Understanding the response of ecological vulnerability to global changes is essential for sustainable ecosystem management.However,incorporating ecological vulnerability assessment into accurate decision-making to ill...Understanding the response of ecological vulnerability to global changes is essential for sustainable ecosystem management.However,incorporating ecological vulnerability assessment into accurate decision-making to illustrate ecosystem dynamics and drivers remains unclear.Here,we used the Vulnerability Scoping Diagram model(VSD)and ecological vulnerability index(EVI)to evaluate the ecosystem vulnerability in the upper reach of Yellow River(URYR).Our results show that EVI increased from southwest to northeast,and EVI first went down from 2005 to 2015 and then slightly up from 2015 to 2020.The lower EVI can be attributed to improving environmental protection and restoration projects in the URYR.The subareas of the High-High cluster were distributed in the northeast,and those of the Low-Low cluster were distributed in the southwest,respectively.Furthermore,we divided the URYR into four zones(e.g.,Protection Zone,Ecological Degradation Zone,Ecological Improvement Zone,and Comprehensive Zone)according to the EVI.Consequently,a long-term detection system and public education should be enhanced to improve environmental awareness,which plays a beneficial role in the sustainable development of four zones.All in all,our findings not only shed light on the dynamic of ecological vulnerability but also provide the diversification management of the upper reach of the Yellow River.展开更多
Vietnam,with its twenty-eight coastal provinces,is one of the nations most profoundly affected by the adverse impacts of climate change(CC).These provinces face severe challenges as they contend with the escalating ef...Vietnam,with its twenty-eight coastal provinces,is one of the nations most profoundly affected by the adverse impacts of climate change(CC).These provinces face severe challenges as they contend with the escalating effects of CC,including rising sea levels,typhoons,flooding,and droughts.In this context,this article aims to assess the vulnerability of households'livelihoods in Quang Nam Province by applying the Livelihood Vulnerability Index(LVI)developed by Hahn et al.,along with the Intergovernmental Panel on Climate Change framework(LVI-IPCC).The study utilises five sources of household capital—human,social,physical,natural,and financial—to construct its indices.The data for this article is based on a survey of 200 households.The research methodology combines both quantitative and qualitative methods,including questionnaire interviews,in-depth interviews,and focus group discussions.The research period spans from 2021 to 2023.The study results revealed that the household LVI was 0.371,while the LVI-IPCC was 0.086,highlighting the critical need for access to food and clean water,which scored 0.458 and 0.351,respectively.The research underscores how CC significantly affects the livelihoods of coastal communities,particularly in sectors such as fishing,aquaculture,and agriculture.The study concludes that CC poses significant challenges to the livelihoods of coastal communities in Quang Nam Province and that adaptation measures are necessary to support these communities.The research highlights the importance of livelihood diversification,job transformation,and improving knowledge and skills to enhance the resilience of coastal communities to CC.展开更多
Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowad...Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowadays,the groundwater vulnerability assessment(GVA)has become an essential task to identify the current status and development trend of groundwater quality.In this study,the Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)models are integrated to realize the spatio-temporal prediction of regional groundwater vulnerability by introducing the Self-attention mechanism.The study firstly builds the CNN-LSTM modelwith self-attention(SA)mechanism and evaluates the prediction accuracy of the model for groundwater vulnerability compared to other common machine learning models such as Support Vector Machine(SVM),Random Forest(RF),and Extreme Gradient Boosting(XGBoost).The results indicate that the CNNLSTM model outperforms thesemodels,demonstrating its significance in groundwater vulnerability assessment.It can be posited that the predictions indicate an increased risk of groundwater vulnerability in the study area over the coming years.This increase can be attributed to the synergistic impact of global climate anomalies and intensified local human activities.Moreover,the overall groundwater vulnerability risk in the entire region has increased,evident fromboth the notably high value and standard deviation.This suggests that the spatial variability of groundwater vulnerability in the area is expected to expand in the future due to the sustained progression of climate change and human activities.The model can be optimized for diverse applications across regional environmental assessment,pollution prediction,and risk statistics.This study holds particular significance for ecological protection and groundwater resource management.展开更多
Climate change and rising temperatures are accelerating the rate of deglaciation in the Hindu Kush Karakoram Himalaya(HKH)ranges,leading to the formation of new glacial lakes and the expansion of existing ones.These l...Climate change and rising temperatures are accelerating the rate of deglaciation in the Hindu Kush Karakoram Himalaya(HKH)ranges,leading to the formation of new glacial lakes and the expansion of existing ones.These lakes are often vulnerable to failure,posing a significant threat to downstream communities and infrastructure.Therefore,a comprehensive assessment of Glacier-Lake Outburst Flood(GLOF)hazards and risk assessment is crucial to evaluate flood runout characteristics and identify settlements and infrastructure that are exposed and vulnerable to floods,aiding in the development and implementation of risk reduction strategies.This study aims to simulate a GLOF event induced by the Shisper glacier lake in northern Pakistan,using the HEC-RAS,and to assess its impact on settlements,infrastructure,and agricultural land.For the hydrometeorological analysis of the GLOF event,topographic data from unmanned aerial vehicles(UAVs),stream profiles,discharge data,Manning's roughness coefficient(n),and land use/land cover(LULC)were analyzed using HEC-RAS and geographic information system(GIS).During the GLOF event on May 7,2022,a maximum water depth of 6.3 m and a maximum velocity of 9.5 m/s were recorded.Based on the runout characteristics of this event,vulnerability and risk assessments have been calculated.The physical,social,and environmental vulnerabilities of the at-risk elements were evaluated using the analytical hierarchy process(AHP)and integrated with the hazard data to develop a risk map.The study identified the areas,infrastructure and settlements susceptible to GLOF hazard to support the development and implementation of targeted and evidence-based mitigation and adaptation strategies.展开更多
