Vulnerability Assessment Research Articles (Page 1)

ABSTRACT Climate hazards can escalate into humanitarian disasters. Understanding their trajectories—considering hazard intensity, human exposure, and societal vulnerability—is essential for effective anticipatory action. The International Disaster Database (EM-DAT) is the only freely available global resource of humanitarian disaster records. However, it has imprecise geocoded information, which severely constrains its integration with spatial climate and socioeconomic data, limiting its use for climate impact research and policy planning. Here, we present Geo-Disasters (https://doi.org/10.5281/zenodo.15487667), a database that provides geocoded locations of 9,217 climate-related disasters reported by EM-DAT from 1990 to 2023, along with an open, reproducible framework for updating (https://doi.org/10.6084/m9.figshare.29125907.v1). Our method remains accurate even when only region names are available and includes matching quality flags to assess reliability. The augmented EM-DAT enables integration with other geocoded data, supporting machine learning applications and cross-domain analyses of climate risks, vulnerabilities, and adaptation deficits. By making more extreme events available, Geo-Disasters aims to bridge critical data gaps in global climate-hazard risk assessment and to inform more equitable adaptation planning.

Vibrations caused by construction activities, transportation, and industrial sources can adversely affect buildings, especial ly those constructed from masonry or large prefabricated elements. In order to ensure structural safety and environmental protection, it is necessary to assess the impact of such vibrations in both the design and diagnostic stages. While full dynamic analysis provi des accurate results, it is often impractical for routine assessments due to the complexity of modeling, lack of vibration data, or the urgency of the evaluation process. In response to this need, simplified procedures have been developed in the Polish engineer ing practice in the form of Dynamic Impact Scales SWD - I and SWD - II. The SWD - I and SWD - II scales offer an approximate yet practical method for evaluating vibration - induced structural damage in buildings, based on empirical classification into five vibration zones ranging from "imperceptible" to "structural failure." The classification is derived from the measured peak va lues of ground acceleration and frequency, and the structural characteristics of the building. The SWD - I scale applies to compact, regular - shaped masonry or panel buildings of up to two storeys, while SWD - II is designed for more general low - rise buildings of up to five storeys, with certain limitations on height - to - width ratios. Both scales are based on engineering judgement and are aligned with the framework of the Polish standard PN - B - 02170:2016. This paper presents a detailed description of both scales, including the criteria for their use, methodology for assigning buildings to the correct zone, and proper sensor placement to capture representative vibration input. A comparative example is provided to demonstrate how a real - world vibration scenario (induced by a nearby compressor) can be evaluated using the SWD - II scale. The case illustrates that even a relatively small source can lead to measurable vibrations that may affect building integrity depending on the frequency content and local amplificati on effects. The results confirm that SWD scales can serve as reliable screening tools for preliminary assessment of structural vulnerability to environmental vibrations. Although not a substitute for detailed numerical modeling in complex or high - risk cases, the SWD approach provides a valuable, cost - effective method for engineers, designers, and decision - makers engaged in environmental impact assessments or construction planning. In addition, the scales can be used to support regulatory complian ce and community communication in vibration - sensitive areas. The use of SWD - I and SWD - II scales fills a methodological gap by offering an intermediate level of assessment between qualitative expert judgement and full - scale structural simulations. This contribution aims to bring these effective tools to a broader international audience and encourage their adaptation or furthe r development in the context of local regulatory frameworks and engineering standards.

This study presents a comparative review of health system resilience in Mauritius and Madagascar amid growing environmental stressors driven by climate change. As small island developing states in sub-Saharan Africa, both countries face increased health risks from extreme weather events, shifting disease patterns, and food insecurity. This review highlights the disproportionate impact on vulnerable populations, particularly women and girls, emphasising the need for anticipatory and flexible health systems to manage these challenges effectively. Methodologically, the study synthesises evidence from government and international agency assessments, peer-reviewed literature, and recent policy reports focused on health system adaptation frameworks. Special attention was paid to the World Health Organisation’s Vulnerability and Adaptation Assessment applied in Mauritius and the Climate-Smart Public Health model piloted in Madagascar. Thematic document analysis was conducted to assess governance, surveillance, infrastructure, community engagement, and equity. The results reveal contrasting yet complementary approaches. Mauritius benefits from robust institutional capacity, universal health coverage, and an advanced Early Warning Alert and Response System anchored in an equally robust multi-sectoral partnership framework. Despite resource limitations, Madagascar has demonstrated innovation through data-driven surveillance, artificial intelligence, and targeted infrastructure upgrades, supported by international cooperation. However, both countries share key lessons on the importance of predictive surveillance, multi-sectoral governance, and community participation in building resilient health systems. In conclusion, this review highlights that effective adaptation requires context-specific, equity-focused strategies that merge strong governance with technological innovation. Additionally, sustained financing, gender-responsive policies, and enhanced inter-sectoral collaboration are critical for future resilience. These insights offer valuable guidance for health systems in vulnerable settings that face escalating climate hazards.

