Vulnerability Assessment Approach Research Articles

Variable climatic conditions dominate decreased wetland vulnerability on the Qinghai‒Tibet Plateau: Insights from the ecosystem pattern-process-function framework

Wetlands are of global importance in providing essential ecosystem services but are also sensitive to climate change and human activities. Monitoring and assessing wetland vulnerability are crucial for ecological conservation and management strategies. However, the framework of wetland vulnerability assessment and the underlying mechanisms have not been well studied. In this study, the spatiotemporal variations in wetland vulnerability on the Qinghai‒Tibet Plateau (QTP) between 1990 and 2020 were investigated based on the ecosystem pattern-process-function framework. The key driving factors were identified by partial least squares structural equation modelling (PLS-SEM) and multiscale geographically weighted regression (MGWR) models. Our results showed that the wetland ecosystem pattern index (EPI), ecosystem process index (EPOI), ecosystem function index (EFI), and wetland vulnerability index (WVI) all demonstrated an increasing pattern from northwest to southeast. Between 1990 and 2020, the mean WVI values gradually decreased from 0.616 to 0.588, indicating a steady improvement in the wetland ecosystem on the QTP. Rapid urbanization increased the EPOI, while rugged topography increased both the EPI and EPOI, and the increase in hydrological abundance enhanced the EFI, which in turn contributed to an increase in the WVI. Conversely, climatic conditions led to a reduction in the EPI, which in turn resulted in a significant decrease in the WVI. Therefore, although urbanization and topographical and hydrological factors have somewhat exacerbated the WVI on the QTP, variable climatic conditions have driven the decline in wetland vulnerability in the last three decades. Furthermore, our results indicated that the proposed framework could provide a comprehensive approach for wetland vulnerability assessment and useful implications for wetland conservation and management.

Adaptive Vulnerability Matching Assessment: A Holistic Approach for Cyber security Resilience

Abstract: In the rapidly evolving landscape of cyber security threats, organizations face the critical challenge of identifying and mitigating vulnerabilities to protect their digital assets. Organizations are left vulnerable to possible security incidents and breaches because traditional vulnerability assessment approaches frequently cannot keep up with the ever-changing nature of cyber threats. To address this challenge, we present Vulnerability Matching assessment, a comprehensive framework designed to enhance cyber security resilience through adaptive vulnerability management. The aim is to provide an efficient and userfriendly solution for identifying and addressing security vulnerabilities, which will ultimately enhance the overall security posture of the scanned software or systems. The program will provide users with comprehensive reports so they can fix the vulnerabilities found.

Differentiated socio-ecological system approach for vulnerability and adaptation assessment in the Central Himalaya

Differentiated socio-ecological system approach for vulnerability and adaptation assessment in the Central Himalaya

Localising vulnerability assessment to urban floods: A comparative analysis of top-down and bottom-up geospatial approaches in patna city, India

A notable increase in research related to geospatial vulnerability assessment of urban floods is observed, primarily driven by the advances in exponential technologies and increasing frequency and severity of such events. Several studies have been attempted in Indian contexts for local-level risk mitigation and preparedness and informed decision-making with a limited scope due to unavailability of granular data at various levels as required. In response to this gap, current study examines the potential of top-down and bottom-up geospatial vulnerability assessment approaches for urban floods in Patna City of Bihar state, India. A qualitative articulation is used to identify and characterise a set of critical indicators and attributes relevant to the local vulnerability assessment of urban floods specific to the study area. Principal Component Analysis (PCA) is applied for dimension reduction and assigning weights to indicators, and Composite Vulnerability Indices (CVIs) at Ward and Household (HH) levels are developed using multicriteria decision analysis. The results show over a fifty percent variation in the vulnerability profiles of households using ward-level (top-down) and HH-level (bottom-up) approaches. Moreover, a narrower range in the z-score of HH-level CVI (−1.26 to 2.60) over ward-level CVI (−3.95 to 2.49) from sensitivity analysis indicates the robustness of the former over the latter. The findings reveal a potential risk of maladaptation when employing a ward-level approach to vulnerability assessment as a policy instrument. The study also analytically validates the significance of localising indicators in regional and global frameworks for achieving efficient decision-making toward sustainability targets.

