Vulnerability Assessment Framework Research Articles

Quantifying household vulnerability to power outages: Assessing risks of rapid electrification in smart cities

As cities accelerate decarbonization through building electrification, the growing dependence on electrical systems introduces new vulnerabilities during power disruptions. While grid-level resilience has been widely studied, household-scale impacts of electrification remain poorly understood. In this study, we develop a vulnerability assessment framework that combines machine learning classification with high-resolution synthetic energy data from 129,000 U.S. single-family homes. Our two-stage approach first identifies household electrification levels with over 80% accuracy and then quantifies outage vulnerability using a composite risk index that incorporates electrification profiles, backup capabilities, and climate exposure. A simulated case study reveals that fully electrified households face significantly higher risks during winter storms, with a 60% greater vulnerability compared to mixed-energy homes. In contrast, solar-equipped electrified households exhibit enhanced resilience during heat waves, leveraging renewable energy resources to mitigate risks. By highlighting critical dependencies and adaptive capacities, our framework emphasizes the importance of energy diversity and distributed energy resources in reducing outage vulnerabilities. This scalable, non-intrusive methodology provides actionable insights for policymakers and urban planners to design climate-resilient urban energy systems.

A vulnerability assessment framework for the disproportionate collapse of RC frame structures subjected to multiple column removal scenarios

A vulnerability assessment framework for the disproportionate collapse of RC frame structures subjected to multiple column removal scenarios

A resilience-oriented approach to integrated energy management systems: Addressing energy conversion unit unavailability and cost efficiency

As integrated energy systems become increasingly complex, their vulnerability to energy conversion units unavailability poses a considerable threat to system resilience and cost efficiency. This research is motivated by the need to address the growing threat of energy conversion unit unavailability and its significant impact on both system performance and operational costs. The development of a comprehensive vulnerability assessment framework, coupled with the integration of machine learning techniques, is hypothesized to significantly enhance the resilience and cost-efficiency of integrated energy management systems when faced with various unavailability scenarios. A novel methodology is introduced to enhance integrated energy management systems, focusing on the critical role of diverse energy conversion units. The methodology includes a new vulnerability index to quantify the impact of energy conversion unit unavailability on system performance, alongside an energy conversion unavailability attack model from the malicious actor perspective. Results indicate that under worst-case energy conversion unavailability attack scenarios, integrated energy systems operational costs can increase by up to 26.19%. The proposed solution, incorporating a Deep Q-Network into the integrated energy management system, achieves an 85.24% F1-score and a 96.68% reduction in additional costs associated with energy conversion unavailability attacks. This research demonstrates the varied impacts of different energy conversion units on integrated energy systems and proposes an enhanced integrated energy management system framework that improves system resilience and cost efficiency.

An open and interoperable Knowledge Management framework for Climate Change Vulnerability Assessment

Climate change vulnerability assessment (CCVA) is crucial to understand how different regions, communities, and ecosystems are susceptible to the impacts of climate change. Through the identification of vulnerabilities, policymakers can prioritize adaptation efforts, allocate resources effectively, and develop risk mitigation strategies. Social and environmental scientists can design solutions for the development of climate-resilient regions. Nowadays, various approaches have been made available for CCVA, leading to increased knowledge along with the identification of challenges and pitfalls. Participatory approaches are required for the proper selection of vulnerability indicators and the interpretation of the assessment results, accompanied by the development of quality data assessment processes to reduce inconsistencies and uncertainties in the provided data. Openness and interoperability of data and processes have to be promoted to enable the extensibility, reproducibility and comparability of the produced results. In the current work, we develop a framework to enable open and interoperable CCVA, taking advantage of a knowledge management infrastructure and specifically a Knowledge Graph that tracks information for various indicators around the Sustainable Development Goals. We follow an indicator-based vulnerability assessment approach for various temporal and spatial scales, while considering hazard-independent and hazard-dependent analysis. Indicative analysis results are provided in national, regional and local scale.

