Natural Disaster Risk Research Articles

Exploring medical students’ perceptions and understanding of the health impacts of climate change: a qualitative content analysis

BackgroundClimate change has been identified as the greatest threat to global health in the twenty-first century, with its unfavorable health consequences being among its impacts on humans. Exploring the perspectives and understanding of healthcare professionals and service providers concerning climate change becomes imperative. The aim of this study is to investigate the perceptions and understanding of final-year medical students regarding the health impacts of climate change on individuals and the healthcare system using a qualitative content analysis.MethodsThis study employed a qualitative content analysis approach. Face-to-face interviews were conducted with the aid of an interview guide to explore the students' awareness, understanding, and attitudes towards the impacts of climate change on public health and the healthcare system. The collected interview data were subsequently organized into codes, categories, and subcategories based on the students' perspectives and attitudes towards climate change.ResultsFifteen medical intern students were interviewed for this study, and the qualitative findings were categorized into 3 categories, 23 subcategories, and 229 codes. The study's findings revealed various health impacts of climate change, which were classified into three main categories, including environmental effects with 8 subcategories, socio-economic effects with 8 subcategories, and health effects with 7 subcategories. The study's findings revealed medical students' perceptions of various health impacts of climate change and These findings suggest that medical student understand that climate change has significant impacts on individuals' health and society, mainly through environmental degradation, increased risks, and climate-related disasters, which ultimately lead to adverse health outcomes.ConclusionsThe perspectives of medical students in this study indicate that climate change may not have a direct and immediate impact on the health of individuals and communities. However, it can significantly influence their health and socio-economic well-being by exacerbating or causing environmental problems, increasing the risk of weather-related events and natural disasters, ultimately leading to adverse health outcomes. While the medical students' perspectives on the health impacts of climate change are indeed broad, incorporating scientific knowledge about this topic into the medical curriculum and educating students on how to deal with patients affected by these consequences can have a significant impact on health management. This proactive approach, despite the students' already comprehensive understanding, can enhance their preparedness to address the health effects of climate change and contribute to strengthening the healthcare system's resilience in the face of climate-related challenges.

A Multi-Hazard Risk Assessment Framework for Urban Disaster Prevention Planning: A Case Study of Xiamen, China

Understanding and measuring the relative risk level of a city facing multi-hazards is fundamental to improving its disaster prevention planning and schemes. A comprehensive risk evaluation approach stands at the intersection of risk management and disaster system theory. It is also an important interdisciplinary field of catastrophology, economics, and urban infrastructure planning. We believe that current attempts to define and measure comprehensive urban natural disaster risks have certain limitations. Therefore, we propose an Urban Multi-hazards Risk Assessment Framework (UMRAF) which draws on definitions, methods, and experience from risk management, evaluation of property, the value of statistical life, and disaster system theory. It contains local disaster identification, place-based risk assessment (taking into account more than one hazard at a time), urban anti-disaster capability assessment, and relative composite risk index measurement. In our case study of Xiamen, China, to check the feasibility of our UMRAF, we examined local multi-hazards risk distribution and urban anti-disaster capacity layout. We then expanded and visualised the spatial distribution of the relative composite risk index of each evaluation unit across the city via our analyst tool, thereby helping to tailor measures that can reduce risk at a local level.

The status of risk perception and behavioral factors related to reducing the risk of natural disasters in the south of Kerman province

The status of risk perception and behavioral factors related to reducing the risk of natural disasters in the south of Kerman province

Application of AHP-ICM and AHP-EWM in Collapse Disaster Risk Mapping in Huinan County

Collapses are one of the most common geological disasters in mountainous areas, which easily damage buildings and infrastructures and bring huge property losses to people’s production and life. This paper uses Huinan County as the study area, and with the help of a geographic information system (GIS) based on the formation principle of natural disaster risk, the information content method (ICM), the analytical hierarchy process (AHP), and the analytical hierarchy process–information content method (AHP-ICM) model are applied to hazard mapping, and the analytical hierarchy process-entropy weight method (AHP-EWM) model is applied to exposure, vulnerability and emergency responses, and recovery capability mapping. A risk mapping model for collapse disasters was also constructed using these four elements. Firstly, an inventory map of 52 landslides was compiled using remote sensing interpretation, field verification, and comprehensive previous survey data. Then, the study area mapping units were delineated using the curvature watershed method in the slope unit, and 21 indicators were used to draw the collapse disaster risk zoning map by considering the four elements of geological disaster risk. The prediction accuracy of the three hazard mapping models was verified using the receiver operating characteristic (ROC) curve, and the area under the curve (AUC) results of the AHP, ICM, and AHP-ICM models were 80%, 85.7%, and 87.4%, respectively. After a comprehensive comparison, the AHP-ICM model is the best of the three models in terms of collapse hazard mapping, and it was applied to collapse risk mapping with the AHP-EWM model to produce a reasonable and reliable collapse risk zoning map, which provides a basis for collapse management and decision making.

