Vulnerability Assessment Research Articles

Liner shipping network vulnerability to component disruptions: A China-Europe container flow analysis

This study conducts a vulnerability analysis of the China-Europe liner shipping network, including all relevant liner shipping services and China Railway Express services. The standard multicommodity network flow model is extended to assess network vulnerability and the relative importance of ports and services by simulating container flow redistribution in various disruption scenarios. The results show the network is highly resilient to both individual port disruptions and individual service disruptions. Additionally, the distinct shipping network structures of the three shipping alliances result in varying degrees of vulnerability, highlighting the significance of diversified market suppliers in enhancing the overall network resilience. Specifically, the Ocean Alliance is vital in facilitating China-Europe trade, especially along the Mediterranean route, and is more resilient to individual port disruptions.

Enhancing resilience and security in the U.S. power grid against cyber-physical attacks

The U.S. power grid faces increasing risks from cyber-physical attacks that could disrupt essential services and compromise national security. The fusion of artificial intelligence with cybersecurity protocols is perceived to present a consequential shift in the current efforts to safeguard critical infrastructures. This manuscript aims to identify the most critical vulnerabilities within the power grid's infrastructure, develop advanced machine learning-based threat detection systems, and propose automated response mechanisms to mitigate impacts effectively. By integrating comprehensive vulnerability assessments, innovative detection technologies, and autonomous response strategies, this study seeks to enhance the resilience of the power grid against sophisticated cyber threats.

A vulnerability evaluation method of earthen sites based on entropy weight-TOPSIS and K-means clustering

The degradation of earthen sites due to natural and human factors has become a pressing issue, necessitating urgent protection measures. In this context, accurate assessment of the vulnerability of earthen sites is essential for the development of effective conservation strategies. In this study, a comprehensive evaluation framework that incorporates multiple indicators is proposed. In particular, the entropy weight- (Technique for Order Preference by Similarity to an Ideal Solution) TOPSIS method is employed for quantitative vulnerability assessment and combined with K-means clustering to define vulnerability levels for earthen sites. To validate the proposed approach, the vulnerability of 29 sections of the Ming Great Wall is evaluated. Eventually, the 29 earthen sites are categorized into three levels: high, medium, and low, according to their degree of vulnerability. The results of gray correlation analysis and entropy weight-TOPSIS method are compared using the ontology missing amount in the original data as the validation standard. The results show that the Pearson correlation coefficient value of the entropy weight-TOPSIS method with the ontology missing amount was 0.859, while the Pearson correlation coefficient value of the gray correlation analysis method with the ontology missing amount was 0.691, so that the results of the entropy weight-TOPSIS method can more accurately reflect the actual vulnerability of earthen sites.

Probabilistic seismic hazard model and vulnerability analysis for typical regional structures

Probabilistic seismic hazard model and vulnerability analysis for typical regional structures

Capability driven vulnerability analysis of a naval combatant

This paper presents a fine-tuned approach to vulnerability assessment, focusing on protecting capabilities instead of individual systems and their components. The foundation of this method is a system model which uses functional chains to identify the contribution of individual systems towards to fulfilment of the ships capabilities. The key advantage of using functional chains is that by showing that individual functions can still be fulfilled, it is demonstrated that the vital capability containing those functions remains available. This paper first explains the theory behind the methodology and then demonstrates its working principles using a generalized case study for a single capability.

Adversarial Attack in Traffic sign Identification using Deep Learning Frameworks

Deep learning frameworks promote the development of artificial intelligence and demonstrate considerable potential in numerous applications. However, the security issues of deep learning frameworks are among the main risks preventing the wide application of it. Attacks on deep learning frameworks by malicious internal or external attackers would exert substantial effects on society and life. We start with a description of the framework of deep learning algorithms and a detailed analysis of attacks and vulnerabilities in them.