Bioethics is the argumentative discipline of decisions and actions that reduce conflicts of interest,dilemmas,or asymmetries between the parties involved in biomedical research.The objective of this work was to review...Bioethics is the argumentative discipline of decisions and actions that reduce conflicts of interest,dilemmas,or asymmetries between the parties involved in biomedical research.The objective of this work was to review and compare the dimensions used by bioethics in the communicative management of the pandemic,namely:risk,vulnerability,resilience,and stigma.A documentary,exploratory,transversal,and retrospective work was carried out with a sample of sources indexed in international repositories,considering the search by keywords and the publication period from 2020 to 2024.The results demonstrate the prevalence of supply,contagion,symptoms,and help against COVID-19.展开更多
In Niger, farms have been facing negative effects of climate change for several decades. The objective of this work is to assess the vulnerability of farms in Tillabery department by proposing an adaptation approach. ...In Niger, farms have been facing negative effects of climate change for several decades. The objective of this work is to assess the vulnerability of farms in Tillabery department by proposing an adaptation approach. A five-step method and descriptive analysis were used on a sample of 250 farmers. The degree of damage caused by pests and crop diseases is significant, with respective proportions of 52.50% and 40.40%. It appears that the main climate risk factors for vulnerability are droughts, floods, soil degradation, and pest invasions. Additionally, the average level of exposure to agricultural operations is very high, with an index of 0.6. The sensitivity index remained constant in the range of 0.3 to 0.6 and is significant (reaching an index of 0.8). However, 61.2% of farms have a medium level of vulnerability and 33.3% have a high vulnerability to the effects of climate change. Nonetheless, a concerning trend regarding the vulnerability of farms has been observed. To assist policymakers and development actors in improving the vulnerability level of these production units, four phases of action are proposed: a diagnostic phase, evaluation, estimation of adaptation needs, implementation, and proper monitoring of actions.展开更多
The increasing frequency and intensity of forest fires,driven by climate change and human activities,pose a significant threat to vital forest ecosystems,particularly where fire is not a natural element in the regener...The increasing frequency and intensity of forest fires,driven by climate change and human activities,pose a significant threat to vital forest ecosystems,particularly where fire is not a natural element in the regeneration cycle.This study aims to identify the indicators influencing forest fire vulnerability and compare maps of forest fire susceptibility that are based on the Intergovernmental Panel on Climate Change tripartite model,with a focus on the vulnerable Hyrcanian forest region in Golestan Province,northern Iran,where forest fires have caused considerable economic losses.On the basis of expert opinions and a literature review,we used geographic information systems,remote sensing and machine learning techniques to select and weigh 30 biophysical,environmental and socioeconomic indicators that affect forest fire vulnerability in the study area.These indicators were rigorously normalized,weighted and amalgamated into a comprehensive forest fire vulnerability index to analyze forest exposure,sensitivity and adaptive capacity.We thus identified and mapped areas with very high forest fire exposure,high sensitivity and low adaptive capacity for urgent targeted intervention and strategic planning to mitigate the impacts of forest fires.The results also revealed a set of critical indicators that contribute more significantly to forest fire vulnerability(e.g.,precipitation,elevation and factors related to biodiversity,human activity and economic reliance on forest resources).Our results provide insights that can inform policy-making,community engagement and environmental management strategies to mitigate the vulnerabilities associated with forest fires in the Hyrcanian forest.展开更多
Software systems are vulnerable to security breaches as they expand in complexity and functionality.The confidentiality,integrity,and availability of data are gravely threatened by flaws in a system’s design,implemen...Software systems are vulnerable to security breaches as they expand in complexity and functionality.The confidentiality,integrity,and availability of data are gravely threatened by flaws in a system’s design,implementation,or configuration.To guarantee the durability&robustness of the software,vulnerability identification and fixation have become crucial areas of focus for developers,cybersecurity experts and industries.This paper presents a thorough multi-phase mathematical model for efficient patch management and vulnerability detection.To uniquely model these processes,the model incorporated the notion of the learning phenomenon in describing vulnerability fixation using a logistic learning function.Furthermore,the authors have used numerical methods to approximate the solution of the proposed framework where an analytical solution is difficult to attain.The suggested systematic architecture has been demonstrated through statistical analysis using patch datasets,which offers a solid basis for the research conclusions.According to computational research,learning dynamics improves security response and results in more effective vulnerability management.The suggested model offers a systematic approach to proactive vulnerability mitigation and has important uses in risk assessment,software maintenance,and cybersecurity.This study helps create more robust software systems by increasing patch management effectiveness,which benefits developers,cybersecurity experts,and sectors looking to reduce security threats in a growing digital world.展开更多
The survival of ectotherms worldwide is threatened by climate change.Whether increasing temperatures increase the vulnerability of ectotherms inhabiting temperate plateau areas remains unclear.To understand altitudina...The survival of ectotherms worldwide is threatened by climate change.Whether increasing temperatures increase the vulnerability of ectotherms inhabiting temperate plateau areas remains unclear.To understand altitudinal variation in the vulnerability of plateau ectotherms to climate warming,Qinghai toad-headed lizards(Phrynocephalus vlangalii)were subjected to semi-natural enclosure experiments with simulated warming at high(2,600 m)and superhigh(3,600 m)elevations of the Dangjin Mountain,China.Our results revealed that the thermoregulatory effectiveness and warming tolerance(WT)of the toad-headed lizards were significantly affected by climate warming at both elevations,but their thermal sensitivity remained unchanged.After warming,the thermoregulatory effectiveness of lizards at superhigh elevations decreased because of the improved environmental thermal quality,whereas that of lizards at high-elevation conditions increased.Although the body temperature selected by high-elevation lizards was also significantly increased,the proportion of their active body temperature falling within the set-point temperature range decreased.This indicates that it is difficult for high-elevation lizards to adjust their body temperatures within a comfortable range under climate warming.Variations in the WT and thermal safety margin(TSM)under climate warming revealed that lizards at the superhigh elevation benefited from improved environmental thermal quality,whereas those at the high elevation originally on the edge of the TSM faced more severe threats and became more vulnerable.Our study highlights the importance of thermal biological traits in evaluating the vulnerability of ectotherms in temperate plateau regions.展开更多