This paper presents a systematic review of research (2020–2025) on the role of Large Language Models (LLMs) in cybersecurity, with emphasis on their integration into Big Data infrastructures. Based on a curated corpus of 235 peer-reviewed studies, this review synthesizes evidence across multiple domains to evaluate how models such as GPT-4, BERT, and domain-specific variants support threat detection, incident response, vulnerability assessment, and cyber threat intelligence. The findings confirm that LLMs, particularly when coupled with scalable Big Data pipelines, improve detection accuracy and reduce response latency compared with traditional approaches. However, challenges persist, including adversarial susceptibility, risks of data leakage, computational overhead, and limited transparency. The contribution of this study lies in consolidating fragmented research into a unified taxonomy, identifying sector-specific gaps, and outlining future research priorities: enhancing robustness, mitigating bias, advancing explainability, developing domain-specific models, and optimizing distributed integration. In doing so, this review provides a structured foundation for both academic inquiry and practical adoption of LLM-enabled cyberdefense strategies. Last search: 30 April 2025; methods followed: PRISMA-2020; risk of bias was assessed; random-effects syntheses were conducted.

In this study, a computational platform is developed for fragility assessment of reinforced concrete (RC) bridges using a damage index (DI)-based approach. The platform integrates a reliability analysis in conjunction with sophisticated nonlinear finite element (FE) analysis. In this process, the first-order reliability method enables efficient probabilistic evaluation, while the FE analysis provides DI values that directly quantify bridge damage condition. The platform was applied to a hollow RC bridge pier under cyclic loading, and fragility curves were derived for multiple damage levels calibrated by experimental thresholds. Compared with Monte Carlo simulation, the proposed platform achieved similar accuracy with far fewer analyses, requiring only 80 runs instead of 2,711. A parametric study further investigated the effect of threshold uncertainty on fragility curves. These results demonstrate the potential of the proposed platform as an efficient tool for reliability-based vulnerability assessment and the derivation of seismic fragility curves.

This study analyses changes in land cover (LC) within the distribution boundaries of the aquifers in the Sitnica River Basin, Kosovo. The basin is characterised by an almost equal proportion of intergranular aquifers – from low to high permeability across flat terrains, and non-porous formations (aquitard or aquiclude). Using a GIS-based analysis, the CORINE Land Cover data were combined with the distribution of the aquifers to assess the spatial and temporal changes and determine their extent and direction. The results indicate a significant expansion of artificial surfaces, increasing from 110.07 km² in 2000 to 219.75 km² in 2018. The largest changes occurred in the groundwater-rich intergranular aquifers, where the built-up areas almost doubled from 99.15 km² to 197.67 km². This expansion is closely linked to the increase in urban flooding observed in recent years. Urban planning must prioritise the protection of aquifers through strict land use controls in recharge zones and encourage compact development to limit impervious surfaces. Planning should incorporate groundwater vulnerability assessments, buffer zones and regulation of nearby agricultural and industrial activities.

Natural hazards, such as wildfires, earthquakes, and floods can cause damage to roads, which can disrupt the operational efficiency of the network. To rank the roads in order of vulnerability, many studies compute shortest-path measures, such as centrality to quantify structural importance. However, computing these measures is computationally inefficient, especially when recomputing is required after the network structure changes. Recent advancements have enabled the centrality-based ranking problem to be quickly solved on large networks using graph neural networks (GNNs). Although these methods have shown success in other domains for the centrality-based ranking task, they require large amounts of labeled training data and have been rarely explored to address the specific properties of road networks. To overcome these challenges, we propose a fast, data efficient machine learning framework called PathVGAE to learn to rank structurally important roads. PathVGAE leverages a Variational Graph Autoencoder (VGAE) architecture to generate expressive path-based embeddings explicitly for ranking, which are then mapped to a final ranking score prediction. Experimental results show that our model can quickly and accurately rank each road in a network given little data, making it a potentially valuable tool for disruption vulnerability analysis.