Climate Resilience, Vulnerability, Differential Impacts and Adaptive Capacity in Nigeria

This research investigates and promotes climate resilience in Nigeria’s vulnerable communities, which are disproportionately affected by climate change. How climate change worsens existing problems such as food insecurity, poverty, and conflict in Nigeria serve as the rationale to this study. Hence, defining climate resilience, reviewing relevant literature, and conducting a case study to generate evidence-based recommendations give insight into the objectives of this study. Therefore, reviewed literature shows the diverse impacts of climate change on disadvantaged groups, such as small-scale farmers and urban coastal dwellers. Vulnerability assessments highlight the need for adaptive capacity, integrating local knowledge in solutions. Case studies illustrate vulnerabilities in poor urban coastal areas, rural regions, and the Niger Delta. Differential impacts are examined, such as the gendered effects of climate change on women and the challenges faced by indigenous communities and urban slums. Adaptive capacity is discussed, stressing social capital, collective action, and the interrelation of adaptation, adaptive capacity, and vulnerability. Adaptive capacity assessment in Nigeria indicates difficulties in rural farming households and urban coastal communities, requiring enhanced capacities. Indigenous knowledge plays a vital role in adaptive measures, fostering resilience. Resilience enhancement strategies emphasize social justice, inclusive governance, and community-based adaptation, acknowledging the significance of local empowerment and participation. Technology and innovation provide possibilities but need context-specific methods. These strategies can help build climate resilience in Nigeria’s vulnerable communities, offering a basis for policymakers, practitioners, and stakeholders to tackle the complex challenges of climate change.

Spatial vulnerability assessment of urbanites of Rawalpindi to the combined effects of climate change and pandemics using GIS mapping

ABSTRACT Pakistan is urbanising at an extremely high pace and if this urbanisation is left unplanned, it can not only increase the vulnerability of its residents, but it can be the source of high GHG emissions, thereby increasing vulnerability to climate change. Moreover, not all city inhabitants are equally vulnerable to climate change and Pandemics like Covid-19. This study estimated the vulnerability ranking of urban union councils of Rawalpindi through a composite index without assigning weights, considering 20 physical, economic, social and institutional indicators as drivers of vulnerability, with an emphasis on climate change and the outbreak of chronic diseases. A survey is conducted from the urban residents of Rawalpindi, to measure the vulnerability towards climate change and the outbreak of Covid-19. The sample size was 440 households. The results showed that union councils of New Katarian-II, Dhaman and MC Stadium B Block were less vulnerable as compared to other areas of Rawalpindi with the lowest vulnerability ranking of MC Stadium B Block as 404 and the most vulnerable ranking of Dhok Ratta, UC-2 as 895. Densely populated areas of Dhok Ratta were more vulnerable to high chronic disease exposure and death rates. This study concluded that high population density and composition, low healthcare facilities in slums coupled with low income were the main reasons for chronic disease outbreaks in urban areas. Urban planning or master plans can be a solution for such densely populated areas and high-rise buildings with green infrastructure can reduce the impact of the heat island effect.

The Influence of the Aggregate Configuration on the Seismic Assessment of Unreinforced Masonry Buildings in Historic Urban Areas

Unreinforced masonry (URM) buildings in historic urban areas of European countries are generally clustered in an aggregate configuration and are often characterized by façade walls mutually interconnected with adjacent ones. As a result, the seismic performance of buildings in an aggregate configuration can be affected by the mutual interaction between the adjacent units. This interaction, often called the aggregate effect, could significantly influence the level of the seismic vulnerability of URM buildings in aggregate configuration toward in-plane and out-of-plane mechanisms, the latter being the object of the present paper. Traditional methods for assessing the seismic vulnerability of URM buildings neglect the interactions between adjacent buildings, potentially underestimating the actual vulnerability. This study aims to derive fragility curves specific for UMR buildings in aggregate configuration and proposes an innovative methodology that introduces the aggregate effect into an analytical approach, previously developed by the authors for isolated URM buildings. The aggregate effect is modeled by accounting for the friction forces arising among adjacent facades during the development of out-of-plane overturning mechanisms by considering different scenarios, based on how façade walls interact with neighboring structures (e.g., whether they are connected to transverse and/or lateral coplanar ones). The proposed approach is applied to a real case study of an Italian historical center. The obtained results demonstrate that the aggregate effect significantly influences the fragility curves of URM buildings arranged in aggregate configurations. This highlights the importance of considering this effect and the usefulness of the proposed approach for large-scale assessments of seismic vulnerability in historic urban areas, contributing to sustainable disaster risk prevention.

