Evaluation Of Vulnerability Research Articles

A GIS-based framework for flood hazard vulnerability evaluation in Thudawa area, Sri Lanka

A GIS-based framework for flood hazard vulnerability evaluation in Thudawa area, Sri Lanka

Comparison of informative modelling and machine learning methods in landslide vulnerability evaluation – a case study of Wenchuan County, China

After the earthquake in Wenchuan in 2008, landslides have been happening frequently, causing a buildup of sediment in slopes and gullies. This creates a potential for mudslides and flash floods. It is important to create a model to assess the likelihood of landslides in Wenchuan County for disaster prevention. This study focuses on Wenchuan County, using 6180 co-seismic landslide sites as data. The relevant survey data is combined, and slope, normalized difference vegetation Index (NDVI), peak seismic acceleration (PGA), stream power index (SPI), topographic wetness index (TWI), distance from the road, distance from the fault, distance from the water system, relief intensity, aspect, curvature, land use type, and lithologic characters are chosen. The significance of the co-seismic landslide key influence factors is discussed. The study area’s disaster susceptibility was assessed using the informative hierarchical analysis method model (I-AHP) and fully connected neural network (FCNN) model; the evaluation’s findings mostly agreed with the survey’s actual findings. The study’s findings demonstrated that: (1) the FCNN model’s AUC value was 0.910, while the I-AHP model’s AUC value was 0.768, and the ROC curve and AUC value analysis indicated that the FCNN model outperformed the I-AHP model. (2) The three most significant influencing factors are lithology, topographic relief, and distance from roads; they account for 29.5%, 11%, and 10% of the total, respectively. Thus, landslide hazard research should be done in the high terrain undulating areas surrounding the road, and rock monitoring should be intensified in post-earthquake landslide-prone areas like Wenchuan County. (3) Given that Yingxiu Town is the epicentre of the region and that landslide disasters are highly likely to occur there, technical support for landslide disaster prediction, forecasting, safe prevention, and control in the area needs to be strengthened.

A GIS-based framework for flood hazard vulnerability evaluation in Thudawa area, Sri Lanka

A GIS-based framework for flood hazard vulnerability evaluation in Thudawa area, Sri Lanka

A Route Diversity–Based Approach for Estimating Vulnerability of Stations in a Multimodal Public Transport Network

Multimodal public transport network (MPTN) plays an important role in relieving road traffic pressure for metropolitan area. Nevertheless, the impact of an accident happened in an individual station may not only disrupt the station itself or the single lines that go through the station but also spread over the whole network. Therefore, identifying the vulnerable stations is essential for improving the MPTN management against the systematic risk caused by accidents. In this paper, we proposed a route diversity‐based approach to measure the vulnerability of stations in MPTN based on the complex network theory. The route constraint parameters were established to reflect the travel time restriction in constructing the set of passengers’ acceptable routes. In addition, an algorithm was formulated to rapidly calculate the route diversity index and meanwhile avoid the “overlapping routes” problem. A simple virtual network was used as a numerical example to compare the proposed approach with the vulnerability evaluation approaches based on degree centrality and betweenness centrality. Finally, the proposed approach was applied to the MPTN of Beijing to explain its effectiveness and potential applications. The results show that the proposed method can efficaciously estimate vulnerable nodes compared with degree centrality and betweenness centrality. Meanwhile, the acceptable routes between any OD pairs in the MPTN are 1–10 according to the constrained parameter. In addition, the average number of acceptable routes between OD pairs of Beijing MPTN is 3.649. By ranking the stations according to their vulnerability, it can be found that the top 5 vulnerable stations are all external traffic hubs or the stations around famous commercial areas. The results suggest that these stations are significant for external transport as well as crucial for internal urban transportation systems. The research output could contribute to the MPTN management in accident prevention and emergency handling.

Evaluation of Groundwater Vulnerability to Pollution Using the DRASTIC Model and GIS in the Plain Part of the Khazir River Basin, Northern Iraq

Evaluation of Groundwater Vulnerability to Pollution Using the DRASTIC Model and GIS in the Plain Part of the Khazir River Basin, Northern Iraq

Evaluation of vulnerability to Tsunamis in the city of Puntarenas, Costa Rica

An estimate of vulnerability to tsunamis was made in the city of Puntarenas, Costa Rica, using the Minimum Geostatistical Units (UGM) contained in the 2011 Population and Housing Census for the country. Vulnerability was defined based on four subvariables: disabled and dependent population, poverty, education and exposure, which in turn were grouped into two variables: socioeconomic and environmental, to finally have a total of 13 explanatory indicators. Once the vulnerability scheme was defined, the census information was collected and the calculations were made. The results obtained were four maps for each subvariable and an integrated map of the vulnerability index.