Geospatial approach for Landslides Vulnerability Assessment of Physical Infrastructures in Sireh Park, Johor Bahru

Geospatial approach for Landslides Vulnerability Assessment of Physical Infrastructures in Sireh Park, Johor Bahru

Deriving community vulnerability indices by analyzing multi-resolution space-borne data and demographic data for extreme weather events in global cities

Climate change in the Anthropocene is cited as one of the main reasons for extreme weather events around the world. There is a need for rapid vulnerability assessment models that may be deployed in near-real time to assist stake holders in driving the required mitigation and adaptation efforts to such events, especially in the Global South. We present an approach to derive Community Vulnerability Index (CVI) to flooding by using an integrated geospatial computational model and illustrate the methodology for six global cities. Time-series of multi-resolution space-borne optical and synthetic aperture radar (SAR) data were analyzed to derive land cover metrics from before and after flood imagery for delineating and mapping the land cover and the spatial extent of flash floods. A texture-based analysis was employed on the SAR data to delineate the spatial extent of flash floods, and this was integrated with the land cover metrics derived from the optical Sentinel-2 (Level 2A) orthorectified surface reflectance imagery. Land cover classification based on supervised image classification yielded a minimum overall accuracy of 71% when only SAR data was available after floods, and increased to 87% with the availability of cloudless Sentinel-2 data. A weighted index method, using weights derived through Factor Analysis, was used to assign appropriate weights to the composite variables derived from satellite datasets and demographic datasets for developing the CVI. The model was developed and tested on global cities with contrasting economies and validated by cross checking results with reliable open source surrogate data. The model provides an effective, adaptable, and cost-effective approach for vulnerability assessment and may provide information for policy makers to design appropriate resilience and adaptation efforts.

Seismic Fragility of RC Wall-Frame Buildings Using the DDBD Approach for Vulnerability Assessment

Seismic Fragility of RC Wall-Frame Buildings Using the DDBD Approach for Vulnerability Assessment

Current Status and Perspective of Vulnerability Assessment of Cyber-Physical Power Systems Based on Complex Network Theory

The increasing factors of uncertainty faced by the system are due to the deep coupling of the electric power cyber network and the physical network. Consequently, ensuring the efficient, secure, and stable operation of the cyber–physical power system (CPPS) has become a key concern. To achieve this, vulnerability assessment plays a crucial role, as it identifies and protects the vulnerable points of the system. The application of complex network theory to assess the vulnerability of CPPSs has garnered significant attention from scholars. This paper delves into the research connotation of vulnerability assessment for CPPSs, starting with the origin, definition, and classification of vulnerability. Subsequently, the assessment framework of vulnerability based on complex network theory is presented, and the status of current domestic and international research in this field is summarized. Furthermore, the interrelationship between system vulnerability and cascading failures is analyzed from the perspective of complex network theory. In conclusion, the ideas of CPPS coupling modeling in vulnerability assessment are summarized, the concept of situation awareness is introduced, and a prospective approach for dynamic vulnerability assessment is proposed. This approach is based on situation awareness combined with complex network theory. Security protection and optimal operation of CPPSs based on vulnerability assessment are also discussed, along with the assessment of vulnerability within integrated energy cyber–physical systems (IECPSs).

