Vulnerability Assessment Research Articles

Seismic vulnerability assessment of urban areas made of adobe buildings through analytical and numerical methods: The case study of the historical center of Cusco (Peru)

This paper aims at giving a contribution to the topic concerning the preservation of valuable adobe buildings, by setting a methodology for assessing their seismic vulnerability at the urban scale by means of analytical and numerical models, accounting for both their in-plane and out-of-plane behaviors. To this purpose, the valuable historical center of Cusco (Peru) is considered as representative case study. The city center of Cusco is an extraordinary example of colonial architecture that preserves essential aspects of its origins, it being representative of a large plethora of historical centers in South America and the Caribbean, where most of the residential buildings are made of adobe masonry.First, archetypes, representative of the whole historical center, are identified in order to get a manageable stock of buildings for which the analyses can be addressed to. The structural capacity of each archetype is then given in terms of capacity curves for both out-of-plane and in-plane mechanisms. Subsequently, these curves are compared with the likely seismic demands to assess the probability of attaining pre-established damage states for different seismic intensities. The obtained probabilities are collected into Damage Probability Matrices (DPMs) and, finally, cumulated to plot the fitting fragility curves.

Survey of source code vulnerability analysis based on deep learning

Amidst the rapid development of the software industry and the burgeoning open-source culture, vulnerability detection within the software security domain has emerged as an ever-expanding area of focus. In recent years, the rapid advancement of artificial intelligence, particularly the notable progress in deep learning for pattern recognition and natural language processing, has catalyzed a surge in research endeavors exploring the integration of deep learning for the enhancement of vulnerability detection techniques. In this paper, we investigate contemporary deep learning-based source code analysis methods, with a concentrated emphasis on those pertaining to static code vulnerability detection. We categorize these methods based on various representations of source code employed during the preprocessing stage, including token-based and graph-based representations of source code, and further subdivided based on the types of deep learning algorithms or graph representations employed. We summarize the basic processes of model training and vulnerability detection under these different representation formats. Furthermore, we explore the limitations inherent in current approaches and provide insights into future trends and challenges for research in this field.

Current status and challenges in operating flood early warning systems at the local level in Japan

Flood early warning systems (FEWS) are crucial in reducing flood loss and damage, especially under increasing flood risks due to climate change. Currently, there is limited literature holistically investigating all components of FEWS, namely Risk knowledge, Monitoring and Forecasting, Warning Dissemination, and Preparedness and Response Capabilities. There is also a lack of in-depth understanding of FEWS operation at the local level where local governments play a crucial role. This study conducted a nation-wide survey targeting Japanese municipalities (n = 350) to investigate the current status and challenges of FEWS operations at the local level and the relationships between these elements. The results indicated that while progress in FEWS operation varies in municipalities, they are encountering different challenges in each element of the system. These include a lack of human resources and financial resources, difficulties in risk assessment and data acquisition, limitations of dissemination methods to reach vulnerable, and limited understanding of public perception. This study is the first to investigate all four key elements of FEWS at the local level and the relationships of its key factors, providing useful implications for policy directions to enhance FEWS implementation. All key elements of FEWS positively correlate, suggesting that improving one component can improve the whole system. Government should also strengthen progress in vulnerability assessment, particularly incorporating socio-economic changes as these have the strongest influence on preparedness and response capability of the local areas.

Climate change ecological vulnerability and hotspot analysis of himalayan forests in North-Eastern region, India

The Himalayan forests are vulnerable to climate change leading to disturb the current flow of critical ecosystem services to the dependent population. Present study attempts to evaluate the current vulnerability status of major forest types in Nagaland, India due to climate change along with identifying the hot spots for mitigating the vulnerability of the forests. Vulnerability of mixed moist deciduous, pine, wet hill and wet temperate forests were evaluated using the IPCC framework. Local Moran's I analysis, hot spot analysis and a histogram analysis of the vulnerability were made in QGIS. Field data such as Shannon-Wiener Index and Importance Value Index along with secondary data such as edaphic factors, vegetation factors and climatic variables were used for vulnerability analysis. Analysis results that mixed moist deciduous forests had the highest area in under high-high clusters, hot spots and was highly vulnerable. The result suggests that very dense or moderately dense forest cover were less vulnerable than low density forests in the region. The study provides baseline information along with identification of vulnerable hot spot forests for future climate change policy and management of resources at the division level. The study suggests strengthening the less dense forests along with increasing the biodiversity through suitable strategy for mitigating the vulnerability.