This study advances the DRASTIC groundwater vulnerability assessment framework by integrating a multi-hazard groundwater index(MHGI)to account for the dynamic impacts of diverse anthropogenic activities and natural fa...This study advances the DRASTIC groundwater vulnerability assessment framework by integrating a multi-hazard groundwater index(MHGI)to account for the dynamic impacts of diverse anthropogenic activities and natural factors on both groundwater quality and quantity.Incorporating factors such as population growth,agricultural practices,and groundwater extraction enhances the framework’s ability to capture multi-dimensional,spatiotemporal changes in groundwater vulnerability.Additional improvements include refined weighting and rating scales for thematic layers based on available observational data,and the inclusion of distributed recharge.We demonstrate the practical utility of this dynamic DRASTIC-based framework through its application to the agro-urban regions of the Irrigated Indus Basin,a major groundwater-dependent agricultural area in South Asia.Results indicate that between 2005 and 2020,54%of the study area became highly vulnerable to pollution.The MHGI revealed a 13%decline in potential groundwater storage and a 25%increase in groundwater-stressed zones,driven primarily by population growth and intensive agriculture.Groundwater vulnerability based on both groundwater quality and quantity dimensions showed a 19%decline in areas of low to very low vulnerability and a 6%reduction in medium vulnerability zones by 2020.Sensitivity analyses indicated that groundwater vulnerability in the region is most influenced by groundwater recharge(42%)and renewable groundwater stress(38%).Validation with in-situ data yielded area under the curve values of 0.71 for groundwater quality vulnerability and 0.63 for MHGI.The framework provides valuable insights to guide sustainable groundwater management,safeguarding both environmental integrity and human well-being.展开更多
Spatial seismic vulnerability assessments are primally conducted at the community and grid level,using heuristic and empirical approaches.Building-based spatial statistical vulnerability models are rare because of dat...Spatial seismic vulnerability assessments are primally conducted at the community and grid level,using heuristic and empirical approaches.Building-based spatial statistical vulnerability models are rare because of data limitations.Generating open-access spatial inventories that document seismic damage and building attributes and test their effectiveness in assessing damage would promote the advancement of spatial vulnerability assessment.The 2022 Mw 6.7 Luding earthquake in the western Sichuan Province of China provides an opportunity to validate this approach.The local government urgently dispatched experts to survey building damage,marking all buildings with damage class stickers.In this work,we sampled 2889 buildings as GPS points and documented the damage classes and building attributes,including structure type,number of floors,and age.A polygon-based digital inventory was generated by digitizing the rooftops of the sampled buildings and importing the attributes.Statistical regressions were created by plotting damage against shaking intensity and PGA,and Random Forest modeling was carried out considering not only buildings and seismic parameters but also environmental factors.The result indicates that statistical regressions have notable uncertainties,and the Random Forest model shows a≥79%accuracy.Topographical factors showed notable importance in the Random Forest modeling.This work provides an open-access seismic building damage inventory and demonstrates its potential for damage prediction and vulnerability assessment.展开更多
This paper proposes a longitudinal vulnerability-based analysis method to evaluate the impact of foundation pit excavation on shield tunnels,accounting for geological uncertainties.First,the shield tunnel is modeled a...This paper proposes a longitudinal vulnerability-based analysis method to evaluate the impact of foundation pit excavation on shield tunnels,accounting for geological uncertainties.First,the shield tunnel is modeled as an Euler Bernoulli beam resting on the Pasternak foundation incorporating variability in subgrade parameters along the tunnel’s length.A random analysis method using random field theory is introduced to evaluate the tunnel’s longitudinal responses to excavation.Next,a risk assessment index system is established.The normalized relative depth between the excavation and the shield tunnel is used as a risk index,while the maximum longitudinal deformation,the maximum circumferential opening,and the maximum longitudinal bending moment serve as performance indicators.Based on these,a method for analyzing the longitudinal fragility of shield tunnels under excavation-induced disturbances is proposed.Finally,the technique is applied to a case study involving a foundation pit excavation above a shield tunnel,which is the primary application scenario of this method.Vulnerability curves for different performance indicators are derived,and the effects of tunnel stiffness and subgrade stiffness on the tunnel vulnerability are explored.The results reveal significant differences in vulnerability curves depending on the performance index used.Compared to the maximum circumferential opening and the maximum longitudinal bending moment,selecting the maximum longitudinal deformation as the control index better ensures the tunnel’s usability and safety under excavation disturbances.The longitudinal vulnerability of the shield tunnel nonlinearly decreases with the increase of the tunnel stiffness and subgrade stiffness,and the subgrade stiffness has a more pronounced effect.Parametric analyses suggest that actively reinforcing the substratum is more effective on reducing the risk of tunnel failure due to adjacent excavations than passive reinforcement of the tunnel structure.展开更多