Identification of Priority Areas for Ecological Restoration in Coal Mining Areas with a High Groundwater Table Based on Ecological Security Pattern and Ecological Vulnerability

Resource exploitation and rapid urbanization in coal mining areas with a high groundwater table (CMA-HGT) have created double pressure on regional ecological security and increased the vulnerability of the ecological environment. Ecological restoration should be carried out to balance the relationship between economic development and ecological and environmental protection. An ecological security pattern (ESP) can identify key locations that are critical to maintaining ecosystem service stability and landscape connectivity, with territories displaying relatively high vulnerability identified as priority areas for ecological restoration. The Jining planning area (JPA), a typical representative of CMA-HGT, was taken as the study area. To construct the integrated ESP, ecological sources were identified by measuring the importance of multiple ecosystem services and landscape connectivity. Then, the ecological resistance surface was constructed, and the ecological corridors, pinch points, and barriers were determined using circuit theory. Ecological vulnerability (EV) evaluation was performed from the perspectives of natural factors and human activities. The study results showed that the spatial superposition of ESP and EV can effectively identify priority areas for ecological restoration. The ecological restoration priority areas for ecological sources, ecological corridors, and ecological pinch points were 44.56 km2, 11.7 km2, and 9.96 km2, respectively, and corresponding restoration strategies were proposed. Some valuable references were provided for the identification and restoration of priority areas for ecological restoration in CMA-HGT.

A REVIEW ON IMPROVISED EXPLOSIVE DEVICES AND THE HUMANITARIAN HARM CAUSED BY THEM

Improvised explosive devices (IEDs) in all their forms have emerged as the most serious threat. These devices can range from simple explosive devices to complex military systems using high-grade explosives. A thorough grasp of the many types of devices and their effects is required for proper mass casualty event planning and management of the complex poly trauma victims that they usually bring. They may wreak havoc on a landscape. Depending on the desired target, they can be initiated in a variety of ways. As a result, there is a pressing need to research and ameliorate the mechanism of extremities harm induced by these devices. These improvised explosive devices are generally uncommonly encountered outside the battlefield. The majorities of vulnerability evaluations presume that an improvised explosive device would attain maximal TNT equivalency and detonate effectively. They are still a popular terrorist weapon. These are unusual explosive weapons that can be used in a variety of ways to cause injury, fatalities, and destruction of property in both military and civilian conditions. IEDs continue to be a domestic menace. The science and damage profile of these devices are described in this review. The humanitarian devastation inflicted by IEDs is thus depicted. KEYWORDS: - improvised explosive devices, explosives, detonation, injury, impact testing, biomechanics, ground penetrating radar, detection, prevention, National Bomb Data Centre

Assessing Climate Disaster Vulnerability in Peru and Colombia Using Street View Imagery: A Pilot Study

Community and household vulnerability to natural hazards, e.g., earthquakes, hurricanes, and floods, is a concern that transcends geographic and economic boundaries. Despite the abundance of research in this field, most existing methods remain inefficient and face the challenge of data scarcity. By formulating and investigating the correlation between the household vulnerability and street view images of buildings, this research seeks to bridge the knowledge gap to enable an efficient assessment. Especially in developing countries, the widespread prevalence of outdated or inadequately enforced building codes poses a significant challenge. Consequently, a considerable portion of the housing stock in these regions fails to meet acceptable standards, rendering it highly vulnerable to natural hazards and climate-related events. Evaluating housing quality is crucial for informing public policies and private investments. However, current assessment methods are often time-consuming and costly. To address this issue, we propose the development of a rapid and reliable evaluation framework that is also cost-efficient. The framework employs a low-cost street view imagery procedure combined with deep learning to automatically extract building information to assist in identifying housing characteristics. We then test its potential for scalability and higher-level reliability. More importantly, we aim to quantify household vulnerability based on street view imagery. Household vulnerability is typically assessed through traditional means like surveys or census data; however, these sources can be costly and may not reflect the most current information. We have developed an index that effectively captures the most detailed data available at both the housing unit and household level. This index serves as a comprehensive representation, enabling us to evaluate the feasibility of utilizing our model’s predictions to estimate vulnerability conditions in specific areas while optimizing costs. Through latent class clustering and ANOVA analysis, we have discovered a strong correlation between the predictions derived from the images and the household vulnerability index. This correlation will potentially enable large-scale, cost-effective evaluation of household vulnerability using only street view images.