Automatic software vulnerability assessment by extracting vulnerability elements

Software vulnerabilities take threats to software security. When faced with multiple software vulnerabilities, the most urgent ones need to be fixed first. Therefore, it is critical to assess the severity of vulnerabilities in advance. However, increasing number of vulnerability descriptions do not use templates, which reduces the performance of the existing software vulnerability assessment approaches. In this paper, we propose an automated vulnerability assessment approach that using vulnerability elements for predicting the severity of six vulnerability metrics (i.e., Access Vector, Access Complexity, Authentication, Confidentiality Impact, Integrity Impact and Availability Impact). First, we use BERT-MRC to extract vulnerability elements from vulnerability descriptions. Second, we assess six metrics using vulnerability elements instead of full descriptions. We conducted experiments on our manually labeled dataset. The experimental results show that our approach has an improvement of 12.03%, 14.37%, and 38.65% on Accuracy over three baselines.

A Machine-Learning-Based Seismic Vulnerability Assessment Approach for Low-Rise RC Buildings

ABSTRACT Seismic vulnerability evaluation of existing buildings is essential to minimize the destructive impacts of earthquakes. Rapid visual screening (RVS) methods are simple and effective vulnerability assessment techniques to help quickly identify high-risk buildings for more detailed evaluations. Among various RVS methods, the Hassan–Sozen priority index (PI) is one of the simplest methods that can be used for low-rise reinforced concrete (RC) buildings. The PI relates simple, easily attainable geometric features of a building including number of stories, floor area, column area, and wall area to damageability. However, the relationship is overly simplified, and there is no absolute basis for defining damage classification boundaries that can be used to interpret the PI. Furthermore, given the lack of seismic parameters as inputs, the PI only allows for a relative evaluation of buildings in a specific region. To address these issues and develop a more broadly applicable RVS method, this study first proposes an improved PI evaluation method using machine learning techniques to define damage classification boundaries. Then, a new generalized RVS method is proposed that considers the PI input features and earthquake intensity measures to predict damage states. Data from six post-earthquake damage surveys (Duzce (1999), Bingol (2003), Nepal (2015), Taiwan (2016), Ecuador (2016), and Pohang (2017)) are used to train and evaluate the classification models. Two earthquake intensity features, modified Mercalli intensity and peak ground acceleration, are introduced to develop a new earthquake intensity aware RVS. The results of the proposed methodologies show a considerable improvement from the original PI with no judgment needed to define the damage classification boundaries.

Vulnerability of Buildings to Meteorological Hazards: A Web-Based Application Using an Indicator-Based Approach

Recent events have demonstrated the devastating impact of meteorological hazards on buildings and infrastructure. The possible effects of climate change on their frequency and intensity but also the rise in the value of assets may increase future risks significantly. It is crucial, therefore, for decision-makers to analyze these risks, focusing on the vulnerability of the built environment to reduce future consequences and the associated costs. However, limited studies focus on the vulnerability of buildings to meteorological hazards. The aim of the present paper is to introduce an indicator-based vulnerability assessment approach for buildings subject to three meteorological hazards (windstorms, heavy rainfall, and hail). The selection of vulnerability indicators (e.g., material, roof shape, etc.) was based on a thorough literature review. The results of an expert survey were analyzed using M-MACBETH software, and the Analytic Hierarchy Process (AHP) was used to weigh each indicator according to the expert opinions and to aggregate them into an index. A web-based application was developed that gives homeowners and other end-users the opportunity to assess the vulnerability of specific buildings by indicating the municipality, the building type, and other building characteristics. The web-application is publicly available and free of charge. The resulting index is a valuable tool for decision-makers, homeowners, authorities, and insurance companies. However, the availability of empirical damage data from real events could contribute to enhancing the performance of the presented approach.