Analyzing Coastal Vulnerability Using Analytic Hierarchy Process and Best–Worst Method: A Case Study of the Marmara Gulf Region

AbstractCoastal regions, integral to human economic and social frameworks, face increasing threats from disasters and climate change. This situation has made it necessary and a priority to study these areas at an international level. To be able to take precautions, protect, and manage coastal areas, it is essential to identify their coastal vulnerability. In this study, a coastal vulnerability analysis was conducted using best–worst method (BWM) in the Marmara Gulf Region, Türkiye. Moreover, comparison was made with the analytic hierarchy process (AHP) method in the subject of assessment of coastal vulnerability. According to the vulnerability map obtained with AHP, 17% of the region was determined to have a moderate vulnerability risk, 54% had a high risk, and 29% had a very high risk. On the other hand, the analysis results in BWM calculated the vulnerability percentages as follows: 18% moderate vulnerability, 57% high vulnerability, and 25% very high vulnerability. Visual and statistical comparisons revealed that the BWM method provides more consistent results and involves fewer pairwise comparisons than the AHP method. Thus, it offers ease of use and convenience to decision maker while maintaining relatively same level of weights for criteria. This study aims to lay the foundation for a dynamic system designed to assess coastal vulnerability, emphasizing usability for policymakers in decision-making and flexibility across various scenarios. Particularly, the adoption of the BWM provides notable benefits due to its direct approach and ability to yield more uniform and dependable evaluations, thereby efficiently addressing intricate decision-making challenges.

Riverine Community Vulnerability to Flooding: An Insight from the Bramhaputra-Jamuna River at Harirampur Upazila, Manikganj

Bangladesh is one of the nations that has a distinctive environment for floods. Most of the land is low-lying, and 80% is a floodplain, making it highly susceptible to frequent flooding. This research aims to investigate the susceptibility of riverine communities to flooding and display the spatial distribution of vulnerability for the study region (Harirampur Upazila). A vulnerability assessment index is developed using multiple variables related to social, physical, and economic vulnerability. A quantitative survey is conducted to gather data on physical, social, and economic factors contributing to household vulnerability to floods. The results reveal that out of the five unions in Harirampur Upazila, Gopinathpur, and Lesraganj unions, they have been found to have a high vulnerability to flooding (vulnerability scores of 3.19 and 3.10, respectively). Kanchanpur Union is severely vulnerable to flooding (score 3.57). On the other hand, Gala Union is designated as a lowly vulnerable (score of 2.23) area, and Ramkrishnapur Union is classified as a moderately vulnerable (score of 2.83) area. Finally, this study would help to assign government and non-government initiatives following the vulnerability category during critical conditions of any disaster period. Jagannath University Journal of Life and Earth Sciences, 9(2): 195-214, 2023 (December)

Integrating Indicators in Agricultural Vulnerability Assessment to Climate Change

AbstractClimate change stands as one of the most pressing challenges confronting global ecosystems and human livelihoods. The agriculture sector of Anuradhapura district, Sri Lanka, well renowned for its pivotal role in the nation’s food production, faces an increasing threat from the changing climate. This study aims to incorporate the indicator system method which uses a set of indicators to assess the agricultural vulnerability (AV) to climate change in Anuradhapura district. The AV assessment used in this study involves three principal components exposure, sensitivity, and adaptive capacity. The indicators are normalized to allow spatial analysis and smooth integration within a geographic information system (GIS) framework. The AV of Anuradhapura district ranged from 0.32 to 0.67 and was divided among five levels very low, low, moderate, high, and very high. The findings suggest that Anuradhapura’s agricultural sector was significantly impacted by climate change as the majority of the total area was found to have very high, high, and moderate levels of AV with 25, 28, and 24%, respectively. The results obtained from this study are essential for formulating plans to improve Anuradhapura’s agricultural sector resilience and adaptability to ensure food security and livelihood sustainability considering the ever-changing climate.