Remote sensing as a tool for natural disaster risk analysis: a bibliometric review

Introduction: remote sensing as a tool is a fundamental resource that helps to develop various applications for disaster risk management.Objective: to conduct a bibliometric analysis of remote sensing as a tool for natural disaster risk analysis in Scopus between 2003 and 2023.Methods: this was a bibliometric analysis that defined the metrics of scientific production. The indicators were obtained from 409 documents chosen from the Scopus database using keywords in English (Remote sensing, natural disaster).Results: the data revealed a significant increase in the number of papers published on the topic (59,9 %) between 2019 and 2023. In addition, China (22,5 %; n=133) recorded the highest rate of scientific output, with the Chinese Academy of Sciences publishing the most (n=36). The journal Remote Sensing published 30 papers, while Natural Hazards received 720 citations, the most referenced author being Pradhan, B. (n=11 papers; 1587 citations).Conclusions: studies on this topic have increased in number, subject variation and author cooperation, as remote sensing allows data to be collected from inaccessible or hazardous regions, enabling real-time monitoring and tracking of natural phenomena.

Introduction of emergency equipment for floods, fires and riots in Augmented Reality

Indonesia is one of the countries with a high risk of natural disasters in several regions. Emergencies can result in death or property destruction. While dealing with catastrophes, swift action and anticipation are required; yet, if the community is unfamiliar with emergency equipment, it will take longer to overcome emergencies. It turns out that many people still do not understand emergency equipment information correctly. The active engagement of the community in recognizing emergency equipment can be enhanced by the provision of information. The implementation of an application utilizing Augmented Reality technology and the Fisher Yates Shuffle algorithm aims to enhance public awareness and promote active engagement in identifying emergency equipment. Once the application has been developed and constructed, the evaluation process will involve the utilization of a pre-test, post-test, and the N-Gain Score methodology. The N-Gain Score is a valuable tool for gauging the extent of the community's comprehension in identifying emergency equipment. Additionally, a Questionnaire will be employed to evaluate the level of user satisfaction with the application. The findings of the study revealed that the level of knowledge acquired by users, as assessed, was 57%. This suggests that users demonstrate a relatively high level of proficiency in understanding emergency equipment when utilizing the application. Furthermore, the overall satisfaction of users with the program received a final score of 86%, showing a high degree of effectiveness.

The Impact of Natural Disaster Risks on Financial Technology in Jordanian Commercial Banks from the Viewpoint of their Employees

The Impact of Natural Disaster Risks on Financial Technology in Jordanian Commercial Banks from the Viewpoint of their Employees

Analysis of Disaster Risk on Expressways for Foreign Tourists to Japan Using Rental Cars: Study Using Central Nippon Expressway Pass

The travel patterns of foreign visitors to Japan are changing from group travel, which was the mainstream to date, to individual travel, and there has been an increase in the number of cars rented by visitors as a means of transportation. The use of rental cars improves access to rural areas and is being promoted as a means of transportation. However, if a natural disaster occurs while driving a rental car, then the individual may face difficulties owing to a lack of local support. Therefore, the traffic volume on the roads used by foreign visitors to Japan during certain periods needs to be estimated in advance as well as the natural disaster risks that exist on those roads. In this study, we utilized CEP data managed by the Central Nippon Expressway Company in Japan to identify the risk of disaster for foreign visitors to Japan who use expressways with rental cars. The CEP data has usage records of foreign visitors to Japan who used rental cars on expressways in the Chubu region of Japan during the period from 1 April 2018 to 31 March 2019. Using this data, we calculated the risk of earthquake and landslide disasters. Result of this analysis, it is clear that the risk of earthquake disasters is high on expressways in the Tokai region, and the risk of landslide disasters is high on expressways in mountainous areas.

Building Resilience for Local Governments in Albania Legal and Institutional Challenges

Uncertainty from natural hazards and disaster risks is high in Albania. Located in the western part of the Balkan, Albania has faced over the years several disaster events and the future, especially in the light of climate change, does not hold any positive news. Multiple hazards are present over the territory and extend beyond the administrative boundaries, revealing the need for an integrated local - to national – to regional approach to resilience building, as a response to uncertainties. The following paper analyses the role and challenges of the local governments, from and institutional perspective, for enabling local resilience. Local resilience has social, institutional, governance, economic, ecological and territorial dimensions. This paper addresses local ecological and territorial resilience.