AI-enabled Landslide Recognition for Effective Public Health Risk Management

The frequency of hazardous landslides has increased worldwide as a result of increased heavy rainfall events and increased human construction activities. The assessment of landslide vulnerability is an essential and effective method for preventing landslides. To resolve these issues, this paper develops a novel Rat Swarm integrated Random Forest (RSRF) method to manage the risk evolved through the landslide. The LISS-III satellite dataset of remote sensing (RS) images was gathered for the study. The preprocessing is performed by employing the z-score normalization to standardize the data images. The rat swarm (RS) optimization enhances the feature selection in the landslide by efficiently forecasting relevant assessment and the random forest (RF) employed to improve the classification accuracy in the landslide recognition process. Various existing methods are utilized for the comparison performance with the proposed RSRF techniques. The outcome shows that the proposed RSRF method improved more significantly than all other existing techniques in terms of area under the curve (AUC (93.3%)), F1-score (85.5%), log-loss (40.4%), and recall (97%).

Flood risk decomposed: Optimized machine learning hazard mapping and multi-criteria vulnerability analysis in the city of Zaio, Morocco

Urban flood risk mapping has become crucial for effective mitigation and urban planning. This study assesses and maps flood risk in the city of Zaio, Morocco, using machine learning and Multi-Criteria Decision Analysis (MCDA) techniques to overcome data scarcity challenges. We employed the Random Forest (RF) model with nine flood conditioning factors for flood hazard and the Analytical Hierarchy Process (AHP) for vulnerability assessment. To enhance RF model performance, we compared three hyperparameter tuning techniques: Bayesian Optimization (RF-BO), Genetic Algorithm (RF-GA), and Grid Search (RF-GS). RF-BO demonstrated superior accuracy in flood hazard modelling. Flood vulnerability was assessed using AHP, incorporating social and physical factors. The final flood risk map was produced by combining the RF-BO hazard model with the AHP vulnerability assessment. Results indicate that flood hazard in Zaio is significantly affected by factors such as topography and distance to rivers. Moreover, areas with high population density closer to rivers, especially in the south-western residential area, were found to be more exposed to flood risk. The findings highlight the utility of ML models, MCDA, and hyperparameter optimization in urban flood risk mapping, enabling the identification of high-risk urban areas that should be prioritized for flood protection efforts. This approach proves especially valuable in ungauged regions with limited hydrological data.

Net Primary Productivity, Vegetation Index Analysis, and Assessment of Protected Areas, Vulnerabilities, and Management Strategies: Review

This bibliographic analysis focused on various methods for estimating net primary productivity, vegetation indices and their various applications, as well as vulnerability assessment and management strategies for protected areas. To do this, a bibliography on the different topics collected using search engines Scopus, Science Direct, ResearchGate, and Google Scholar via the Publish or Perish portal was analyzed. Of the 1128 scientific papers on the selected topic after refining the database, 978 were journal articles, 59 were books, 52 were reports, 20 were conference proceedings, and 19 were theses. These documents detail numerous methods for estimating net primary productivity, a key parameter for assessing ecosystem performance. Methods using remote sensing data, especially vegetation indices, appear to be the easiest, least costly, and least labor-intensive today, ensuring reliable results. These innovative methods are best suited for assessing fragile ecosystems. This is the case for protected areas which have been facing the combined effects of anthropogenic actions and climate change in recent years. Considering the challenges posed by the management of Togolese protected areas, particularly since the socio-political disturbances of the 1990s, it is urgent to assess the health status of these specific ecosystems, focusing on their performance.