Harnessing ML and GIS for Seismic Vulnerability Assessment and Risk Prioritization

Harnessing ML and GIS for Seismic Vulnerability Assessment and Risk Prioritization

Deep learning vulnerability analysis against adversarial attacks

In the age of artificial intelligence advancements, deep learning models are essential for applications ranging from image recognition to natural language processing. Despite their capabilities, they're vulnerable to adversarial examplesdeliberately modified inputs to cause errors. This paper explores these vulnerabilities, attributing them to the complexity of neural networks, the diversity of training data, and the training methodologies. It demonstrates how these aspects contribute to the models' susceptibility to adversarial attacks. Through case studies and empirical evidence, the paper highlights instances where advanced models were misled, showcasing the challenges in defending against these threats. It also critically evaluates mitigation strategies, including adversarial training and regularization, assessing their efficacy and limitations. The study underlines the importance of developing AI systems that are not only intelligent but also robust against adversarial tactics, aiming to enhance future deep learning models' resilience to such vulnerabilities.

Seismic vulnerability analysis of a high‐rise steel frame structure equipped with novel displacement‐amplified viscoelastic dampers

SummaryThe energy dissipation capacity of conventional viscoelastic dampers (VEDs) cannot be fully exerted due to the relatively small inter‐story displacement of building structures. Thus, a novel VED with a displacement amplification mechanism based on the lever principle is developed in this study to achieve small displacement but high energy dissipation ability. First, the structural layout of an amplified viscoelastic damper (AVED) system is briefly introduced, and then the displacement amplification effect is described in detail. According to the working principle of the AVED and the relationship of force and geometric displacement in an actual frame structure, the restoring force theoretical formula of the corresponding amplified damper is derived. Based on the restoring force of the AVED and in combination with the secondary development function of the ABAQUS unit, a VUEL subroutine suitable for the explicit algorithm is programmed with the FORTRAN language. Then, the correctness of the subroutine is verified through a comparative analysis of the ABAQUS simulation and theoretically calculated results. Consequently, the vulnerability analysis of a high‐rise steel frame structure is conducted by using the incremental dynamic analysis (IDA) method, and the influence of the AVED on the seismic performance of the steel frame structure is quantitatively analyzed from the perspective of probability statistics. The analysis results show that compared with the VED structure, the failure probability of the AVED‐2 and AVED‐3 structures reaching the ultimate failure state at all levels is reduced by approximately 43% and 57%, respectively, and the collapse margin ratio (CMR) of structures under large earthquakes is increased by approximately 35% and 68%, respectively. This indicates that the AVED structures with displacement‐amplified dampers have a better effect on the seismic stability of tall steel frame structures under random seismic excitations. This study aims to provide comprehensive information for the seismic‐resistant design of tall buildings in earthquake‐prone regions.

Vulnerability Assessment of Groundwater Influenced Ecosystems in the Northeastern United States