Assessment of Natural Disasters Impact on Cultural Mayan Heritage Spaces in Remotes Villages of Guatemala: Case of Black Salt

In the town of Sacapulas located in the mountainous country of Guatemala, there is a constant risk of natural disasters. Floods and landslides occur frequently, resulting in the loss of human lives and cultural aspects. Specifically, in the region, the creation of black salt is the most affected. This resource has been created since the time of the Mayans on the salt beach surrounding the town. However, from the 1940s onwards, this industry has shrunk, impacting the sustainability of indigenous people. After conducting several area and space analyses, it was found that the black salt beach has evolved considerably since the last research conducted in 2001. The shape of the space has been reduced, while the use of the area has been modified by the people of the town, who specifically use the hot springs located below the river shore of the beach. This new usage can coexist with the Salt making industry is only made by a few people now, there are few working in this industry, and they only work in the dry season. The result is an opportunity for economic growth and an increase in tourism if the area handled properly by managing the land and planning ahead.

Optimizing vaccine distribution in developing countries under natural disaster risk

AbstractFor many developing countries, COVID‐19 vaccination roll‐out programs are not only slow but vaccination centers are also exposed to the risk of natural disaster, like flooding, which may slow down vaccination progress even further. Policy‐makers in developing countries therefore seek to implement strategies that hedge against distribution risk in order for vaccination campaigns to run smoothly and without delays. We propose a stochastic‐dynamic facility location model that allows policy‐makers to choose vaccination facilities while accounting for possible facility failure. The model is a multi‐stage stochastic variant of the classic facility location problem where disruption risk is modelled as a binary multivariate random process–a problem class that has not yet been studied in the extant literature. To solve the problem, we propose a novel approximate dynamic programming algorithm which trains the shadow price of opening a flood‐prone facility on historical data, thereby alleviating the need to fit a stochastic model. We trained the model using rainfall data provided by the local government of several major cities in the Philippines which are exposed to multiple flooding events per year. Numerical results demonstrate that the solution approach yields approximately 30%–40% lower cost than a baseline approach that does not consider the risk of flooding. Recommendations based on this model were implemented following a collaboration with two large cities in the Philippines which are exposed to multiple flooding events per year.

Spatiotemporal Variability of Hot Days in Association with the Large-Scale Atmospheric Drivers over Vietnam

The severe heatwaves and hot spells in Vietnam were observed more frequently in intensity and duration due to global warming and climate change impacts. The hot days and extreme summer events make the weather harsh and significantly affect human health and the environment. This study presents the spatiotemporal distribution of the number of hot days (NHDs) in Vietnam. The variability of NHD in seven climate subregions is also examined in association with the large-scale drivers. The European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis version 5 (ERA5) data for the period 1981–2020 are used. Principal component analysis is also applied to the observed monthly NHD to obtain spatial patterns and time series. The results show that the NHD in the Highland and South subregions from March to May is linked with the subtropical high associated with decreased 500hPa-level vertical velocity (VV500). From May to June, the North and Central subregions seem related to deepening the Asiatic low and enhancement of southwest flows across the Indochina Peninsula. Finally, both increased southwest flows and decreased VV500 can partly contribute to the intensification of NHD in the North and Central subregions during July and August. The long trends of NHD are also examined. The results reveal that the increasing trends in NHD occur in most subregions, except for the Central Highland, and changing trends of NHD in June greatly contribute to the annual trend of NHD. Finally, the examinations with the El Niño-Southern Oscillation events show that NHD is significantly higher in El Niño events than in La Niña events in March and April for the Northwest, South Central, Central Highlands, and South, in May and June for all subregions, and in July and August for only the Red River Delta subregion. This suggests that ENSO can provide the potential for improving seasonal climate forecasts and mitigating natural disaster risks for the community.

Connection number structure-based information diffusion model for agricultural drought disaster risk assessment: A case study in Jianghuai watershed area