A spatial assessment of agricultural vulnerability to climate change using multidimensional data in Maharashtra state of India

This study aims to examine district-level agricultural vulnerability in the semi-arid and drought-prone state of Maharashtra. Using a multidimensional approach, suitable indicators for 34 districts were selected under exposure, sensitivity and adaptive capacity, aligning with the IPCC 2007 framework for vulnerability assessment. The analysis used temperature and rainfall data spanning 50 years (1968–2017). It also incorporated area and yield data from 2011 to 2020, along with other indicators for the year 2019–20. The composite agricultural vulnerability index (CAVI) score was obtained using proper normalization and assigning weights. The study revealed that the Marathwada and Vidarbha regions reported higher potential crop losses. Among the districts, Sangali was the most exposed (0.57), while Jalgaon was the least exposed (0.31) to climate variations. Ahmednagar exhibited the highest sensitivity (0.70), while Nagpur showed the lowest sensitivity (0.34) to climate change. Regarding adaptive capacity, Pune demonstrated the highest resilience (0.57) and Sindhudurg displayed the lowest score (0.23). CAVI highlighted that the Konkan region, along with western Maharashtra and eastern parts of Vidarbha, exhibited relatively lower vulnerability to climate change, due to factors such as lower potential crop loss and better adaptive capacity. In contrast, Marathwada and western Vidarbha regions were more vulnerable, facing significant crop production losses and higher exposure to climate variability. Hence, to build resilience to climate change and reduce crop production losses, crop planning needs to be adjusted according to the climatic conditions in these regions.

Underlying drivers of coral reef vulnerability to bleaching in the Mesoamerican Reef

Coral bleaching, a consequence of stressed symbiotic relationships between corals and algae, has escalated due to intensified heat stress events driven by climate change. Despite global efforts, current early warning systems lack local precision. Our study, spanning 2015–2017 in the Mesoamerican Reef, revealed prevalent intermediate bleaching, peaking in 2017. By scrutinizing 23 stress exposure and sensitivity metrics, we accurately predicted 75% of bleaching severity variation. Notably, distinct thermal patterns—particularly the climatological seasonal warming rate and various heat stress metrics—emerged as better predictors compared to conventional indices (such as Degree Heating Weeks). Surprisingly, deeper reefs with diverse coral communities showed heightened vulnerability. This study presents a framework for coral reef bleaching vulnerability assessment, leveraging accessible data (including historical and real-time sea surface temperature, habitat variables, and species composition). Its operational potential lies in seamless integration with existing monitoring systems, offering crucial insights for conservation and management.

Cyber Security of Smart-Grid Frequency Control: A Review and Vulnerability Assessment Framework

Smart grid involves application of information and communication technology (ICT) for monitoring, protection, operation and control of interconnected power systems under various scenarios. Smart grid control (SGC) is an important aspect that is constantly subjected to various vulnerabilities, threats and attacks under central and distributed control architectures. Cybersecurity of SGC, especially, load frequency control (LFC), is an important issue that is addressed in this article. The state-of-the-art in cybersecurity and attacks on SGC and intensive literature review is discussed with a comprehensive list of references on LFC. The authors present a part of their own work carried out on a systematic vulnerability assessment (VA) framework that can be used to identify weak points in the LFC system. The proposed methodology is explained for VA of the standard 39-bus New England test system and the 96-bus reliability test system (RTS) to illustrate the concept of cybersecurity and VA of smart grid LFC.