Smart contracts are widely used on the blockchain to implement complex transactions,such as decentralized applications on Ethereum.Effective vulnerability detection of large-scale smart contracts is critical,as attack...Smart contracts are widely used on the blockchain to implement complex transactions,such as decentralized applications on Ethereum.Effective vulnerability detection of large-scale smart contracts is critical,as attacks on smart contracts often cause huge economic losses.Since it is difficult to repair and update smart contracts,it is necessary to find the vulnerabilities before they are deployed.However,code analysis,which requires traversal paths,and learning methods,which require many features to be trained,are too time-consuming to detect large-scale on-chain contracts.Learning-based methods will obtain detection models from a feature space compared to code analysis methods such as symbol execution.But the existing features lack the interpretability of the detection results and training model,even worse,the large-scale feature space also affects the efficiency of detection.This paper focuses on improving the detection efficiency by reducing the dimension of the features,combined with expert knowledge.In this paper,a feature extraction model Block-gram is proposed to form low-dimensional knowledge-based features from bytecode.First,the metadata is separated and the runtime code is converted into a sequence of opcodes,which are divided into segments based on some instructions(jumps,etc.).Then,scalable Block-gram features,including 4-dimensional block features and 8-dimensional attribute features,are mined for the learning-based model training.Finally,feature contributions are calculated from SHAP values to measure the relationship between our features and the results of the detection model.In addition,six types of vulnerability labels are made on a dataset containing 33,885 contracts,and these knowledge-based features are evaluated using seven state-of-the-art learning algorithms,which show that the average detection latency speeds up 25×to 650×,compared with the features extracted by N-gram,and also can enhance the interpretability of the detection model.展开更多
Coasts are subject to multiple natural hazards,which are increasing nowadays.Coastal flooding and erosion are some of the most common hazards affecting coastlines.Being aware of the vulnerability of coasts is importan...Coasts are subject to multiple natural hazards,which are increasing nowadays.Coastal flooding and erosion are some of the most common hazards affecting coastlines.Being aware of the vulnerability of coasts is important to achieve integrated coastal management.The coastal vulnerability index(CVI)is a common index used to assess coastal vulnerability because it is easily calculated.However,given that its calculation includes numerous manual steps,it requires considerable time,which is often unavailable,to produce accurate and utilizable results.In this work,we developed a ModelBuilder model by using the tools provided by ArcGIS Pro(ESRI).Through this model,we automatized most of the steps involved in CVI calculation.We applied the ModelBuilder model in the northern Peloponnese,for which the CVI has already been calculated in three other works.We were thus able to assess the effectiveness of our ModelBuilder model.Our results demonstrated that through the ModelBuilder,most of the processes could effectively be automatized without problems,and our results are consistent with the findings of previous works in our study area.展开更多
The escalating complexity and heterogeneity of modern energy systems—particularly in smart grid and distributed energy infrastructures—has intensified the need for intelligent and scalable security vulnerability cla...The escalating complexity and heterogeneity of modern energy systems—particularly in smart grid and distributed energy infrastructures—has intensified the need for intelligent and scalable security vulnerability classification.To address this challenge,we propose Vulnerability2Vec,a graph-embedding-based framework designed to enhance the automated classification of security vulnerabilities that threaten energy system resilience.Vulnerability2Vec converts Common Vulnerabilities and Exposures(CVE)text explanations to semantic graphs,where nodes represent CVE IDs and key terms(nouns,verbs,and adjectives),and edges capture co-occurrence relationships.Then,it embeds the semantic graphs to a low-dimensional vector space with random-walk sampling and skip-gram with negative sampling.It is possible to identify the latent relationships and structural patterns that traditional sparse vector methods fail to capture.Experimental results demonstrate a classification accuracy of up to 80%,significantly outperforming baseline methods.This approach offers a theoretical basis for classifying vulnerability types as structured semantic patterns in complex software systems.The proposed method models the semantic structure of vulnerabilities,providing a theoretical foundation for their classification.展开更多
RESTful APIs have been adopted as the standard way of developing web services,allowing for smooth communication between clients and servers.Their simplicity,scalability,and compatibility have made them crucial to mode...RESTful APIs have been adopted as the standard way of developing web services,allowing for smooth communication between clients and servers.Their simplicity,scalability,and compatibility have made them crucial to modern web environments.However,the increased adoption of RESTful APIs has simultaneously exposed these interfaces to significant security threats that jeopardize the availability,confidentiality,and integrity of web services.This survey focuses exclusively on RESTful APIs,providing an in-depth perspective distinct from studies addressing other API types such as GraphQL or SOAP.We highlight concrete threats-such as injection attacks and insecure direct object references(IDOR)-to illustrate the evolving risk landscape.Our work systematically reviews state-of-the-art detection methods,including static code analysis and penetration testing,and proposes a novel taxonomy that categorizes vulnerabilities such as authentication and authorization issues.Unlike existing taxonomies focused on general web or network-level threats,our taxonomy emphasizes API-specific design flaws and operational dependencies,offering a more granular and actionable framework for RESTful API security.By critically assessing current detection methodologies and identifying key research gaps,we offer a structured framework that advances the understanding and mitigation of RESTful API vulnerabilities.Ultimately,this work aims to drive significant advancements in API security,thereby enhancing the resilience of web services against evolving cyber threats.展开更多
Climate change and economic development impact the population expansion and water shortage in the middle reaches of the Yangtze River Basin(MYRB),leading directly to drought aggravation-expansion and impacting agricul...Climate change and economic development impact the population expansion and water shortage in the middle reaches of the Yangtze River Basin(MYRB),leading directly to drought aggravation-expansion and impacting agricultural production in the MYRB.Therefore,this study quantitatively evaluated agricultural drought vulnerability in the MYRB based on the variable fuzzy evaluation model(VFEM).The main conclusions are as follows:(1)The arable land in the MYRB gradually decreased between 2005 and 2020,whereas the forest cover decreased and then increased;(2)precipitation and evapotranspiration were the key factors affecting the agricultural drought vulnerability(e.g.,weights of 0.24 and 0.21),whereas arable land density and gross domestic product(GDP)per capita played less significant roles;and(3)the agricultural drought vulnerability in the MYRB during 2005-2020 was mainly at level 3 and below,with higher drought vulnerability in the western and northern regions,which had a higher drought risk potential.展开更多