Vulnerability Assessment of Prefabricated Component Suppliers in Prefabricated Buildings Based on the Controlled Interval and Memory Method Extended under the Probability Language Terminology Set

Accurately evaluating the vulnerability of prefabricated components in prefabricated buildings is the basis of scientific management of its supply chain. Thus, a novel vulnerability evaluation method for the suppliers of prefabricated components in the context of prefabricated buildings was proposed in this study. First, a vulnerability evaluation index system was identified and constructed by the pressure–state–response (PSR) model. With the clear logic and strong applicability of PSR, this system, including 18 indexes, was easy to understand and exhibited strong practicability. The weights were then determined via the multiplicative analytic hierarchy process with the expansion of a probabilistic language terminology set (PLTS). PLTS could describe decision information more comprehensively, so the evaluation result is more scientific. Subsequently, the probability of the vulnerability level was superimposed in a parallel bottom-to-top method via the controlled interval and memory (CIM) models, and these were extended by the PLTS. This set handled the uncertain information in the decision-making process more efficiently and accurately, and finally determined the vulnerability distribution. Ultimately, the probability of each vulnerability level was determined by combining the weight information. The reconstruction and resettlement housing construction project of urban shanty towns in the Taohua South Road Community in Xihu District, Nanchang City, was selected as a case study. The case study proves the effectiveness of this method, which has higher resolution and is more convenient for managers to make decisions.

Impact of climate and environmental change on the menopause.

The huge impact of climate change on humankind is multidimensional, and includes direct and indirect challenges to the physical, psychological and socio-cultural wellbeing. Women may be more vulnerable to climate-sensitive diseases, but little attention has been paid to specific needs and challenges associated with the menopause transition. The increase in average and extreme temperatures may modulate the manifestation of vasomotor symptoms; in particular, environmental temperature and seasonality may affect hot flushes and night sweats. However, more research is needed to define the impact of climate-related factors among the determinants influencing the individual experience of menopause. In addition, increased exposure to environmental pollution and toxins may also have a role in the modulation of ovarian aging mechanisms, possibly influencing timing of menopause. Finally, both air pollution and menopause transition are associated with unfavorable modifications of cardio-metabolic, bone and cognitive health, and account should be taken of these in the evaluation of the individual woman's health vulnerabilities. Overall, the evidence reported in this narrative review supports the need for specific strategies aimed at reducing the burden of climate and environmental change on menopausal women. Healthcare providers should promote behavioral measures that reduce anthropogenic climate change and at the same time have a beneficial role on several domains of physical and psychological wellbeing. From this perspective, menopause represents a golden moment to implement virtuous behaviors that will benefit at the same time women's longevity and the planet.

Identification of key brittleness factors and multi-scenario analysis of the water-energy-food-ecology nexus vulnerability based on NRS-BN

Water, energy and food are the basic resources on which human beings depend for survival. With the intensification of human activities, the demand for resources represented by water, energy and food continues to increase, resulting in increasing pressure on the ecological environment, and the vulnerability of water, energy, food and ecosystem becomes increasingly prominent. Identifying the critical vulnerability factors of the water-energy-food-ecology nexus and formulating targeted management measures have become the key to achieving sustainable development. This paper innovatively proposes to study the water-energy-food-ecology nexus from the perspective of vulnerability for the first time, in which the vulnerability evaluation index system of the water-energy-food-ecology nexus is firstly constructed based on the VSD framework, and the attribute reduction is carried out using neighborhood rough sets. Then, a Bayesian network model is built and parameter learning is performed by combining machine learning and expert experience. Finally, different scenarios are set up to identify the key factors that hinder the vulnerability reduction of the water-energy-food-ecology nexus and obtain the vulnerability probability of the nexus under different scenarios using forward and backward inference and sensitivity analysis of Bayesian networks, overcoming the drawback that many prediction models cannot achieve diagnostic inference. The results show that: 1) from 2008 to 2019, the overall vulnerability of the water-energy-food-ecology nexus in the Yangtze River Economic Belt is low. 2) The key factors at the indicator level that hinder the vulnerability reduction of the water-energy-food-ecology nexus mainly include the storage capacity of water conservancy projects, wastewater discharge per 10,000-yuan GDP, and water consumption per 10,000-yuan GDP, and the subsystem level is water, food, energy and ecology system, in that order. 3) The reduction in vulnerability within an individual subsystem can have a beneficial impact on reducing vulnerability within the water-energy-food-ecology nexus. However, this reduction may also lead to an increase in vulnerability within other subsystems. Therefore, in the process of developing water, energy, food, and ecology system, high priority should be given to the coordinated development of all four.