The place of social transformation analysis in vulnerability assessment for climate adaptation planning in Upper West Region, Ghana: A review synthesis

AbstractClimate vulnerability could be influenced by transforming demographic, technological, cultural, political and economic factors, which cuts across global, regional, national and local scales. Such social transformations result in positive and negative outcomes, with implications for the adaptive capacities of resource‐poor households, especially those headed by women. However, these transformations are usually not integrated in climate vulnerability assessments. Based on insights from a stakeholders’ brainstorming workshop and the synthesis of information from traditional literature review, this paper contributes to better understanding of the intersections of social transformation with climate vulnerabilities in the Upper West Region of Ghana. The review indicates that the region is experiencing social transformation triggered by technological, demographic and cultural factors, with implications for climate resilience building. For example, compared to the last decade, there is now an increased use of mobile phones, resulting in improved access to e‐extension and climate‐smart agriculture services. At the same time, an emerging trend of land commodification is driving poor households to sell or lease farming lands. Within the context of these transformations, climate vulnerability is still assessed through approaches that mainly focus on a ‘static’ view of the extent of exposure to climate hazards and their impacts on rural livelihoods. A social transformation analysis that promotes a systematic investigation of the transforming factors is proposed as an effective approach for vulnerability assessment in climate adaptation planning. This approach provides critical reflections on, and sustainable resilience interventions for addressing both changing biophysical and social vulnerabilities of rural communities.

A Preventive Control Approach for Power System Vulnerability Assessment and Predictive Stability Evaluation

Early detection of cascading failures phenomena is a vital process for the sustainable operation of power systems. Within the scope of this work, a preventive control approach implementing an algorithm for selecting critical contingencies by a dynamic vulnerability analysis and predictive stability evaluation is presented. The analysis was carried out using a decision tree with a multi-parameter knowledge base. After the occurrence of an initial contingency, probable future contingencies are foreseen according to several vulnerability perspectives created by an adaptive vulnerability search module. Then, for cases identified as critical, a secure operational system state is proposed through a vulnerability-based, security-constrained, optimal power flow algorithm. The modular structure of the proposed algorithm enables the evaluation of possible vulnerable scenarios and proposes a strategy to alleviate the technical and economic impacts due to prospective cascading failures. The presented optimization methodology was tested using the IEEE-39 bus test network and a benchmark was performed between the proposed approach and a time domain analysis software model (EMTP). The obtained results indicate the potential of analysis approach in evaluating low-risk but high-impact vulnerabilities in power systems.

Flood vulnerability and risk assessment of historic urban areas: Vulnerability evaluation, derivation of depth‐damage curves and cost–benefit analysis of flood adaptation measures applied to the historic city centre of Tomar, Portugal

AbstractAround 45% of natural hazards reported worldwide are related to floods, and current indications show that exposure to floods and inherent losses will keep escalating. Historic centres are particularly vulnerable in this context due to the structural and material characteristics of the buildings and because they embrace social and cultural values that must be safeguarded. This article aims to contribute to this research area by presenting and discussing the application of an index‐based methodology specifically tailored to assess flood risk in historic urban centres. The historic city centre of Tomar, Portugal, an area that encompasses over 500 buildings and has a rich history of floods, is used here as a case study. Vulnerability data resulting from the application of the vulnerability assessment approach are then combined with flood hazard—that is, water velocity and depth obtained from flood peaks estimated for 20‐ and 100‐year periods of return—and used to identify the buildings at risk. Finally, a set of depth‐damage curves is derived and used here to carry out a cost–benefit analysis for different flood adaptation measures.

A fuzzy logic-based approach for groundwater vulnerability assessment.

Groundwater vulnerability assessment systems have been developed to protect groundwater resources. The DRASTIC model calculates the vulnerability index of the aquifer based on seven effective parameters. The application of expert opinion in rating and weighting parameters is the DRASTIC model's major weakness, which increases uncertainty. This study developed a Mamdani fuzzy logic (MFL) in combination with data mining to handle this uncertainty and predict the specific vulnerability. To highlight this approach, the susceptibility of the Qorveh-Dehgolan plain (QDP) and the Ardabil plain aquifers was investigated. The DRASTIC index was calculated between 63 and 160 for the Ardabil plain and between 39 and 146 for the QDP. Despite some similarities between vulnerability maps and nitrate concentration maps, the results of the DRASTIC model based on nitrate concentration cannot be verified according to Heidke skill score (HSS) and total accuracy (TA) criteria. Then the MFL was developed in two scenarios; the first included all seven parameters, whereas the second used only four parameters of the DRASTIC model. The results showed that, in the first scenario of the MFL modeling, TA and HSS values were respectively 0.75 and 0.51 for the Ardabil plain and 0.45 and 0.33 for the QDP. In addition, according to the TA and HSS values, the proposed model was more reliable and practical in groundwater vulnerability assessment than the traditional method, even using four input data.