Dynamic vulnerability assessment framework for urban flood disasters considering real-time population changes

ABSTRACT Flood vulnerability assessment is an important means to cope with urban flood disasters. However, urban population mobility and land-use changes bring significant uncertainty to vulnerability assessments. In order to respond to the refined and dynamic needs of urban flood disaster prevention and control, a multi-scale and multi-categorized urban flood dynamic vulnerability assessment framework that considers real-time population changes was proposed in this study. The framework used the comprehensive function of exposure, sensitivity, and coping ability under population dynamic changes to characterize urban flood dynamic vulnerability, and constructed urban flood disaster vulnerability assessment models at both grid and regional scales. The results show that population mobility has a significant impact on the vulnerability of urban flood disasters. The vulnerability of flood disasters in Zhengzhou City is highest at 8:00 and 18:00, and high-vulnerability areas are mainly concentrated in residential areas in the central part of the city and main transportation channels. From 10:00 to 15:00, the high-vulnerability areas migrate to public service areas in the eastern part of Zhengzhou City. In the evening, the vulnerability degree of flood disasters is relatively low. The research results can provide a theoretical basis for urban flood managers to prevent and control flood disasters.

Vulnerability-based seismic resilience and post-earthquake recovery assessment for substation systems

The function of substation systems is significantly influenced by earthquakes. To assess the seismic resistance of substation systems and improve post-earthquake recovery efficiency, this study constructs a multi-module framework for seismic vulnerability assessment of substations. Based on the equipment functional status analysis module, a functional network model was established considering the structural function and electrical load of substation systems. The system functional state matrix was obtained through Monte Carlo simulation, and the seismic vulnerability was assessed from the aspects of electrical transmission reliability and the importance of power users. Subsequently, a substation functional index was established based on seismic vulnerability parameters. Post-earthquake functional recovery strategy analysis frameworks were constructed to quantitatively evaluate the recovery process. A stepped functional time-varying function was proposed through iterative analysis, thereby converting the probability parameters of functional status into seismic resilience deterministic indices. Through seismic resilience analysis of a typical 220 kV step-down substation, seismic vulnerability curves for both electrical transmission and power users were obtained. Furthermore, the seismic sensitive intervals of the substation and key equipment for post-earthquake recovery were clarified. Notably, the seismic resilience level and optimal recovery strategy of the typical 220 kV step-down substation were determined.

A Bayesian network-based probabilistic framework for seismic vulnerability assessment of road networks

After an earthquake, roadside debris from collapsed buildings, structural damage to bridges, and large-scale emergency evacuations are major obstructions to the connectivity of road networks (RNs). However, few studies have comprehensively examined the impact of all these obstructions on the seismic vulnerability of RNs while considering uncertainties. In this paper, a novel probabilistic framework is proposed for seismic vulnerability assessment of RNs based on Bayesian networks (BNs). Herein, the vulnerability of RNs is defined as a combination of the failure probability of a road link and its conditional consequences in terms of network dis-connectivity. The framework can be used to assess the effects of debris distributions, the functionality losses of bridges, and evacuation flows on the failure probabilities of road links and the seismic vulnerability of RNs. Uncertainties in the post-earthquake damage states of buildings and bridges, building collapse types, building-to-road distances, and travel costs are explicitly considered and propagated in the framework using Monte Carlo (MC) simulations. The MC results are used to establish prior probabilities, and based on bidirectional inference with BNs, it is possible to assess the seismic vulnerability of RNs and identify critical roads. The proposed methodology is verified through a real-world RN in China.

Unveiling economic resilience: exploring the impact of financial vulnerabilities on economic volatility through the economic vulnerability index