Ecosystems conservation and analysis of the representativeness of protected areas of the Kyrgyz Republic in a climate change

The most valuable natural complexes and objects should be represented in the State protected areas (PA) system. In order to preserve biodiversity, it is necessary to expand protected lands to 10% of the total country area and create an ecological network that includes various categories of ecosystems. Each protected area can represent one or a number of ecosystems, depending on the height above sea level, landform, climate and other factors. In a perfect scenario, PA system of Kyrgyz Republic must represent all categories of ecosystems including mountain, steppe, semi-desert and desert. In this research the analysis of the representativeness of protected areas in Kyrgyz Republic was performed for the first time. For this purpose, GIS data sets for PAs and classification of ecosystems were used. According to the results, forest ecosystems have the highest coverage, whereas low mountain meadows and wetlands have the lowest coverage in PA system of Kyrgyz Republic. This research suggests that this type of analysis is an important tool for biodiversity conservation and evaluation of an ecological vulnerability.

Vulnerability evaluation utilizing AHP and an ensemble model in a few landslide-prone areas of the Western Ghats, India

Vulnerability evaluation utilizing AHP and an ensemble model in a few landslide-prone areas of the Western Ghats, India

Economic evaluation for 5G planning of distribution network considering transmission loss and vulnerability

Aiming at the difficulty of existing 4G networks to meet distribution network services, and the unclear economics of 5G in distribution network applications, an evaluation method of 5G communication planning economics for distribution networks considering the path loss model and comprehensive node vulnerability is proposed. Firstly, the exact coverage radius of 5G base stations is solved according to the signal transmission path loss model, and the objective function for solving the number and location of communication base stations in the distribution network is established. Secondly, a comprehensive communication node vulnerability evaluation model is established to judge whether each communication node meets the node vulnerability requirements under different communication modes, and to improve the communication reliability by installing additional fiber optic or micro base stations for nodes with too much vulnerability. Meanwhile, a comprehensive cost model for communication planning is established. Finally, the distribution network communication planning economic evaluation model is established. To verify the effectiveness of the method in this paper, the IEEE-123 topology model is used as an example to compare the economic evaluation of 5G and 4G in three scenarios: main urban areas, county town and township, respectively. According to the simulation results, using 5G communication in the distribution network reduces the total investment, operation and maintenance costs of the main urban area by 1.64 million yuan compared to 4G within 10 years. The total investment and maintenance cost of 5G in the county town for 10 years decreased by 2.16 million yuan compared to 4G. The cost of 5G in the township scenario is reduced by 860,000 yuan compared to 4G. According to the results of the comprehensive economic comparison, the comprehensive economic performance of 5G in the three scenarios is lower than that of 4G in the first few years due to the investment cost. In comparison, the comprehensive economic performance of 5G is better than that of 4G in the subsequent time due to the unique obtainable benefits of 5G, which concludes that 5G is better than that of 4G communication in all cases.

Groundwater heavy metal(loid)s risk prediction based on topsoil contamination and aquifer vulnerability at a zinc smelting site

Groundwater pollution is a recurrent problem in abandoned non-ferrous metal smelting sites, and its severity is influenced by topsoil contamination, hydrogeological characteristics, and hydrogeochemical conditions. In such unique areas, traditional methods for evaluating groundwater pollution risk are biased, as the long production history of these sites have led to highly polluted and heterogeneous soil and groundwater. Herein, based on a typical lead-zinc smelting site, As, Pb, Zn, Cd, Mn, and Ni were found to be the predominant heavy metal (loid)s in groundwater, with respective exceedance rates of 44.4%, 50.0%, 72.2%, 88.9%, 88.9%, and 61.1%. Combined with the groundwater pollution characteristics, the representative hydrogeochemical factors were screened out to optimize the following aquifer vulnerability evaluation using the AHP-DRASTICH method. A comprehensive evaluation model (DI-NCPI) for groundwater pollution risk was established by combining the DRASTICH index (DI) obtained after optimization and the Nemerow comprehensive contamination index (NCPI) of topsoil. The fit between DI-NCPI and groundwater heavy metal (loid) pollution index reached 0.956, which laterally confirms that the model has some reference value. In terms of distribution, the high-risk and very high-risk zones were mainly concentrated in the zinc smelting system, located in the southeastern and central-western parts of the site. These areas have relatively high levels of topsoil contamination and aquifer vulnerability and require focused attention in site remediation. This research highlights the importance of combining topsoil contamination and aquifer vulnerability to evaluate groundwater pollution risk in smelting areas. It provides a more targeted reference for groundwater remediation strategies in abandoned smelting sites, as well as severely polluted industrial areas.