Assessment of land surface vulnerability using time-series geospatial datasets

This Governance Diagnostic (GD) Report has been prepared by IMF staff at the request of the Ghanian authorities. Informed by political economy analysis, the GD discusses the nature and severity of corruption and provides a comprehensive analysis of governance weaknesses and corruption vulnerabilities affecting the following key state functions: fiscal governance (public financial management and revenue administration), financial sector oversight, rule of law, anti-money laundering and countering the financing of terrorism. The GD report proposes a set of prioritized, time-bound reform measures aimed at strengthening economic governance, rule of law and reducing corruption vulnerabilities. The authorities have committed to strengthening governance and reducing corruption, providing a critical opportunity to address the long-standing vulnerabilities.

Heat Vulnerability Assessment and Analysis of Driving Mechanisms in a Megacity Based on Local Climate Zones: A Street-Level Case Study of Chengdu

Seismic Vulnerability Assessment Method of PE Gas Pipeline Networks Based on a Comprehensive Weight Cloud Model

Progressive damage processes and quantitative vulnerability assessment of buildings under debris flow and mudflow impacts

Coastal Vulnerability Assessment in Svalbard region of the Arctic exploiting geoinformation technologies and a cloud-based platform

Reliability-based vulnerability assessment of steel truss bridge components

Resource-efficient automatic software vulnerability assessment via knowledge distillation and particle swarm optimization

Seismic vulnerability assessment method of overhead contact systems based on numerical simulation

Developing enhanced road network vulnerability assessment using simulation-informed weighted graphs

5G networks become integral to modern communication which ensure their security against emerging threats has become a critical challenge. This research investigates the security risks and vulnerabilities in 5G network traffic to focus on the comparative performance of traditional machine learning (ML) models and generative artificial intelligence (GAI) techniques for attack detection. Specifically, the study evaluates the detection accuracy of DoS, MITM, and DDoS attacks across both traditional ML and GAI models. The findings reveal that GAI significantly outperforms traditional ML models in terms of detection accuracy with an average improvement of 15-20%. The study also explores the potential privacy and performance trade-offs associated with each approach. The results show that while generative AI introduces a slight increase in latency compared to traditional models, the improved security benefits justify this trade-off. This research highlights the promising role of GAI to enhance the security and privacy of 5G networks which offer a robust solution to the evolving threats in next-generation communications. The study concludes by recommending for further exploration into hybrid models and real-time attack prediction to strengthen the security framework of 5G networks.

Zambia continues to experience increasingly frequent and intense droughts, with the 2023/2024 season among the most severe in recent history. These events have threatened livelihoods, strained water and food systems, and placed immense pressure on already limited national and local resources. Given the limited knowledge in the literature on drought management in Zambia, this study investigated the state of localized district efforts across the country. By using mixed methods with a total of 161 interviews, it assessed the participation of district governments and sector players across key components of drought governance, including early warning, monitoring, vulnerability and impact assessment, mitigation, and response. Although Zambia has made notable progress in establishing national institutional frameworks and climate policies, key findings reveal a pattern of limited proactive engagement, with most participation occurring only in response to extreme events like the 2023/2024 drought. This reactive posture at the district level is further compounded by inadequate resources, limited coordination, a lack of localized drought planning, and systemic bureaucratic constraints that undermine a timely and effective response. Nonetheless, numerous opportunities exist to strengthen drought management by localizing decision-making, integrating indigenous knowledge into existing early warning systems, and leveraging community-based infrastructures to maximize scarce resources and build long-term resilience. The paper concludes with recommendations for enhancing Zambia’s drought preparedness and response capacity through inclusive, risk-based, and proactive strategies; insights that can be adapted to other developing country contexts.

This dataset presents the most comprehensive estimate of residential air conditioning (AC) prevalence across the continental United States. Using property-level data for over 103 million housing units from the Dewey database, we imputed and classified four AC types: central, other, evaporative cooler, and none, using XGBoost models optimized for performance. Housing characteristics, socioeconomic indicators, and environmental conditions, such as Cooling Degree Days and elevation, informed predictions. The final product offers national coverage with spatial resolution at the census tract, ZIP code, and metropolitan levels. Model validation was conducted using American Housing Survey data, with strong alignment observed for the central and no air conditioning (AC) categories. This dataset addresses longstanding gaps in understanding the geographic and demographic disparities in AC access, critical for public health, climate adaptation, and energy equity research. Users may integrate these data into epidemiological modeling, resilience planning, and policy analysis to support heat vulnerability assessments and infrastructure interventions.