Groundwater-influenced ecosystems (GIEs) are increasingly vulnerable due to groundwater extraction, land-use practices, and climate change. These ecosystems receive groundwater inflow as a portion of their baseflow or water budget, which can maintain water levels, water temperature, and chemistry necessary to sustain the biodiversity that they support. In some systems (e.g., springs, seeps, fens), this connection with groundwater is central to the system’s integrity and persistence. Groundwater management decisions for human use often do not consider the ecological effects of those actions on GIEs. This disparity can be attributed, in part, to a lack of information regarding the physical relationships these systems have with the surrounding landscape and climate, which may influence the environmental conditions and associated biodiversity. We estimate the vulnerability of areas predicted to be highly suitable for the presence of GIEs based on watershed (U.S. Geological Survey Hydrologic Unit Code 12 watersheds: 24–100 km2) and pixel (30 m × 30 m pixels) resolution in the Atlantic Highlands and Mixed Wood Plains EPA Level II Ecoregions in the northeastern United States. We represent vulnerability with variables describing adaptive capacity (topographic wetness index, hydric soil, physiographic diversity), exposure (climatic niche), and sensitivity (aquatic barriers, proportion urbanized or agriculture). Vulnerability scores indicate that ~26% of GIEs were within 30 m of areas with moderate vulnerability. Within these GIEs, climate exposure is an important contributor to vulnerability of 40% of the areas, followed by land use (19%, agriculture or urbanized). There are few areas predicted to be suitable for GIEs that are also predicted to be highly vulnerable, and of those, climate exposure is the most important contributor to their vulnerability. Persistence of GIEs in the northeastern United States may be challenged as changes in the amount and timing of precipitation and increasing air temperatures attributed to climate change affect the groundwater that sustains these systems.

Analysis of landscape pattern vulnerability in Dasi river basin at the optimal scale

Since the reform and opening up in 1978, the Dasi River Basin within Jinan’s startup area from replacing old growth drivers with new ones (startup area) has experienced rapid urbanization and industrialization, and the landscape pattern has changed significantly, resulting in a series of eco-environmental problems. In order to more accurately identify the vulnerable areas of landscape pattern, understand their cause mechanism and changing laws, and provide a theoretical basis for the implementation of sustainable landscape pattern planning and management in the region. Four Landsat images of 2002, 2009, 2015 and 2020 were taken as data sources, and the optimal granularity of landscape pattern analysis was determined from the perspective of landscape level and class level by using the coefficient of variation method, granularity effect curve and information loss model, and the optimal amplitude was determined by using the grid method and semi-variance function. Then, the landscape vulnerability assessment model was constructed based on the optimal scale, and its spatiotemporal evolution characteristics and spatial autocorrelation were analyzed. The result showed that: (1) The optimal granularity of landscape pattern analysis in this study area was 80 m, and the optimal amplitude was 350 × 350 m. (2) During 2002–2020, the overall vulnerability of landscape pattern in the southern part of the study area showed an increasing trend, while that in the middle and northern parts showed a decreasing trend. (3) The mean values of the vulnerability index of the overall landscape pattern in 2002, 2009, 2015 and 2020 were 0.1479, 0.1483, 0.1562 and 0.1625, respectively, showing an increasing trend year by year. In terms of land use, during 2002–2020, the average vulnerability indices of forestland and built up land increased by 23.18% and 21.43%, respectively, followed by water body and bare land, increased by 12.18% and 9.52%, respectively, while the changes of cropland and grassland were relatively small, increasing by 5.36% and 5.65%, respectively. (4) During 2002–2020, the landscape pattern vulnerability showed a significant spatial positive correlation in terms of spatial distribution. The Low-Low areas were generally transferred from the southeastern and midwestern to the middle and northern, and the High–High areas were mainly transferred from the middle to the southern. Overall, the degree of the spatial agglomeration of the landscape pattern vulnerability showed an increasing trend.

Northeast Atlantic fish stock productivity hindcasts and forecasts from a Bayesian framework reveal pronounced climate‐induced dynamics

AbstractClimate change continues to exert pressure on ocean ecosystems. The fisheries‐related responses, such as altered body growth, recruitment and spatial distribution of the targeted stock(s), have generally been reasonably well investigated. Nevertheless, there are still important knowledge gaps in how biophysical drivers impact stock productivity and thereby sustainable harvest levels. Considering this, we investigated 26 fish stocks in the Northeast Atlantic, a region characterized by accelerated climate change effects and a diverse set of fisheries. A novel, stepwise, Bayesian framework to quantify stock productivity was established to identify shared trends and project future patterns, aiming at determining essential baselines for adaptive fishery management in the face of climate change. Despite variation among large marine ecosystems and stocks, an overall declining trend in productivity over the past four decades was observed, especially in high‐latitude areas. These hindcast results were mainly attributed to higher temperatures posing negative effects on productivity, which was dependent on the stock's thermal preference. Contrastingly, the proxy for food availability – gross secondary production – exhibited less consistent impacts. In the forecast, the applied Shared Socio‐economic Pathways (SSPs) indicated that most stocks are likely to encounter adverse effects, with the worst cases expected to occur under SSP2‐4.5 in the 2050s, and under SSP5‐8.5 in the 2090s. Thus, this study generally not only supports earlier climate vulnerability assessments (‘scorings’) of experts but also provides evidence for revised directional effects under climate change, underlining the complexity of processes affecting stock productivity.

