems. Also, it confirmed that adopting Just-in-Time (JIT) strategy to reduce the logistic chain cost may lead to inbuilt complexity and risks. Efficient tools are therefore needed to make complexity optimized supply chain decisions. Evolutionary algorithms, including genetic algorithms (GA), have proven effective in identifying optimal solutions that address the trade-offs between total supply chain cost and carbon emissions regulatory policy represented by carbon tax charges. These solutions pertain to the design challenges of supply networks exposed to potential disruption risks. However, GA have a set of parameters must be chosen for effective and robust performance of the algorithms. This paper aims to set the most suitable values of these parameters that used via GA – ased optimization cost and risk reduction model in firms using a JIT as a delivery system. The model has been conceptualized for addressing the design complexities of the supply chain, referred to as SCRRJITS (Simultaneous Cost and Risk Reduction in a Just-in-Time System). A complete analysis of the different parameters and operators of the algorithm is carried out using design of experiments approach. The algorithm performance measure used in this study is convergence of solutions. The results show the extent to which the quality of solution can be changed depending on selection of these parameters.

Medical needs during the Kumamoto heavy rain 2020: analysis from emergency medical teams’ responses

BackgroundRainfall-induced floods represented 70% of the disasters in Japan from 1985 to 2018 and caused various health problems. To improve preparedness and preventive measures, more information is needed on the health problems caused by heavy rain. However, it has proven challenging to collect health data surrounding disasters due to various inhibiting factors such as environmental hazards and logistical constraints. In response to the Kumamoto Heavy Rain 2020, Emergency Medical Teams (EMTs) used J-SPEED (Japan-Surveillance in Post Extreme Emergencies and Disasters) as a daily reporting tool, collecting patient data and sending it to an EMTCC (EMT Coordination Cell) during the response. We performed a descriptive epidemiological analysis using J-SPEED data to better understand the health problems arising from the Kumamoto Heavy Rain 2020 in Japan.MethodsDuring the Kumamoto Heavy Rain 2020 from July 5 to July 31, 2020, 79 EMTs used the J-SPEED form to submit daily reports to the EMTCC on the number and types of health problems they treated. We analyzed the 207 daily reports, categorizing the data by age, gender, and time period.ResultsAmong the 816 reported consultations, women accounted for 51% and men accounted for 49%. The majority of patients were elderly (62.1%), followed by adults (32.8%), and children (5%). The most common health issues included treatment interruption (12.4%), hypertension (12.0%), wounds (10.8%), minor trauma (9.6%), and disaster-related stress symptoms (7.4%). Consultations followed six phases during the disaster response, with the highest occurrence during the hyperacute and acute phases. Directly disaster-related events comprised 13.9% of consultations, indirectly related events comprised 52.0%, and unrelated events comprised 34.0%. As the response phases progressed, the proportions of directly and indirectly related events decreased while that of unrelated events increased.ConclusionBy harnessing data captured by J-SPEED, this research demonstrates the feasibility of collecting, quantifying, and analyzing data using a uniform format. Comparison of the present findings with those of two previous analyses of J-SPEED data from other disaster scenarios that varied in time, location, and/or disaster type showcases the potential to use analysis of past experiences to advancing knowledge on disaster medicine and disaster public health.

Toward Resilience City: Potential Hazards and Scenario for Ternate Island, North Maluku

Natural hazards and climate change significantly threaten almost every small island. This is due to the small island's isolation and lack of resources. Ternate Island is a small island that is now afflicted by this issue. Geographically, the island is surrounded by active plates and volcanic paths, making it at risk of serious catastrophes. Population growth and limited land resources also contribute to a high disaster susceptibility. We characterize the features of the small island of Ternate by classifying the types of disasters that threaten it based on a literature database. Earthquakes, volcanic eruptions, floods, extreme weather events, and tsunamis are natural disasters that occur often in Ternate. Additionally, these findings indicate the significant importance of a multi-hazard approach to disaster response. Our examination concludes with some recommendations for establishing the resilience of Ternate City.

Evaluation of Disaster Perception Levels of University Students: A Sample Application Study

Disasters are important events that are difficult to predict and occur in various ways, often with high destructive power. Earthquakes, floods, droughts, major industrial accidents, famine, technological accidents, epidemics, etc. are only a few of the major types of disas-ters that mankind has been exposed to from past to present. In recent years, natural and unnatural disaster events have been observed both in Turkey (Düzce-earthquake, 1999; Giresun-Dereli-flood-flood, 2020; Kahramanmaraş-Pazarcık-earthquake, 2023) and in many other parts of the world (Sumatra-tsunami, 2004; Ecuador-earthquake, 2016; USA-hurricane-storm, 2023). The impact of disasters is often not limited to one region but can spread over a wide area. As a matter of fact, the impact of this destruction can be reduced at some point thanks to various measures that can be taken before the disaster. For exam-ple, identification of regional risks, early warning systems, crisis management, emergency action plans, etc. can be considered within the scope of these measures. Another important issue is to increase the level of disaster awareness of the society. Especially in regions where disaster risk maps have been prepared, such awareness raising activities can be carried out to make the reaction of the society against disasters more controlled and conscious. The aforementioned issues constitute the main objective of this study. This study was conducted to evaluate the disaster awareness levels of Gümüşhane Univer-sity central campus students. The data obtained in the study were analyzed using SPPS 25 statistical program. In the study, a 5-point Likert scale was used to measure disaster risk perception. In the study, frequency analysis test, Kruskal Wallis test and Mann Whitney U test were performed. As a result of the analysis, it was analyzed at 5% level of significance (p<0.05) and it is stated in the tables as arithmetic mean, standard deviation and percent-age of the number of individuals. Accordingly, as a result of the non-parametric Mann Whitney-U test, no significant difference was found between the groups. As a result of the non-parametric Mann Whitney-U test conducted to determine whether the disaster risk perception sub-dimension scores of the students differed significantly according to the gender variable, a statistically significant difference was found between the groups at the p<0.05 level in favor of the group exposed to disaster.