Assessing the vulnerability of a platform is crucial in its design.In fact,the results obtained from vulnerability analyses provide valuable information,leading to precise design choices or corrective solutions that e...Assessing the vulnerability of a platform is crucial in its design.In fact,the results obtained from vulnerability analyses provide valuable information,leading to precise design choices or corrective solutions that enhance the platform's chances of surviving different scenarios.Such scenarios can involve various types of threats that can affect the platform's survivability.Among such,blast waves impacting the platform's structure represent critical conditions that have not yet been studied in detail.That is,frameworks for vulnerability assessment that can deal with blast loading have not been presented yet.In this context,this work presents a fast-running engineering tool that can quantify the risk that a structure fails when it is subjected to blast loading from the detonation of high explosive-driven threats detonating at various distances from the structure itself.The tool has been implemented in an in-house software that calculates vulnerability to various impacting objects,and its capabilities have been shown through a simplified,yet realistic,case study.The novelty of this research lies in the development of an integrated computational environment capable of calculating the platform's vulnerability to blast waves,without the need for running expensive finite element simulations.In fact,the proposed tool is fully based on analytical models integrated with a probabilistic approach for vulnerability calculation.展开更多
The negative impacts of natural hazards on communities at all scales have been increasing.Floods comprise one such natural hazard that has emerged as one of the most destructive in the US and worldwide.While a lot of ...The negative impacts of natural hazards on communities at all scales have been increasing.Floods comprise one such natural hazard that has emerged as one of the most destructive in the US and worldwide.While a lot of damage is estimated in terms of the cost of rebuilding infrastructure and direct loss of economy,the negative impacts of such disruptions go beyond the physical infrastructure.The impact on(and of)the social and institutional framework is rarely examined in conjunction with the physical and technical aspects.This paper examines flood vulnerability and risk of a community at an intersection of social,ecological,technical,and intuitional perspectives,and presents a framework for a holistic flood vulnerability and risk assessment that has a strong foundation in all four aspects of a resilient community.The study builds on the existing risk,vulnerability,and hazard assessment approaches,and refines them with a holistic perspective.The study uses a mixed method approach with qualitative and quantitative methodologies to assess flood occurrence probabilities,vulnerability,and risk from the social,ecological,technical,and institutional perspectives.A case study of the City of Atlanta is conducted using the framework to assess the overall vulnerability and risk of the city.The results of this analysis show that the regions that have the highest probability of flood hazard occurrence also appear to have the highest social,ecological,and technical vulnerabilities in the Atlanta area.While the results are intuitive,the applications support a focus on holistic resilience building across these four criteria.This study is potentially useful to practitioners,researchers,government agencies,and community organizations working to mitigate flood risk particularly as this risk continues to evolve with the changing climate.展开更多
基金supported by Ecological Conservation and High-Quality Development of the Yellow River Basin program(2022-YRUC-01-0102)the Second Tibetan Plateau Scientific Expedition and Research Program(Grant No.2019QZKK0405).
文摘Understanding the response of ecological vulnerability to global changes is essential for sustainable ecosystem management.However,incorporating ecological vulnerability assessment into accurate decision-making to illustrate ecosystem dynamics and drivers remains unclear.Here,we used the Vulnerability Scoping Diagram model(VSD)and ecological vulnerability index(EVI)to evaluate the ecosystem vulnerability in the upper reach of Yellow River(URYR).Our results show that EVI increased from southwest to northeast,and EVI first went down from 2005 to 2015 and then slightly up from 2015 to 2020.The lower EVI can be attributed to improving environmental protection and restoration projects in the URYR.The subareas of the High-High cluster were distributed in the northeast,and those of the Low-Low cluster were distributed in the southwest,respectively.Furthermore,we divided the URYR into four zones(e.g.,Protection Zone,Ecological Degradation Zone,Ecological Improvement Zone,and Comprehensive Zone)according to the EVI.Consequently,a long-term detection system and public education should be enhanced to improve environmental awareness,which plays a beneficial role in the sustainable development of four zones.All in all,our findings not only shed light on the dynamic of ecological vulnerability but also provide the diversification management of the upper reach of the Yellow River.
基金supported by the"Vietnam Sea for the Goals of National Defence and National Development"project managed by the Office of the Vietnam Academy of Social Sciences。
文摘Vietnam,with its twenty-eight coastal provinces,is one of the nations most profoundly affected by the adverse impacts of climate change(CC).These provinces face severe challenges as they contend with the escalating effects of CC,including rising sea levels,typhoons,flooding,and droughts.In this context,this article aims to assess the vulnerability of households'livelihoods in Quang Nam Province by applying the Livelihood Vulnerability Index(LVI)developed by Hahn et al.,along with the Intergovernmental Panel on Climate Change framework(LVI-IPCC).The study utilises five sources of household capital—human,social,physical,natural,and financial—to construct its indices.The data for this article is based on a survey of 200 households.The research methodology combines both quantitative and qualitative methods,including questionnaire interviews,in-depth interviews,and focus group discussions.The research period spans from 2021 to 2023.The study results revealed that the household LVI was 0.371,while the LVI-IPCC was 0.086,highlighting the critical need for access to food and clean water,which scored 0.458 and 0.351,respectively.The research underscores how CC significantly affects the livelihoods of coastal communities,particularly in sectors such as fishing,aquaculture,and agriculture.The study concludes that CC poses significant challenges to the livelihoods of coastal communities in Quang Nam Province and that adaptation measures are necessary to support these communities.The research highlights the importance of livelihood diversification,job transformation,and improving knowledge and skills to enhance the resilience of coastal communities to CC.
基金supported by the National Key Research and Development Program of China(No.2021YFA0715900).