Geospatial approach to pluvial flood-risk and vulnerability assessment in Sunyani Municipality

Historically, and in recent times, efforts have been to understand, predict, analyze, and quantify floods and their impacts in various countries of the globe. Although recent scientific advances have introduced approaches to assessing the risks presented by flooding, little studies have been carried out in the Sunyani Municipality of Ghana for generating a pluvial flood-risk and vulnerability map for risk identification, resilience, emergency preparedness, and urban spatial planning. In this study, five parameters that influence both pluvial and fluvial flooding were assessed to map flood-prone areas within the Sunyani Municipality. These are precipitation, drainage density, LULC, elevation, and slope, which were integrated in GIS. Using an AHP, weights were assigned to each parameter based on its level of influence on flooding. The findings reveal that 21.32 % of the Sunyani Municipality lies within a highly flood-prone area, 39.65 % in a flood-prone area, while 28.06 % and 10.97 % in slightly flood-prone and not flood-prone areas respectively. Built-up areas close to watersheds with lower elevations and larger drainage density are the places that are highly flood-prone. Some towns within the highly flood-prone and flood-prone areas are Abesim, Newtown, Nkwarbeng, Baakoniaba, Kootokrom, and Penkwase. Highly valued infrastructure such as schools, churches, and hospitals have also been found within these highly flood-prone areas. These findings can aid the government and relevant stakeholders in disaster risk management to be better informed, and to effectively plan and prevent flood challenges in the Sunyani Municipality. Moreover, urban spatial planners in the study setting can consider incorporating the flood hazard maps generated from this study into their spatial plans for proactive physical developments.

Pre-and post-landslide analyses for sustainable development in Kiarapandak area, Bogor, West Java, Indonesia

Abstract The landslide in Kiarapandak has destroyed several dwellings, impaired farming and agriculture, and critical land formation. Analysing the conditions before and after the landslide is essential for supporting sustainable development, protecting the community, and mitigating environmental damage. This investigation employs Google Earth imagery, site surveys, field observations, landslide typology, and vulnerability assessments. The research area, Kiarapandak, is categorized as a landslide-prone typology Zone B with a predominantly 21%–40% slope and more than 40%. The vulnerability level is high. In the critical land created by the landslide, there is a reduction in soil fertility due to surface stripping, increased erosion, and surface runoff, causing sedimentation and river flooding. Land rehabilitation efforts are necessary to improve the quality of land resources in the post-landslide area and support the ecosystem while generating economic value for affected landowners. An agroforestry system, with a vertical arrangement of species strata, can be implemented in the post-landslide location to serve ecological and financial functions and optimize land resource quality through fertilizer application.

Optimisation of decision-making on risk management strategy for the hydromelioration systems in biosphere reserves

The field of nature conservation in Ukraine is currently experiencing a profound crisis. In light of the mounting risks to Ukraine’s ecosystems, it is of paramount importance that conservation planning incorporates risk management strategies. In the aftermath of the Chernobyl disaster, the management of water bodies in the Exclusion Zone assumed paramount importance, given that the rivers had become the primary conduit for the transportation of radioactive substances. It is therefore imperative to evaluate the risks and vulnerabilities of climate change on Ukraine’s most developed economic sectors, population, and natural ecosystems. This will facilitate comprehension of the prospective consequences of climate change, ascertain the extent of potential losses, and inform decision-making aimed at reducing or preventing such losses in a timely manner. One of the primary challenges facing specialists at the Chornobyl Radiation and Ecological Biosphere Reserve, in addition to other organisations and enterprises of the State Agency of Ukraine on Exclusion Zone Management and scientific institutions, is the assessment of the feasibility of hydromelioration systems. It is of the utmost importance that a scientifically sound methodological approach be employed in order to ensure the reliability and validity of the results obtained through the assessment of risks and vulnerabilities to climate change. In this context, the utilisation of adaptive methodologies is of paramount importance for the development of risk management strategies. One such methodology is the Methodology for Risk Analysis and Information Management for Strategic Ecosystems (MARISCO). This article considers the potential deployment of the MARISCO adaptive method as a universal methodology for the analysis of environmental issues, with a view to informing decision-making on risk management strategies in the Chornobyl Radiation and Ecological Biosphere Reserve.