While earlier studies have explored the relationship between economic vulnerability and economic resilience, they have repeatedly overlooked the significance of financial vulnerabilities within the economic vulnerability index, and the effects of financial and economic vulnerabilities on economic volatility. An attempt is made in this study to close this research gap by conducting a detailed analysis of the relationship between financial vulnerabilities, economic vulnerability and economic volatility, stressing the significance of tweaking prevailing vulnerability indices to fully encapsulate its multidimensional nature in developing countries. Employing panel data for 142 countries over the 2002 to 2022 period and a robust econometric approach like the Driscoll and Kraay fixed effect method, the study reveals that financial vulnerabilities yield significant coefficients to influence economic volatility, thereby accentuating their significance in the Economic Vulnerability Index. Sub-group analyses reinforce the need for incorporating financial variables in vulnerability investigations. Moreover, the causality tests reveal that all the variables and indices meant to capture the economic and financial vulnerabilities Granger causes economic volatility across the sample. In essence, this study fills a critical gap in existing research by demonstrating, that financial vulnerabilities significantly influence economic volatility, underscoring the imperative of integrating financial variables into vulnerability assessments for policymakers and scholars focusing on sustainable development. This study contributes to a broader understanding of economic vulnerability by highlighting the crucial role of financial vulnerabilities in driving economic volatility, suggesting a fundamental reconsideration of existing vulnerability assessment frameworks for policymakers and researchers focused on sustainable development frameworks. By uncovering the causal relationship between financial vulnerabilities and economic volatility across a diverse set of countries, the findings underscore the imperative of integrating financial factors into vulnerability investigations to enhance resilience and stability in developing economies.

Vulnerability analysis of socio-ecological systems on small islands from the perspective of fisheries and tourism: A case study of Zhoushan Gouqi Island

Small island social-ecological systems are increasingly vulnerable to the combined impacts of human activities and environmental changes, making focused vulnerability analyses critical for sustainable governance. In this study, Gouqi Island, a remote island, was selected as the research site to establish a comprehensive vulnerability assessment framework based on the Vulnerability-Sensitivity-Danger (VSD) model. To account for potential interdependencies among indicators, a fuzzy integration method was employed to quantitatively assess vulnerability trends from 2016 to 2022. The analysis revealed that vulnerability reached its lowest point in 2018, peaked at 0.876 in 2020, and remained above 2016 levels through 2022, despite a slight decline. Additionally, machine learning techniques were used to identify key factors influencing vulnerability, highlighting indicators such as D24 (proportion of fisheries and tourism output), D41 (number of tourists), and D52 (aquaculture production) as particularly significant, though distinct from traditional sensitivity indicators. These findings inform the identification of critical factors affecting the island's sustainable development and lead to the proposal of adaptive governance strategies. The insights from this study provide valuable guidance for promoting sustainable development in similar remote islands.

Climate change vulnerability assessment in the new urban planning process in Tanzania

Climate change vulnerability assessment is an essential tool for identifying regions that are most susceptible to the impacts of climate change and designing effective adaptation actions that can reduce vulnerability and enhance long-term resilience of these regions. This study explored a framework for climate change vulnerability assessment in the new urban planning process in Jangwani Ward, Tanzania. Specifically, taking flood as an example, this study highlighted the steps and methods for climate change vulnerability assessment in the new urban planning process. In the study area, 95 households were selected and interviewed through purposeful sampling. Additionally, 10 respondents (4 females and 6 males) were interviewed for Focus Group Discussion (FGD), and 3 respondents (1 female and 2 males) were selected for Key Informant Interviews (KII) at the Ministry of Lands, Housing and Human Settlements Development. This study indicated that climate change vulnerability assessment framework involves the assessment of climatic hazards, risk elements, and adaptive capacity, and the determination of vulnerability levels. The average hazard risk rating of flood was 2.3. Socioeconomic and livelihood activities and physical infrastructures both had the average risk element rating of 3.0, and ecosystems had the average risk element rating of 2.9. Adaptive capacity ratings of knowledge, technology, economy or finance, and institution were 1.6, 1.9, 1.4, and 2.2, respectively. The vulnerability levels of socioeconomic and livelihood activities and physical infrastructure were very high (4.0). Ecosystems had a high vulnerability level (3.8) to flood. The very high vulnerability level of socioeconomic and livelihood activities was driven by high exposure and sensitivity to risk elements and low adaptive capacity. The study recommends adoption of the new urban planning process including preparation, planning, implementation, and monitoring-evaluation-review phases that integrates climate change vulnerability assessment in all phases.