The information diffusion model is widely used in natural disaster risk assessment because of its operationalization, low data requirements and clear significance of the evaluation results. However, the commonly used normal information diffusion model is based on an average distance assumption, and the diffusion widow with h is directly affected by sample size and extreme values, especially when the sample size is more than 11, which may lead to unstable results of normal diffusion, with typical uncertainty. Considering the influence of the whole samples on the diffusion points, incorporating the connection number method, a connection number structure-based information diffusion model was proposed, and also a relative connection number method is proposed to calculate the connection number, which was applied to the risk assessment of drought disasters in Jianghuai watershed area. The results show that the connection number structure-based information diffusion model reduces the direct influence of sample extremes and sample size, eliminates the constraint of the average distance assumption in the traditional model, and performs more consistently with increasing sample size for risk assessment. And the relative connection number method can contain the traditional connection number expression and offers a wider application. A study of drought risk in the Jianghuai watershed shows that the indices of agriculture drought-suffered, drought-affected and drought-damaged fluctuated greatly, but the trends were generally consistent with each other from 1990 to 2020. It is more likely to suffer from drought in the area, but the disaster rate after drought tends to decrease over time. With the increase in drought intensity, the disaster risk will decrease significantly compared to the drought risk. The connection number structure-based information diffusion model has no two-point proximity principle and the average distance assumption, with better stability in the disaster risk assessment for different samples size compared with the traditional model, which will have more extensive application in risk assessment.

A Lightweight and Partitioned CNN Algorithm for Multi-Landslide Detection in Remote Sensing Images

Landslide detection is crucial for natural disaster risk management. Deep-learning-based object-detection algorithms have been shown to be effective in landslide studies. However, advanced algorithms currently used for landslide detection require high computational complexity and memory requirements, limiting their practical applicability. In this study, we developed a high-resolution dataset for landslide-prone regions in China by extracting historical landslide remote sensing images from the Google Earth platform. We propose a lightweight LP-YOLO algorithm based on YOLOv5, with a more-lightweight backbone that incorporates our designed PartitionNet and neck equipped with CSPCrossStage. We constructed and added the vertical and horizontal (VH) block to the backbone, which explores and aggregates long-range information with two directions, while consuming a small amount of computational cost. A new feature fusion structure is proposed to boost information flow and enhance the location accuracy. To speed up the model learning process and improve the accuracy, the SCYLLA-IoU (SIoU) bounding box regression loss function was used to replace the complete IoU (CIoU) loss function. The experimental results demonstrated that our proposed model achieved the highest detection performance (53.7% of Precision, 49% of AP50 and 25.5% of AP50:95) with a speed of 74 fps. Compared to the YOLOv5 model, the proposed model achieved 4% improvement for Precision, 2.6% improvement for AP50, and 2.5% for AP50:95, while reducing the model parameters and FLOPs by 38.4% and 53.1%, respectively. The results indicated that the proposed lightweight method provides a technical guidance for achieving reliable and real-time automatic landslide detection and can be used for disaster prevention and mitigation.

Geographic-Information-System-Based Risk Assessment of Flooding in Changchun Urban Rail Transit System

The frequent occurrence of urban flooding in recent years has resulted in significant damage to ground-level infrastructure and poses a substantial threat to the metro system. As the central city’s core transportation network for public transit, this threat can have unpredictable consequences on travel convenience and public safety. Therefore, assessing the risk of urban flooding in the metro system is of utmost importance. This study is the first of its kind to employ comprehensive natural disaster risk assessment theory, establishing an assessment database with 22 indicators. We propose a GIS-based method combined with the analytical hierarchy process (AHP) and an improved entropy weight method to comprehensively evaluate the urban flood risk in Changchun City’s metro systems in China. This study includes a total of nine metro lines, including those that are currently operational as well as those that are in the planning and construction phases, situated in six urban areas of Changchun City. In this study, we utilize the regional risk level within the 500 m buffer zone of the metro lines to represent the flood risk of the metro system. The proposed method assesses the flood risk of Changchun’s rail transit system. The results reveal that over 30% of Changchun’s metro lines are located in high-risk flood areas, mainly concentrated in the densely populated and economically prosperous western part of the central city. To validate the risk assessment, we vectorized the inundation points and overlaid them with the regional flood risk assessment results, achieving a model accuracy of over 90%. As no large-scale flood events have occurred in the Changchun rail transit system, we employed receiver operating characteristic (ROC) curves to verify the accuracy of the flood risk assessment model, resulting in an accuracy rate of 91%. These findings indicate that the present study is highly reliable and can provide decision makers with a scientific basis for mitigating future flood disasters.