Vulnerability assessment of coastal tourist attractions to oil spill stress based on a pressure-state-response framework: A case study of the Bohai Sea, China

The protection of coastal tourist attractions under oil spill stress requires a more detailed vulnerability assessment approach. In this study, an assessment model for estimating the vulnerability of coastal tourist attractions to oil spills along the Bohai Sea’s coastlines was developed, natural environmental and socioeconomic data were collected, and a geographic information system (GIS) was used. As many as 26 factors were selected for the vulnerability analysis of typical tourist destinations in the littoral area. All of the factors were grouped into different categories according to a pressure-state-response (PSR) framework (pressure parameters, state parameters, and response parameters), and a different rank of importance was assigned to each factor via the analytical hierarchy process. The comprehensive analysis results were calculated and visualized using GIS. The results show that the state and response together strongly affected the vulnerability of the various coastal tourist attractions, while the pressure had less effect on the vulnerability. There were obvious spatial variations among the different coastal tourist destinations. Overall, the coastal tourist attractions surrounding the Bohai Sea were characterized by a high oil spill pressure, high sensitivity, low emergency response capabilities, and high vulnerability. This study provides a reference for vulnerability assessment of tourist destinations under oil spill stress, provides a scientific decision-making basis for the protection of coastal tourist destinations, and is conducive to promoting the healthy development of the coastal tourism industry.

Macromechanism Approach for Vulnerability Assessment of Buildings on Shallow Foundations in Liquefied Soils

The damage caused by seismic shaking and liquefaction-induced permanent ground deformation has conventionally been assessed as two separate problems often by different engineers. However, the two problems are inherently linked, since ground shaking causes liquefaction, and liquefaction-induced soil softening affects ground shaking. Modelling both problems within a single numerical model is complex for both the engineer and the software, and most finite element software only have the capabilities to address one of them. To improve the estimates of the seismic performance of buildings on liquefiable soil, a new sub-structuring approach is proposed called the macro-mechanism approach. This approach allows the soil-liquefaction-foundation-structure interaction to be considered in a series of sub-models accounting for the major nonlinear mechanisms of the system at a macro level. The proposed approach was implemented in the open-source finite element software OpenSees and then applied to a case study of a building where significant liquefaction- and shaking-induced damage was observed after the 1999 Mw 7.4 Kocaeli Earthquake. The case study building was also simulated using two different commercial software programs, the finite difference software FLAC, and the finite element software PLAXIS, by two different research teams. A comparison between the results from the macro-mechanism approach compared to full numerical models shows that the macro-mechanism approach can capture the extent of the foundation deformation and provide more realistic estimates of the building damage than full approaches since the FLAC and PLAXIS models consider elastic elements for the building.