System-wide vulnerability of multi-component software

In software systems comprised of many interconnected components, the vulnerability of each component will affect the vulnerability of other components and of the system as a whole. Existing techniques allow the quantification of the vulnerability of individual components taken singly, but the assessment of their vulnerability when they are interconnected or interdependent remains a challenge. The present work addresses this problem with a novel System-Wide Vulnerability Assessment (SWVA) framework for interconnected software components, based on an Associated Random Neural Network (ARNN) that estimates the system-wide vulnerability of all software components from known local vulnerabilities of individual components, and from their interconnections. The ARNN uses a problem-specific weight initialization, and learns from existing software system examples with a gradient-based deep learning algorithm. The ARNN is then used to assess the vulnerability of hitherto unseen software systems. The performance of the proposed ARNN-based SWVA framework is evaluated and compared against several well-known machine learning techniques on 13 different versions of a real-world software system with up to 11 components. The experimental results show the superior performance of the ARNN achieving above 85% median accuracy and good high scalability with respect to the number of connected software components.

Conceptual Framework for Flood Vulnerability Assessment

Conceptual Framework for Flood Vulnerability Assessment

A meta-ensemble machine learning strategy to assess groundwater holistic vulnerability in coastal aquifers

Assessing the vulnerability of groundwater in coastal aquifers is crucial for mitigating risks associated with seawater intrusion and anthropogenic impacts. This study introduces an innovative machine learning (ML)-enhanced methodology that synergizes the strengths of two established vulnerability assessment frameworks, DRASTIC and GALDIT. The hybrid approach overcomes the limitations of each framework—DRASTIC's inadequacies in coastal settings and GALDIT's limited consideration of agricultural and industrial impacts. Utilizing advanced decision tree-based ML algorithms—Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), and Random Forest (RF)—this research was conducted in the Azarshahr Plain, NW Iran. Model efficacy was validated using Pearson's correlation coefficient (r) and distance correlation (DC), with nitrate (NO3−) and total dissolved solids (TDS) serving as proxies for evaluating the DRASTIC and GALDIT models, respectively. The original DRASTIC indices exhibited weak correlations with NO3− (r = 0.24, DC = 0.25), but ML-enhanced models, particularly AdaBoost, showed significant improvements (r = 0.78, DC = 0.79). Similar enhancements were observed with GALDIT, where correlations improved markedly with AdaBoost integration. A sophisticated second-level AdaBoost meta-ensemble was developed to integrate enhanced DRASTIC and GALDIT assessments, achieving superior correlation metrics (r = 0.80, DC = 0.84 for NO3−; r = 0.82, DC = 0.83 for TDS). These results underscore the effectiveness of an integrated ML-based approach in advancing beyond traditional vulnerability assessment methods, providing a more comprehensive, accurate, and robust evaluation of coastal aquifer vulnerability.

A framework for structural vulnerability assessment with consideration of seismic uncertainty

In recent decades, performance-based earthquake engineering (PBEE) has been dedicated to the structural vulnerability assessment (SVA) as a critical research area for seismic decision-making. Among the various approaches to SVA, the classical linear regression method (LRM) has gained wide and prominent use. However, the conventional LRM relies on the quantile regression method (QRM) to approximate a deterministic probability density distribution (PSD) of structural fragility against a specific intensity measure (IM) associated with an earthquake. This approach may lack credibility when evaluating a newly occurred seismic event due to its neglect of the seismic uncertainty of stochastic ground motion. To address this concern, this present study introduces a meticulously designed framework that integrates physics-based and machine learning models to probabilistically assess seismic fragility while accounting for seismic uncertainty. Additionally, novel fragility hyperparameters are estimated within this framework. The study provides a detailed elucidation of the underlying design philosophy and the mechanism of translation. In order to validate the proposed method, both the LRM and the new framework are applied to a six-story frame structure, utilizing a novel fully-automatic batch processing approach that combines APDL and coding languages for structural analysis. The comprehensive validation cases convincingly confirm the exceptional capability and significant advantages of the proposed framework in achieving rapid probabilistic seismic fragility assessment, which is of paramount importance to modern PBEE.

