Vulnerability Assessment Approach Research Articles

Seismic vulnerability assessment of buildings using rapid visual screening for hybrid UMNN-RFO approach

This chapter proposes a hybrid approach for seismic vulnerability assessment of buildings using rapid visual screening (RVS). The Unconstrained Monotonic Neural Network (UMNN) and Red Fox Optimisation (RFO) are combined to create the hybrid technique, thus, the term ‘UMNN-RFO approach’. The UMNN algorithm predicts the RVS score and the RFO algorithm optimizes the RVS score. The RVS methods via Google Maps are being used to examine the Andaman and Nicobar Islands because it is one of the main earthquake prone regions in the world. A score was given after a screening that looked at Reinforced Concrete moment-resisting frames, load-bearing structures, steel frames (SF) and wooden frames (WF). By then, the MATLAB platform will have the proposed model implemented. The proposed approach outperforms all current techniques, including Granular Computing Artificial Neural Network (GrC-ANN), Particle Swarm Optimization-Back Propagation Neural Network (PSO-BPNN) and Genetic Algorithm-Artificial Neural Network (GA-ANN). The proposed method’s accuracy is 90%, determination coefficient is 0.91 and the Root Mean Square error is 0.27%. From the result, the proposed method concluded that older structures are more likely to sustain greater damage shortly than more recently built ones based on the score.

GIS-DRASTIC integrated approach for groundwater vulnerability assessment under soil erosion hot spot areas in Northern Pakistan

GIS-DRASTIC integrated approach for groundwater vulnerability assessment under soil erosion hot spot areas in Northern Pakistan

Flood Vulnerability Index Based on Indicator Approach in Assessing Flood Risk in Selangor, Malaysia

Floods are one of the most common and damaging natural disasters, claiming lives, and causing significant economic damage. Urbanization can increase the risk of flooding due to increased water runoff and volume. This study focuses on a flood vulnerability assessment approach as a planning tool by combining the Flood Vulnerability Index (FVI) with variables available in urban development areas. This index was created by combining the variables of three components: exposure, susceptibility, and resilience. FVI is a critical element in risk management and flood damage assessment. Vulnerability is described in FVI research as the amount of damage that can be predicted under conditions of exposure, susceptibility, and resilience. Vulnerability studies frequently use an indicator-based vulnerability assessment to estimate the complexity and degree of risk. FVI reflects the vulnerability of urban areas. Secondary data were used to obtain variables for the social and economic components. A quantitative method was used to calculate the correlation of each variable with flooding via the Statistical Package for the Social Sciences (SPSS). The indicators are categorized as exposure, susceptibility, and resilience, and these indices were created using normalization, weighting, and aggregation of the indicators. To determine the flood vulnerability in Selangor, a small number of indicators were selected to quantitatively assess the flood risk in Selangor. Based on calculated FVI, the vulnerability of the districts in Selangor has been ranked and categorized from high to very low. The result shows Gombak and Klang are the most vulnerable districts in Selangor to flooding because of their high exposure to flooding. The results of this study could serve as a reference tool for future flood risk mitigation efforts in Selangor.

Cyber-Physical Modeling and Vulnerability Assessment of Substations for Transmission System Operator

In bulk power systems, the majority of interaction between cyber and physical components takes place in substations. However, in most of the cyber–physical power system models the physical layer is typically done using the bus-branch (BB) model, where each substation is considered as a single node. This approach will not capture the details of the cyber layer. This paper proposes a framework to model the substations using node-breaker (NB) models for physical system representation so that the detailed station configurations, the current and the voltage transformer positions, arrangements of protective relays, bay control units and associated communication infrastructure within the substations and its dependencies on the physical elements can be captured effectively using a single cyber–physical graph. Keeping a transmission system operator in view, who does not use power flow and security assessment tools for station operations and maintenance, a vulnerability assessment approach is proposed to assess the risk using some representative attack scenarios. The proposed approach is demonstrated using WECC 3-machine system for breaker and half station configuration. The attack scenarios are developed based on the real substation configuration and the adversary’s ability to understand the substation protection and BI/BO operations.