文摘Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowadays,the groundwater vulnerability assessment(GVA)has become an essential task to identify the current status and development trend of groundwater quality.In this study,the Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)models are integrated to realize the spatio-temporal prediction of regional groundwater vulnerability by introducing the Self-attention mechanism.The study firstly builds the CNN-LSTM modelwith self-attention(SA)mechanism and evaluates the prediction accuracy of the model for groundwater vulnerability compared to other common machine learning models such as Support Vector Machine(SVM),Random Forest(RF),and Extreme Gradient Boosting(XGBoost).The results indicate that the CNNLSTM model outperforms thesemodels,demonstrating its significance in groundwater vulnerability assessment.It can be posited that the predictions indicate an increased risk of groundwater vulnerability in the study area over the coming years.This increase can be attributed to the synergistic impact of global climate anomalies and intensified local human activities.Moreover,the overall groundwater vulnerability risk in the entire region has increased,evident fromboth the notably high value and standard deviation.This suggests that the spatial variability of groundwater vulnerability in the area is expected to expand in the future due to the sustained progression of climate change and human activities.The model can be optimized for diverse applications across regional environmental assessment,pollution prediction,and risk statistics.This study holds particular significance for ecological protection and groundwater resource management.
基金the Higher Education Commission of Pakistan for supporting the study through the CRG-CPEC-130 project。
文摘Climate change and rising temperatures are accelerating the rate of deglaciation in the Hindu Kush Karakoram Himalaya(HKH)ranges,leading to the formation of new glacial lakes and the expansion of existing ones.These lakes are often vulnerable to failure,posing a significant threat to downstream communities and infrastructure.Therefore,a comprehensive assessment of Glacier-Lake Outburst Flood(GLOF)hazards and risk assessment is crucial to evaluate flood runout characteristics and identify settlements and infrastructure that are exposed and vulnerable to floods,aiding in the development and implementation of risk reduction strategies.This study aims to simulate a GLOF event induced by the Shisper glacier lake in northern Pakistan,using the HEC-RAS,and to assess its impact on settlements,infrastructure,and agricultural land.For the hydrometeorological analysis of the GLOF event,topographic data from unmanned aerial vehicles(UAVs),stream profiles,discharge data,Manning's roughness coefficient(n),and land use/land cover(LULC)were analyzed using HEC-RAS and geographic information system(GIS).During the GLOF event on May 7,2022,a maximum water depth of 6.3 m and a maximum velocity of 9.5 m/s were recorded.Based on the runout characteristics of this event,vulnerability and risk assessments have been calculated.The physical,social,and environmental vulnerabilities of the at-risk elements were evaluated using the analytical hierarchy process(AHP)and integrated with the hazard data to develop a risk map.The study identified the areas,infrastructure and settlements susceptible to GLOF hazard to support the development and implementation of targeted and evidence-based mitigation and adaptation strategies.
文摘Bioethics is the argumentative discipline of decisions and actions that reduce conflicts of interest,dilemmas,or asymmetries between the parties involved in biomedical research.The objective of this work was to review and compare the dimensions used by bioethics in the communicative management of the pandemic,namely:risk,vulnerability,resilience,and stigma.A documentary,exploratory,transversal,and retrospective work was carried out with a sample of sources indexed in international repositories,considering the search by keywords and the publication period from 2020 to 2024.The results demonstrate the prevalence of supply,contagion,symptoms,and help against COVID-19.
文摘In Niger, farms have been facing negative effects of climate change for several decades. The objective of this work is to assess the vulnerability of farms in Tillabery department by proposing an adaptation approach. A five-step method and descriptive analysis were used on a sample of 250 farmers. The degree of damage caused by pests and crop diseases is significant, with respective proportions of 52.50% and 40.40%. It appears that the main climate risk factors for vulnerability are droughts, floods, soil degradation, and pest invasions. Additionally, the average level of exposure to agricultural operations is very high, with an index of 0.6. The sensitivity index remained constant in the range of 0.3 to 0.6 and is significant (reaching an index of 0.8). However, 61.2% of farms have a medium level of vulnerability and 33.3% have a high vulnerability to the effects of climate change. Nonetheless, a concerning trend regarding the vulnerability of farms has been observed. To assist policymakers and development actors in improving the vulnerability level of these production units, four phases of action are proposed: a diagnostic phase, evaluation, estimation of adaptation needs, implementation, and proper monitoring of actions.
基金funding provided by University of Natural Resources and Life Sciences Vienna(BOKU).
文摘The increasing frequency and intensity of forest fires,driven by climate change and human activities,pose a significant threat to vital forest ecosystems,particularly where fire is not a natural element in the regeneration cycle.This study aims to identify the indicators influencing forest fire vulnerability and compare maps of forest fire susceptibility that are based on the Intergovernmental Panel on Climate Change tripartite model,with a focus on the vulnerable Hyrcanian forest region in Golestan Province,northern Iran,where forest fires have caused considerable economic losses.On the basis of expert opinions and a literature review,we used geographic information systems,remote sensing and machine learning techniques to select and weigh 30 biophysical,environmental and socioeconomic indicators that affect forest fire vulnerability in the study area.These indicators were rigorously normalized,weighted and amalgamated into a comprehensive forest fire vulnerability index to analyze forest exposure,sensitivity and adaptive capacity.We thus identified and mapped areas with very high forest fire exposure,high sensitivity and low adaptive capacity for urgent targeted intervention and strategic planning to mitigate the impacts of forest fires.The results also revealed a set of critical indicators that contribute more significantly to forest fire vulnerability(e.g.,precipitation,elevation and factors related to biodiversity,human activity and economic reliance on forest resources).Our results provide insights that can inform policy-making,community engagement and environmental management strategies to mitigate the vulnerabilities associated with forest fires in the Hyrcanian forest.
基金supported by grants received by the first author and third author from the Institute of Eminence,Delhi University,Delhi,India,as part of the Faculty Research Program via Ref.No./IoE/2024-25/12/FRP.