Detection and Analysis of Broken Access Control Vulnerabilities in App–Cloud Interaction in IoT

Detection and Analysis of Broken Access Control Vulnerabilities in App–Cloud Interaction in IoT

Liquid-liquid phase separation-related signature predicts prognosis and therapeutic response in esophageal adenocarcinoma

BackgroundEsophageal adenocarcinoma is a leading cause of mortality worldwide. New evidence indicates that liquid-liquid phase separation is related to malignancies. The current study aims at exploring the functions of liquid-liquid phase separation within esophageal adenocarcinoma. Patients within the TCGA dataset were classified using liquid–liquid phase separation-related genes. Significantly differentially expressed genes and prognostic factors for overall survival have been screened by Cox regression. Based on the liquid-liquid phase separation score, the construction of a prognostic model and liquid-liquid phase separation signature was constructed. Tumor mutation burden and drug sensitivity were analyzed in two groups: high liquid-liquid phase separation scores, and low liquid-liquid phase separation scores. According to liquid-liquid phase separation, some small-molecule compounds targeting esophageal adenocarcinoma were screened. The results were verified in vitro with an external cohort. Results87 samples are involved, and 61 liquid-liquid phase separation-related genes may influence esophageal adenocarcinoma by changing DNA conformation and metabolism. Meanwhile, based on a high liquid-liquid phase separation score and low score group including 43 patients, it is found that the result significantly lowered the 5-year overall survival to 32.6 %, compared to 64.8 % in the low-score group of 44 patients with p < 0.001. The high score group had an average TIDE score of 0.27 versus 0.14 in the low-score group, with p = 0.003. The median tumor mutation burden was 9.1 mutations/Mb in the high-score group versus 6.4 mutations/Mb in the low-score group, with p = 0.011. The predictive model worked very well, with area under the curve values of 0.82, 0.79, and 0.76 for 1-, 3-, and 5-year survival, respectively. Liquid-liquid phase separation has been validated as an effective prognostic biomarker and drug sensitivity predictor. SignificanceLiquid-liquid phase separation is potentially implicated in esophageal adenocarcinoma and works as a prognostic biomarker assessment of vulnerability to LLPS, which could help develop individualized therapies by showing how one is situated about various medications where responses vary across the body.

Assessing the vulnerability of Elasmobranch species in the Bay of Bengal: Insights from Lakkha gill net fishery of Bangladesh

The elasmobranch population is declining in the Bay of Bengal of Bangladesh due to large-mesh gill net fishing, locally known as the Lakkha net, which primarily targets Indian threadfin (Leptomelanosoma indicum). This study was the first attempt to identify megafaunal bycatch in Lakkha fishing and assess its vulnerability using Productivity Susceptibility Analysis. A total of 40 elasmobranch bycatch species were identified, with sharks comprising 13 species from three families, while 27 rays belonged to six families, with the majority belonging to the Myliobatiformes order (60 %). Productivity and susceptibility scores were assigned to all identified species, with values ranging from 1.27 to 2.73 and 1.50 to 2.63, respectively. The target Lakkha fish exhibited the highest susceptibility score, followed by several pelagic sharks and eagle rays. Vulnerability assessment revealed that 31.7 % (n = 13) of species were highly vulnerable, while 43.9 % (n = 18) were classified as moderate, and 24.4 % (n = 10) were considered to have low vulnerability. All the high-risk megafauna species (n = 13) are classified as threatened by the global IUCN Red List. Sensitivity analysis highlighted susceptibility as a major contributor to species’ vulnerability. Alterations in susceptibility scores led to significant changes in the vulnerability status of many species. The overall data quality assessment indicated moderate data quality across species, with variability observed between productivity (76 % of species received a poor data quality score) and susceptibility attributes. However, vulnerability of these species can be reduced through adequate gear modification, shorter net deployment periods, adoption of safe discharge techniques, identification of critical habitats, and establishment of marine protected areas within this region. This study provides valuable insights into the species composition and vulnerability of elasmobranchs in the Lakkha gill net fishery, emphasizing the need for conservation measures to mitigate bycatch impacts on threatened species.

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.

Seismic vulnerability assessment of a railway beam bridge considering brittle failure of the restrainer in a friction pendulum system

AbstractThe restrainers in the friction pendulum system (FPS) may experience brittle failure during an earthquake. Strong nonlinear behavior should be considered to precisely assess the seismic performance of the railway beam bridge under an earthquake. A seismic vulnerability assessment was performed based on a typical simply supported railway beam bridge. Three different models of the FPS in fixed direction were considered: elastic restrainer model, brittle restrainer model, and nonrestrainer model. Through dynamic analysis, the responses of the railway beam bridge were obtained, including the force and displacement of the FPSs, the curvature ductility at the pier bottom, and transverse dislocation at the beam gap. The analysis results pointed out when the earthquake intensity exceeded the fundamental intensity, the brittle failure of the restrainers was very likely to happen. The sudden release of energy introduced a displacement pulse to the FPS. The elastic restrainer model overestimated the force demand and damage probability of the substructures but underestimated the FPS displacement and dislocation at the beam gap. The nonrestrainer model seriously underestimated the force demand of the substructure and the FPS displacement under strong earthquakes. The brittle restrainer model could reflect the nonuniform failure of the restrainers and provide a more accurate estimate of the transverse dislocation at the beam gap.