Increasing Vulnerability of Village Heritage: Evidence from 123 Villages in Aba Prefecture, Sichuan, China

As the threat of global risks has increased, the study of village heritage has begun to move away from the interpretation of traditional values or the presentation of historical wisdom to focus on the vulnerability of villages. Taking Chinese traditional villages (a type of vernacular heritage) as the target, this study clarifies the connotation of village vulnerability and its generation pattern. Drawing on the framework of “exposure-sensitivity-adaptive capacity”, a set of vulnerability evaluation index systems integrating the characteristics of village heritage is proposed. By utilizing vulnerability index and obstacle degree models, we analyze the spatial differentiation and evolutionary characteristics of vulnerability in 123 traditional villages within Aba Prefecture, Sichuan Province, southwestern China, while also exploring the main factors influencing vulnerability evolution at different spatial scales. The results reveal an “east high, west low” spatial pattern and a clustered distribution of vulnerability in traditional villages across the region. From 2012 to 2019, the vulnerability levels fluctuated and intensified, with decreasing individual differences. The evolutionary characteristics of exposure, sensitivity, and adaptive capacity also displayed significant variations. Persistent and stable influences on village vulnerability were identified from factors such as land use scale, population density, gross domestic product, and land fragmentation. Based on these findings, strategic recommendations for village classification, protection, and development are proposed.

Drought Vulnerability Evaluation Results of Community Adaptation in Bancak District, Semarang Regency

The factors that cause drought in Bancak District are quite complex and come from many factors, both internal factors such as soil type, rainfall and topographic conditions, human resources who still manage soil and water conventionally and only take advantage of rainfall that comes in the rainy season, as well as external factors, such as climate change and global warming. This study aims to evaluate the extent to which adaptation has been carried out by the parties involved in the field of drought vulnerability management and whether the adaptation has been in accordance with the physical and socio-economic characteristics, also needs of the community in the study location. The research method carried out is in the form of qualitative research, and the questionnaires were presented in the form of semi-closed questions. From the results of the adaptation evaluation conducted that the most of the respondents stated that they had been “superlative” is to plant crops that have high economic value, so this one can be replicated and applicable to the other places.

Assessing the Imperative for Underpass Safety Technologies in Climate Change Challenges

Amidst the heightening urgency of climate change impacts, underpass roadways are particularly susceptible to the intensifying and unpredictable weather events, with flooding being a primary concern. This paper highlights the dire need for sophisticated safety assessment technologies specifically catered to these critical infrastructures. Herein, we present a pioneering risk assessment model that holistically incorporates environmental variables along with urban developmental factors to yield an exhaustive evaluation of flood vulnerability. Our proposed methodological framework offers a robust scientific foundation for the identification of high-risk areas, which is crucial for effective emergency response planning, optimized resource allocation, and enhancing the adaptive capacity of urban environments to weather extremes. The adoption of such advanced assessment tools is not just beneficial but becomes essential for informed urban planning and safeguarding the populace against the dire consequences of flood events.

EMPIRICAL VULNERABILITY ANALYSIS OF RAILWAY BRIDGE SEISMIC DAMAGE BASED ON 2022 MENYUAN EARTHQUAKE

A 6.9 magnitude earthquake at a depth of 10 km struck Menyuan County, Haibei Prefecture, Qinghai Province, China, on January 8, 2022. This earthquake damaged some railway bridges on the Lanzhou-Xinjiang Passenger Dedicated Line. This study combines relevant historical earthquake damage experience, considers the effects of earthquake intensity, site soil classification, superstructure type, foundation failure factor, number of spans, and total bridge length, and develops empirical formulas for seismic damage prediction of railway bridges using ordinal logistic regression model in SPSS software. The seismic damage matrix, as were the anticipated multi-intensity mean damage index and the empirical vulnerability curve based on the two-parameter lognormal distribution function, were generated on this basis. According to the conclusions, although the suggested particular equations and vulnerability curves do not apply to the remainder of the region owing to geographical uniqueness, the technical approach is valid. It may be used as a reference for seismic damage prediction and vulnerability evaluation in other regions. The empirical vulnerability analysis based on the earthquake damage prediction matrix derived from the regression analysis can provide reasonable and fast forecasts before the next earthquake.