Analysis of Consumer IoT Device Vulnerability Quantification Frameworks

The increasing deployment of Internet of Things (IoT) devices in mission-critical systems has made them more appealing to attackers. Cyberattacks on IoT devices have the potential to expose sensitive data, disrupt operations, and even endanger lives. As a result, IoT security has recently gained traction in both industry and academia. However, no research has examined existing IoT vulnerability assessment frameworks in a systematic and comprehensive manner. To address this gap, this paper systematically reviews and analyses the research challenges and state-of-the-art IoT vulnerability assessment frameworks while taking into account both breadth and depth. The study provides insight into current IoT vulnerability assessment approaches, which is useful for ongoing efforts to characterise cybersecurity risks and manage IoT vulnerabilities. It will be of interest to a spectrum of readers, including those in the IoT research community, researchers in cybersecurity, risk and vulnerability management professionals, and others. By offering the latest perspective on the present IoT vulnerability assessment techniques, this study will raise IoT security awareness and facilitate research into IoT vulnerability assessment methodologies. The knowledge provided by this study will also be beneficial to future academics who are interested in the issues and solutions surrounding IoT security. The report also assists in understanding the research direction in IoT vulnerability assessment approaches, making it beneficial for those looking to create new methods for determining IoT vulnerabilities.

A Vulnerability Assessment Approach for Transportation Networks Subjected to Cyber–Physical Attacks

Transportation networks are fundamental to the efficient and safe functioning of modern societies. In the past, physical and cyber space were treated as isolated environments, resulting in transportation network being considered vulnerable only to threats from the physical space (e.g., natural hazards). The integration of Internet of Things-based wireless sensor networks into the sensing layer of critical transportation infrastructure has resulted in transportation networks becoming susceptible to cyber–physical attacks due to the inherent vulnerabilities of IoT devices. However, current vulnerability assessment methods lack details related to the integration of the cyber and physical space in transportation networks. In this paper, we propose a new vulnerability assessment approach for transportation networks subjected to cyber–physical attacks at the sensing layer. The novelty of the approach used relies on the combination of the physical and cyber space, using a Bayesian network attack graph that enables the probabilistic modelling of vulnerability states in both spaces. A new probability indicator is proposed to enable the assignment of probability scores to vulnerability states, considering different attacker profile characteristics and control barriers. A probability-based ranking table is developed that details the most vulnerable nodes of the graph. The vulnerability of the transportation network is measured as a drop in network efficiency after the removal of the highest probability-based ranked nodes. We demonstrate the application of the approach by studying the vulnerability of a transportation network case study to a cyber–physical attack at the sensing layer. Monte Carlo simulations and sensitivity analysis are performed as methods to evaluate the results. The results indicate that the vulnerability of the transportation network depends to a large extent on the successful exploitation of vulnerabilities, both in the cyber and physical space. Additionally, we demonstrate the usefulness of the proposed approach by comparing the results with other currently available methods. The approach is of interest to stakeholders who are attempting to incorporate the cyber domain into the vulnerability assessment procedures of their system.

GIS-Based Risk Assessment of Structure Attributes in Flood Zones of Odiongan, Romblon, Philippines

Flood triggered by heavy rains and typhoons leads to extensive damage to land and structures putting rural communities in crucial condition. Most of the studies on risk assessment focus on environmental factors, and building attributes have not been given attention. The five most expensive typhoon events in the Philippines were recorded in 2008–2013, causing USD 138 million in damage costs. This indicates the lack of tool/s that would aid in the creation of appropriate mitigation measure/s and/or program/s in the country to reduce damage caused by typhoons and flooding. Hence, this study highlights a structure vulnerability assessment approach employing the combination of analytical hierarchy process, physical structure attributes, and existing flood hazard maps by the local government unit. The available flood hazard maps were layered into base maps, and building attributes were digitized using a geographic information system. The result is an essential local scale risk map indicating the building risk index correlated to the structural information of each exposed structure. It was recorded that of 3094 structures in the community, 370 or 10.25% were found to be at moderate risk, 3094 (76.79%) were found to be high risk, and 503 (12.94%) were very high risk. The local government unit can utilize the resulting maps and information to determine flood risk priority areas to plan flood mitigation management strategies and educate people to improve the structural integrity of their houses. A risk map gives people an idea of what to improve in their houses to reduce their vulnerability to natural disasters. Moreover, the result of the study provides direction for future studies in the country to reduce loss and enhance structure resiliency against flooding.