Infrastructure Resilience: A Vulnerability Assessment of Padang City’s Road Network in Disaster Area

Infrastructure Resilience: A Vulnerability Assessment of Padang City’s Road Network in Disaster Area

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

Seismic Vulnerability Assessment of Non-Overflow Concrete Gravity Dam Section

Abstract As the economy advances, there is a growing need for energy. Harnessing hydroelectric energy has emerged as a crucial strategy for addressing the energy crisis. To promote hydropower development, numerous tall concrete dams are either under construction or in the planning stages. These dams are frequently situated in areas with elevated water levels and face the challenge of being located in regions with high seismic intensity. An accident resulting from the failure of concrete dams can pose significant threats to both economic development and public safety. Therefore, it is of crucial importance to investigate the stability of such dams, particularly under conditions of high water levels and seismic activity. The finite element program Geostudio is being undertaken to evaluate a complete numerical analysis of the dam. In this paper, Longtan Dam has been selected as the case study for the research. This study is applied in two cases: seismic and static conditions. A two-dimensional seismic numerical analysis was carried out using the Koyna earthquake acceleration time records in 1976. The results of the FEM model concluded that the dam is unsafe under sesmic conditions. The concrete-rock interface, particularly susceptible to sliding, was identified as the most critical part of the dam, presenting the most likely failure mode. The study highlights the significance of its methodology in investigating earthquake effects on dams, prioritizing it over the achieved results. The aim is for the conclusions and recommendations to provide valuable guidance for future studies and initiatives focused on enhancing safety at hydropower dams.

Regional Disaster Vulnerability Analysis and Insurance Selection Analysis Based on AHP-EWM-TOPSIS Group Method

Floods, hurricanes, cyclones, and droughts are the most common types of weather, and they have a profound impact on insurance. To specifically analyse the impact, the paper first introduces the concept of Regional Resilience Index (RRI), and then use the AHP-EWM-TOPSIS Method to derive the RRI scores and categorize all regions into five categories based on the size of RRI, which are rock-solid, stable, moderate, weak and vulnerable. Next, the Insurance Break-even Index Evaluation Model is built through the analysis. Insurers should increase their coverage in rock-solid, stable and moderate areas in line with the equilibrium curve, where they should invest in extensive publicity. In weak and vulnerable areas, insurance companies should appropriately reduce policy investment, build a multi-level insurance portfolio such as reinsurance mechanism, and invest a certain amount of funds in regional disaster prevention and loss prevention to maximize the win-win situation between the insurance company and the insured.

Monitoring climate change vulnerability in the Himalayas.

Longitudinal assessment of climate vulnerability is essential for understanding the complex factors affecting how people experience and respond to climate change. We report on the first longitudinal assessment of climate vulnerability in the Himalayan region, exploring the evolving landscape, perceptions, and experiences of communities of climate change impacts, vulnerability, and adaptation in Kashmir over an 8-year period from 2017 to 2024. We provide the Himalayan Re-study Framework (HRF) to monitor, characterise, and conceptualise climate change in the Himalayas. Utilising mixed methods, we showcase how climate change is affecting social, economic, political, and environmental dimensions, examining how the impacts of climate change and vulnerability evolve over time, shaping and reshaping how climate risks are experienced and responded to by communities. Our analysis reveals a nuanced understanding of vulnerability, highlighting the impact on communities' livelihoods and water security, differential impacts on marginalised communities, and the gendered nature of climate change. We examine how certain sections of the population face marginalisation, discrimination, and racism, and how climate change exacerbates these challenges. Kashmir's vulnerability to climate change extends beyond environmental factors, intertwining with culture, livelihoods, social dynamics, and politics. Climate change continues to compete for attention with immediate political and socio-economic challenges, highlighting the need for integrated approaches to address both environmental and societal issues in Kashmir.