Data driven food fraud vulnerability assessment using Bayesian Network: Spices supply chain

Recognizing the vulnerabilities that arise in the spices' supply chain for food fraud, determining which products and food fraud types to assess is crucial for ensuring food quality and food safety. In this study, we developed a data driven food fraud vulnerability assessment approach based on a Bayesian Network (BN) and Failure Modes and Effects Analysis (FMEA) to predict the food fraud vulnerability level for products entering Europe including the food fraud types and potential adulterants for each step in the supply chain. The BN model was developed using a dataset based on spice-related fraud cases reported in the European Union (EU) Rapid Alert System for Food and Feed (RASFF) over the period 2005–2020. Three use cases were explored in the study: chilli, black pepper, and turmeric. The model showed a prediction accuracy higher than 95%. The vulnerability factors in the spices’ supply chain having the highest prediction accuracy for fraud are closely associated with the product concerned, the site of intervention and the country of origin of the product. A food fraud vulnerability assessment approach developed in this study could support the food industry and authorities to be more efficient in resource allocation for monitoring and verification whilst maximising their opportunity in detecting a fraudulent product.

Introducing a novel approach for assessment of groundwater salinity hazard, vulnerability, and risk in a semiarid region

The application of machine learning approaches to improve groundwater salinity risk mapping is limited despite all potential advantages. Therefore, there is an ongoing need for investigations that present new techniques, like machine learning, validated against conventional methods. These advances are particularly important for arid and semiarid regions, such as the Bakhtegan Basin (southern Iran), where groundwater use may far outweigh recharge, but groundwater management improvements are essential. To address these needs, groundwater salinity hazard, vulnerability, and risk maps were investigated using an integrated application of statistical (i.e., frequency ratio and statistical index models), machine learning (i.e., random forest and classification and regression trees algorithms), and decision-making models (fuzzy analytic hierarchy process (FAHP)). Results showed that the risk of groundwater salinity was high in the central areas of the region as well as at the margins of Bakhtegan Lake and irrigated farming lands. Based on the modeling results in the testing phase, it was found that the frequency ratio and random forest models exhibited better performance, with Nash–Sutcliffe efficiency metrics of 0.73 and 0.70, respectively, compared to the classification and regression trees and statistical index models, which had Nash–Sutcliffe efficiency metrics of 0.65 and 0.63, respectively. The innovative techniques developed in the current work accurately identified variables such as normalized difference salinity index, distance from mines, and land use associated with the highest weight values (0.59, 0.14, and 0.11, respectively) compared to other variables to identify groundwater salinity areas of vulnerability. The application of the fuzzy analytic hierarchy process method in developing vulnerability maps, integrating them with hazard maps, and developing groundwater salinity risk maps was innovative in this study. The innovative approach is transferrable for groundwater salinity susceptibility, vulnerability, and risk assessments globally in the study area and similar settings.

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).

Approaches for Groundwater Vulnerability Assessment in Relation to Pollution Potential: A Critical Evaluation and Challenges

Abstract Escalated groundwater contamination around the world developed the idea of aquifer vulnerability, which has widely been employed globally to safeguard groundwater from contamination. Several extensive studies have been done to evaluate the degree of groundwater vulnerability using various techniques. However, a comprehensive assessment of existing literature in order to identify major research gaps associated with aquifer vulnerability is still required. The current study provides a critical overview of several groundwater risk assessment methodologies, including the GIS-based DRASTIC and modified DRASTIC methods, application of Artificial Intelligence (AI) and Machine Learning techniques, and integrated approach for both intrinsic and specific vulnerability assessment. The study discusses validation of DRASTIC model and its comparison with a few other methodologies as well. The paper also highlights the international approach (time dimension in groundwater vulnerability, integrated process based and isotopic studies, etc.) that needs to be practiced rigorously on a larger scale in India. A descriptive summary of the literature indicates that a multidisciplinary and integrated framework such as understanding contaminant type, uncertainties in vadose zone need to be implemented in a much more comprehensive way, especially in India. A conceptual framework that is both reasonably adaptable and scientifically sound must be created for specific vulnerability assessment.

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.