Eigenvector spatial filtering enhancing natural hazards vulnerability assessment in a susceptible urban environment: A case study of Izmir earthquake in Turkey

The increasing risk of earthquakes in urban areas has made it crucial to develop accurate vulnerability models for city infrastructure and systems. We aimed to assess and compare the effectiveness of different models and vulnerability analysis techniques in predicting earthquake vulnerability in the specific context of Izmir, Turkey. One central hypothesis in this research aimed to determine whether integrating Eigenvector Spatial Filtering (ESF) into both regression models and machine learning algorithms would yield a comparable enhancement in model performance. We performed earthquake vulnerability modeling (EVM) by considering (ⅰ) only seismic-related variables (SRV) and (ⅱ) integrating ESF by using Moran's eigenvector maps (MEMs). For each approach, we evaluated the predictive performance of two simple regression-based models; generalized linear model (GLM) and generalized additive model (GAM), and two complex machine learning ones; generalized boosting model (GBM), and random forest (RF). The study utilized five primary indicators encompassing geotechnical, physical, structural, social, and facilities data. The predictive performance of the models was assessed using evaluation metrics including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and adjusted R2. The results indicated that the optimal candidate model consisted of five key variables: altitude, building height, distance to safety gathering places, Peak Ground Acceleration (PGA), and population density. We found that decision-tree-based methods performed better than regression-based methods for both modeling schemes. RF exhibited the highest predictive performance for the training data (RMSE = 0.59, adjusted R2 = 0.71), while GBM outperformed other models for the test data (RMSE = 0.79, adjusted R2 = 0.78). However, incorporating ESF to the EVM analysis revealed that regression-based methods, particularly the GLM, obtained highest improvement in accuracy (RMSE 0.94 vs 0.76 and adjusted R2 0.56 vs 0.71 for the SRV and SRV + MEMs modeling approach). Significant differences were observed between GLM-GBM and GLM-RF comparisons, as well as GAM-GBM and GAM-RF comparisons. The findings of this research are expected to be helpful for informed decision-making, targeted risk reduction, and the development of effective policies and strategies to enhance preparedness and resilience in the face of seismic events in highly susceptible urban systems.

Comparative Analysis of Flood Estimation using Log-Pearson Type III and Gumbel Max Models in the Cauvery River, India

Flooding is one of the most destructive global disasters in scale, geographical extent, property and life loss, and population displacement. The Cauvery River is one of the flood vulnerable rivers in the Peninsular region of India. At-site flood frequency analysis is performed using flow data obtained at the Kodumudi gauged site in the Cauvery River. Log Pearson Type III and Gumbel Max distribution models are used in the present study to estimate peak floods for different return periods. The Central Water Commission provides the annual maximum discharge for the Kodumudi gauged site over 39 years (1980-2018). The goodness of fit test employing the Kolmogorov-Smirnov and Anderson- Darling tests, reveals that Log-Pearson Type III best estimates peak floods in the study area. The peak floods predicted by Log-Pearson Type III for return periods 2, 5, 10, 25, 50, 100, 200, and 500 years are approximately 929, 1886, 2998, 5303, 8002, 11929, 17633, and 29228 cumecs. Hydraulic structures can be designed in the region based on 100-year flood. The present research could help with flooding management approaches, vulnerability analyses, and hydraulic structure design in the study region.