Application of moderation in determining natural disaster mitigation strategies as inclusive education in elementary school

Natural disaster mitigation learning is inclusive education in elementary school. There for, this aims to express the responses and opinions of teachers about the implementation of the disaster mitigation learning model and to improve the ability of teachers in elementary schools. The study was conducted on three schools consist elementary schools in Mataram, Ampenan, and Sembalun in Lombok Island-West Nusa Tenggara-Indonesia. The method of research begins with a presentation of a learning model for natural disaster mitigation such as peer teaching by the research team. After that discussion with moderation technique regarding disaster mitigation learning structure applied in the previous presentation. The results of the study obtained 5 main ideas of strategies about the implementation of the natural disaster mitigation learning model, namely that 1) students must be conditioned to natural disasters in the learning process, 2) teachers must have the correct steps in providing the understanding of natural disasters and in conducting direct demonstrations so that students thoroughly understand, 3) teachers must have knowledge of earthquakes, tsunamis, landslides, floods, hurricanes and volcanoes, 4) teachers provide examples of disaster mitigation learning to students using tools such as videos, disaster mitigation tools, etc, 5) understanding risk, vulnerability, threat and capability of natural disaster. In general conclusion, teachers have to ability to use technology pedagogical and content knowledge strategies for natural disaster mitigation learning.

Detecting Natural Disaster Risk Areas and Measuring Disaster Preparedness for the Elderly as the Population Declines and Ages

Detecting Natural Disaster Risk Areas and Measuring Disaster Preparedness for the Elderly as the Population Declines and Ages

Disaster risk analysis of technological failure of industrial estate: a case study

The world has agreed on reducing disaster risks through Sendai Framework for Disaster Risk Reduction (SFDRR) 2015–2030. Efforts to reduce disaster risks are one way to attain Sustainable Development Goals related to "sustainable cities and communities." The first points of disaster risk reduction priorities inscribed in the SFDRR 2015–2030 incorporate disaster risk studies. While studies on natural disaster risks have been widely conducted, non-natural (manmade) disaster risk studies are relatively scant, parti­cularly for technological failure disasters. In this paper, the author investigates the levels of technological failure disaster risks in Gresik Regency, Indonesia, one of the National Strategic Areas in East Java Province. This study employs a disaster risk analysis encompassing aspects of hazard and vulnerability through map overlays with the help of a Geo­graphical Information System (GIS) to identify areas with risks of techno­logical failure. Results illustrate that a high risk is predominantly spread in areas with high hazards, which is 60 m radius of the industrial area. The findings in this study may help shed light on the hazards that may arise due to technological failures that span not only around the source of hazard, i.e., the industrial areas, but also beyond them, and also conclude that the higher the disaster risk is, the higher the vulnerability of an area will be.

Distribution Center Location Selection in Humanitarian Logistics Using Hybrid BWM–ARAS: A Case Study in Türkiye

Abstract This study investigates the criteria affecting the location of humanitarian logistics distribution centers in the Sakarya province of Turkey, an area prone to natural disasters. The study identifies potential distribution center locations and uses the Best-Worst Method (BWM) to determine criteria such as population, distance to major highways and airports, public transportation availability, natural disaster risk, and suitable infrastructure. BWM is used to assign weights to each criterion and rank them based on their importance. The Additive Ratio Assessment (ARAS) method is then used to evaluate potential distribution center locations based on the established criteria. Disaster management experts and academicians provide their opinions through an online and face-to-face survey. Based on the results, Adapazarı is identified as the most suitable district for a humanitarian logistics distribution center. The study highlights the importance of considering multiple criteria when selecting distribution center locations and provides a framework for using multi-criteria decision-making methods in logistics planning. Disaster managers and policymakers can use the results to make informed decisions about the location of humanitarian logistics distribution centers.

Urban Forests, Territorial Planning and Political Stability: Key Factors to Face Climate Change in a Megacity

Megacities across the planet face a range of economic and territorial challenges. Future climate predictions suggest that several urban areas will present greater social and environmental problems in the coming decades, which makes strategic planning urgent and necessary for sustainable adaptation in all senses, i.e., economic, social and environmental. Some cities in the global south, such as Sao Paulo, had a history of rapid urban development without environmental planning throughout the 20th century, making urgent the need to expand green infrastructure, especially with the connection of forest fragments to the urban fabric. Therefore, this study aimed (i) to evaluate the increase in official urban parks throughout history, considering the spatialization and typologies of new parks in the territory of Sao Paulo, (ii) to understand the ecosystem services provided by urban forests and its distribution in the city, and (iii) to associate the temporal stability attributes of recent environmental secretaries with the officialization of protected areas in the city of Sao Paulo. The results revealed that only at the beginning of the 21st century was there an effective increase in the number of protected areas that shelter fragments of urban forests, contrasting the strong socio-spatial segregation that occurred in the 20th century with an economically vulnerable population that occupied peripheral areas with greater natural disaster risk. Political stability was a key factor for success in the environmental management of a megacity. The scenarios of environmental injustice reported in this manuscript can be revised with the implementation of policies and actions aimed at expanding green infrastructure in strategic sites, based on specific park typologies for each location. Such actions may come from public–private partnerships (PPP) that subsidize the socio-environmental transformation of the territory.