Vulnerability to climate change of coastal mangrove-dependent communities in the Tana Delta, Kenya: a local perspective

Coastal mangrove-dependent communities face various risks due to climate change, thus rendering them vulnerable. This study explored the vulnerability of these communities in the Tana Delta in Kenya using an indicator-based vulnerability assessment framework to better understand their exposure, sensitivity and adaptive capacity. Data was collected through household surveys (n = 377), focus group discussions (FGDs), and key informant interviews (KIIs). The analysis revealed mean vulnerability indices at the sub-location level ranged from 0.850 for Kipini to − 0.913 for Kilelengwani and − 2.702 for Ozi. The statistically significant indicators of vulnerability were (i) exposure—high temperatures and rainfall changes; (ii) sensitivity—household numbers, head of household’s age, dependents’ education, and dependents’ employment; and (iii) adaptive capacity—ownership of assets, access to community infrastructure and services, condition/quality of houses, total income, and alternative sources of income. The findings highlight the need for adaptation strategies that ensure greater financial assets supported by education and skills enhancement to utilize existing opportunities. Attention to community infrastructure and services is crucial. Policy should focus on financial, physical, and human assets to reduce community vulnerability alongside the continued conservation and management of mangrove resources. The results will help policymakers in addressing the impacts of climate change and benefit households in the study area. These insights can be applied to regions with similar climate conditions and livelihood systems.

Framework for Seismic Vulnerability Assessment of Nonstructural Elements Inside an Inflatable Lunar Habitat

The pursuit of knowledge and curiosity are once again driving humanity to seek a human presence on extraterrestrial bodies. While numerous challenges must be overcome on the moon to achieve this goal, the moonquake hazard has often been overlooked. Using NASA’s Moon to Mars Architecture requirements for surface habitation and accounting for the midterm/long-term mission duration, a framework for vulnerability assessment is developed for essential nonstructural elements (NSEs) inside an inflatable habitat subjected to moonquakes. The aim of this study is to develop an approach to assess the vulnerability of typical NSEs that support essential equipment, such as the environmental control and life support system, under a paucity of information related to the seismic hazard. We also emphasize that the launch dynamic environment and lunar seismic environment exhibit notable differences in their characteristics, which can lead to higher lateral acceleration levels than initially expected. This acceleration may be linked to high-occurrence seismic events. However, due to the lack of information regarding the frequency of these events, the risk of NSEs being subjected to unforeseen loads increases. It is imperative to equip future lunar habitats with seismic mitigation measures until additional seismometers are deployed near upcoming surface human outposts.

A novel framework for the spatiotemporal assessment of urban flood vulnerability

Considering the pressing Urban Flood (UF) challenges faced by developing countries, the assessment of Urban Flood Vulnerability (UFV) assumes paramount importance in comprehending the detrimental impacts of UF on urban environments, thereby facilitating effective UF management. Addressing a notable gap in previous UFV research, which predominantly focused on spatial characteristics while overlooking temporal dynamics, we introduce a novel framework for spatiotemporal UFV assessment, by presenting a multiplicative equation to elucidate the interaction between Urban Flood Susceptibility (UFS) and Vulnerable Entities (VE). Employing this framework in the Great Bay Area (GBA), by considering the composition of the UFV database, we leveraged Machine Learnings and the Urban Vitality Index for quantifying the UFS and VE, respectively. SHapley Additive exPlanations was employed to quantify the local contributions of various factors to UFV. We found (1) a discernible upward trajectory in UFV across the GBA from 2012 to 2020, coupled with an expansion in spatial scope, indicative of persistent challenges in the future, and (2) the impervious surface percentage emerges as the primary contributor to UFV, exhibiting spatial heterogeneity reflective of regional environments. We believe this study has the potential to significantly contribute to addressing UF challenges and fostering the development of sustainable cities.