文摘Software systems are vulnerable to security breaches as they expand in complexity and functionality.The confidentiality,integrity,and availability of data are gravely threatened by flaws in a system’s design,implementation,or configuration.To guarantee the durability&robustness of the software,vulnerability identification and fixation have become crucial areas of focus for developers,cybersecurity experts and industries.This paper presents a thorough multi-phase mathematical model for efficient patch management and vulnerability detection.To uniquely model these processes,the model incorporated the notion of the learning phenomenon in describing vulnerability fixation using a logistic learning function.Furthermore,the authors have used numerical methods to approximate the solution of the proposed framework where an analytical solution is difficult to attain.The suggested systematic architecture has been demonstrated through statistical analysis using patch datasets,which offers a solid basis for the research conclusions.According to computational research,learning dynamics improves security response and results in more effective vulnerability management.The suggested model offers a systematic approach to proactive vulnerability mitigation and has important uses in risk assessment,software maintenance,and cybersecurity.This study helps create more robust software systems by increasing patch management effectiveness,which benefits developers,cybersecurity experts,and sectors looking to reduce security threats in a growing digital world.
基金supported by grants from the National Natural Science Foundation of China(31861143023 and 31872252)the Fundamental Research Funds for the Central Universities(2572019AA09)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA20050201).
文摘The survival of ectotherms worldwide is threatened by climate change.Whether increasing temperatures increase the vulnerability of ectotherms inhabiting temperate plateau areas remains unclear.To understand altitudinal variation in the vulnerability of plateau ectotherms to climate warming,Qinghai toad-headed lizards(Phrynocephalus vlangalii)were subjected to semi-natural enclosure experiments with simulated warming at high(2,600 m)and superhigh(3,600 m)elevations of the Dangjin Mountain,China.Our results revealed that the thermoregulatory effectiveness and warming tolerance(WT)of the toad-headed lizards were significantly affected by climate warming at both elevations,but their thermal sensitivity remained unchanged.After warming,the thermoregulatory effectiveness of lizards at superhigh elevations decreased because of the improved environmental thermal quality,whereas that of lizards at high-elevation conditions increased.Although the body temperature selected by high-elevation lizards was also significantly increased,the proportion of their active body temperature falling within the set-point temperature range decreased.This indicates that it is difficult for high-elevation lizards to adjust their body temperatures within a comfortable range under climate warming.Variations in the WT and thermal safety margin(TSM)under climate warming revealed that lizards at the superhigh elevation benefited from improved environmental thermal quality,whereas those at the high elevation originally on the edge of the TSM faced more severe threats and became more vulnerable.Our study highlights the importance of thermal biological traits in evaluating the vulnerability of ectotherms in temperate plateau regions.
基金funding from the National Science Foundation(NSF Award 2114701)of the United States.
文摘This study advances the DRASTIC groundwater vulnerability assessment framework by integrating a multi-hazard groundwater index(MHGI)to account for the dynamic impacts of diverse anthropogenic activities and natural factors on both groundwater quality and quantity.Incorporating factors such as population growth,agricultural practices,and groundwater extraction enhances the framework’s ability to capture multi-dimensional,spatiotemporal changes in groundwater vulnerability.Additional improvements include refined weighting and rating scales for thematic layers based on available observational data,and the inclusion of distributed recharge.We demonstrate the practical utility of this dynamic DRASTIC-based framework through its application to the agro-urban regions of the Irrigated Indus Basin,a major groundwater-dependent agricultural area in South Asia.Results indicate that between 2005 and 2020,54%of the study area became highly vulnerable to pollution.The MHGI revealed a 13%decline in potential groundwater storage and a 25%increase in groundwater-stressed zones,driven primarily by population growth and intensive agriculture.Groundwater vulnerability based on both groundwater quality and quantity dimensions showed a 19%decline in areas of low to very low vulnerability and a 6%reduction in medium vulnerability zones by 2020.Sensitivity analyses indicated that groundwater vulnerability in the region is most influenced by groundwater recharge(42%)and renewable groundwater stress(38%).Validation with in-situ data yielded area under the curve values of 0.71 for groundwater quality vulnerability and 0.63 for MHGI.The framework provides valuable insights to guide sustainable groundwater management,safeguarding both environmental integrity and human well-being.
基金supported by Mission No. 9 "Geological Environment and Hazards" (2019QZKK0900) of "The Second Tibetan Plateau Scientific Expedition and Research" projectNational Natural Science Foundation of China (No.42101087)
文摘Spatial seismic vulnerability assessments are primally conducted at the community and grid level,using heuristic and empirical approaches.Building-based spatial statistical vulnerability models are rare because of data limitations.Generating open-access spatial inventories that document seismic damage and building attributes and test their effectiveness in assessing damage would promote the advancement of spatial vulnerability assessment.The 2022 Mw 6.7 Luding earthquake in the western Sichuan Province of China provides an opportunity to validate this approach.The local government urgently dispatched experts to survey building damage,marking all buildings with damage class stickers.In this work,we sampled 2889 buildings as GPS points and documented the damage classes and building attributes,including structure type,number of floors,and age.A polygon-based digital inventory was generated by digitizing the rooftops of the sampled buildings and importing the attributes.Statistical regressions were created by plotting damage against shaking intensity and PGA,and Random Forest modeling was carried out considering not only buildings and seismic parameters but also environmental factors.The result indicates that statistical regressions have notable uncertainties,and the Random Forest model shows a≥79%accuracy.Topographical factors showed notable importance in the Random Forest modeling.This work provides an open-access seismic building damage inventory and demonstrates its potential for damage prediction and vulnerability assessment.