Optimization-driven artificial intelligence-enhanced municipal waste classification system for disaster waste management

This research addresses the critical challenge of disaster waste management, a growing concern exacerbated by the increasing frequency and intensity of natural disasters like flooding. Traditional waste systems often struggle with the volume and heterogeneity of disaster waste, highlighting the need for innovative solutions. In this study, we present a novel disaster waste classification model integrating advanced artificial intelligence (AI) and optimization techniques to streamline waste categorization in post-disaster environments. Our approach leverages a dual ensemble deep learning framework. The first ensemble combines various image-segmentation methods, while the second integrates outputs from diverse convolutional neural network architectures. A modified artificial multiple intelligence system serves as a decision fusion strategy, enhancing accuracy at both ensemble points. We rigorously evaluated our model using three datasets: the “TrashNet” dataset for benchmarking against existing methods, as well as two meticulously curated, real-world datasets collected from flood-affected areas in Thailand. The results demonstrate that our method outperforms existing algorithms like VGG19, YoloV5, and InceptionV3 in general solid waste classification, achieving an average improvement of 11.18%. Regarding disaster waste specifically, our model achieves 96.48% and 96.49% accuracy on the curated datasets, consistently outperforming ResNet-101, DenseNet-121, and InceptionV3 by an average of 3.47%. These findings demonstrate the potential of our AI-enhanced model to revolutionize disaster waste management practices. Thus, we advocate integrating such technologies into municipal waste management policies to enhance resilience and optimize disaster responses. Future research will explore scaling the model to diverse disaster types and incorporating real-time data for adaptable waste management strategies.

Perceptions of Pre-service Teachers about Natural Disasters: Coronavirus, Earthquake, Fire, and Flood

This research explores the perceptions of preservice teachers in Turkey in terms of their preparedness for various disasters, including epidemics, earthquakes, fires, and floods, all of which have profound social and economic impacts on education. Using a descriptive survey model, the study examined the responses of 512 preservice teachers at a single university across all levels of their educational program. The survey, conducted digitally due to the pandemic, consisted of 70 questions. Findings reveal inconsistent levels of confidence and readiness across different types of disasters. For instance, while most preservice teachers understand the severity of the coronavirus and are concerned about the long-term effects of protective measures, the majority admit to a lack of preparedness for earthquakes. While they lack practical experience with fires, they are conversant with various fire safety measures. Their understanding of and preparedness for floods, however, were limited. The study highlights the critical need for comprehensive disaster education and readiness training for preservice teachers, suggesting the urgent need for reliable, scientifically backed information.

Capturing Extraordinary Multisensory Experiences in Writing: Reports on Natural Disasters in an 18th Century Newspaper Corpus

The article examines reports of natural disasters in the 18th century Austrian newspaper "Wienerisches Diarium" to gain insights into how people captured the extraordinary sensory experiences of such events in written form. By analysing a digitised corpus of over 300 newspaper issues, the study identifies 302 text passages referring to natural disasters, among them 285 news reports, and explores textual traces of (multi)sensuality present within this material. The close reading and semantic annotation of the textual findings reveals that comparisons to familiar sensations are commonly used to convey the sensory experiences of natural disasters, allowing readers to (better) relate to and understand the extreme events. At the same time, touch and vision constitute the most frequently mentioned senses, while smell and taste only play a minor role in early modern disaster depiction. In addition, the study finds that earthquakes and volcanic eruptions are more likely to include multisensory descriptions compared to other types of disasters. These and further findings shed light on when and how (multi)sensory impressions of disasters were conveyed in written form within early modern news.

A research on the relationship between landslide area changes and environmental factors in the southern Tibetan plateau

Introduction: Landslides are known to be one of the most frequent types of geological disasters. However, there is not an established method for large-scale, rapid, and high-precision landslide extraction. The quantitative impact of environmental changes on landslide development is also not well understood, which hinders accurate assessments and decision-making in environmental and disaster response. The polar regions, including the Antarctic, the Arctic, and the Tibetan plateau (TP), sensitive to global environmental changes, are significantly affected by global warming. This leads to extensive landslide development, particularly in the southern TP. This research focuses on new landslides in the southern TP, exploring extraction methods and the relationship between landslides and environmental factors.Methods: Utilizing the Google Earth Engine (GEE) and an improved Otsu threshold segmentation algorithm, we processed remote sensing images with 10 m resolution to identify landslide areas. The proposed Normalized Landslide Bare-soil Separation Index (NDLBSI) achieved an 87% pre-extraction accuracy in extracting landslides from Sentinel-2 images from 2019 to 2023. For the pre-extraction results, manual interpretation and correction were carried out, and a model correlating annual landslide changes with environmental factors was established based on least squares multivariate statistical methods.Results: Results show that a significant increase in landslide areas in the southern TP over the past 5 years, correlating with the watershed-wide increase in annual average temperature and vegetation cover, along with a decrease in snow cover area.Discussion: These changes could affect soil and rock moisture, influencing soil stability and landslide occurrence. The study provides valuable insights for large-scale landslide detection and understanding the environmental factors influencing landslides, which is of some significance for landslide hazards early warning.