基金Project(52178402) supported by the National Natural Science Foundation of China。
文摘This paper proposes a longitudinal vulnerability-based analysis method to evaluate the impact of foundation pit excavation on shield tunnels,accounting for geological uncertainties.First,the shield tunnel is modeled as an Euler Bernoulli beam resting on the Pasternak foundation incorporating variability in subgrade parameters along the tunnel’s length.A random analysis method using random field theory is introduced to evaluate the tunnel’s longitudinal responses to excavation.Next,a risk assessment index system is established.The normalized relative depth between the excavation and the shield tunnel is used as a risk index,while the maximum longitudinal deformation,the maximum circumferential opening,and the maximum longitudinal bending moment serve as performance indicators.Based on these,a method for analyzing the longitudinal fragility of shield tunnels under excavation-induced disturbances is proposed.Finally,the technique is applied to a case study involving a foundation pit excavation above a shield tunnel,which is the primary application scenario of this method.Vulnerability curves for different performance indicators are derived,and the effects of tunnel stiffness and subgrade stiffness on the tunnel vulnerability are explored.The results reveal significant differences in vulnerability curves depending on the performance index used.Compared to the maximum circumferential opening and the maximum longitudinal bending moment,selecting the maximum longitudinal deformation as the control index better ensures the tunnel’s usability and safety under excavation disturbances.The longitudinal vulnerability of the shield tunnel nonlinearly decreases with the increase of the tunnel stiffness and subgrade stiffness,and the subgrade stiffness has a more pronounced effect.Parametric analyses suggest that actively reinforcing the substratum is more effective on reducing the risk of tunnel failure due to adjacent excavations than passive reinforcement of the tunnel structure.
基金partially supported by the National Natural Science Foundation (62272248)the Open Project Fund of State Key Laboratory of Computer Architecture,Institute of Computing Technology,Chinese Academy of Sciences (CARCHA202108,CARCH201905)+1 种基金the Natural Science Foundation of Tianjin (20JCZDJC00610)Sponsored by Zhejiang Lab (2021KF0AB04)。
文摘Smart contracts are widely used on the blockchain to implement complex transactions,such as decentralized applications on Ethereum.Effective vulnerability detection of large-scale smart contracts is critical,as attacks on smart contracts often cause huge economic losses.Since it is difficult to repair and update smart contracts,it is necessary to find the vulnerabilities before they are deployed.However,code analysis,which requires traversal paths,and learning methods,which require many features to be trained,are too time-consuming to detect large-scale on-chain contracts.Learning-based methods will obtain detection models from a feature space compared to code analysis methods such as symbol execution.But the existing features lack the interpretability of the detection results and training model,even worse,the large-scale feature space also affects the efficiency of detection.This paper focuses on improving the detection efficiency by reducing the dimension of the features,combined with expert knowledge.In this paper,a feature extraction model Block-gram is proposed to form low-dimensional knowledge-based features from bytecode.First,the metadata is separated and the runtime code is converted into a sequence of opcodes,which are divided into segments based on some instructions(jumps,etc.).Then,scalable Block-gram features,including 4-dimensional block features and 8-dimensional attribute features,are mined for the learning-based model training.Finally,feature contributions are calculated from SHAP values to measure the relationship between our features and the results of the detection model.In addition,six types of vulnerability labels are made on a dataset containing 33,885 contracts,and these knowledge-based features are evaluated using seven state-of-the-art learning algorithms,which show that the average detection latency speeds up 25×to 650×,compared with the features extracted by N-gram,and also can enhance the interpretability of the detection model.
文摘Coasts are subject to multiple natural hazards,which are increasing nowadays.Coastal flooding and erosion are some of the most common hazards affecting coastlines.Being aware of the vulnerability of coasts is important to achieve integrated coastal management.The coastal vulnerability index(CVI)is a common index used to assess coastal vulnerability because it is easily calculated.However,given that its calculation includes numerous manual steps,it requires considerable time,which is often unavailable,to produce accurate and utilizable results.In this work,we developed a ModelBuilder model by using the tools provided by ArcGIS Pro(ESRI).Through this model,we automatized most of the steps involved in CVI calculation.We applied the ModelBuilder model in the northern Peloponnese,for which the CVI has already been calculated in three other works.We were thus able to assess the effectiveness of our ModelBuilder model.Our results demonstrated that through the ModelBuilder,most of the processes could effectively be automatized without problems,and our results are consistent with the findings of previous works in our study area.
基金supported by the MSIT(Ministry of Science and ICT),Republic of Korea,under the Convergence Security Core Talent Training Business Support Program(IITP-2025-RS-2023-00266605,50%)in part by the Institute of Information&Communications Technology Planning&Evaluation(lITP)grant funded by the Korea government(MSIT)(RS-2025-02305436,Development of Digital Innovative Element Technologies for Rapid Prediction of Potential Complex Disasters and Continuous Disaster Prevention,30%)supported by the Chung-Ang University Graduate Research Scholar-ship in 2023(20%).
文摘The escalating complexity and heterogeneity of modern energy systems—particularly in smart grid and distributed energy infrastructures—has intensified the need for intelligent and scalable security vulnerability classification.To address this challenge,we propose Vulnerability2Vec,a graph-embedding-based framework designed to enhance the automated classification of security vulnerabilities that threaten energy system resilience.Vulnerability2Vec converts Common Vulnerabilities and Exposures(CVE)text explanations to semantic graphs,where nodes represent CVE IDs and key terms(nouns,verbs,and adjectives),and edges capture co-occurrence relationships.Then,it embeds the semantic graphs to a low-dimensional vector space with random-walk sampling and skip-gram with negative sampling.It is possible to identify the latent relationships and structural patterns that traditional sparse vector methods fail to capture.Experimental results demonstrate a classification accuracy of up to 80%,significantly outperforming baseline methods.This approach offers a theoretical basis for classifying vulnerability types as structured semantic patterns in complex software systems.The proposed method models the semantic structure of vulnerabilities,providing a theoretical foundation for their classification.
文摘RESTful APIs have been adopted as the standard way of developing web services,allowing for smooth communication between clients and servers.Their simplicity,scalability,and compatibility have made them crucial to modern web environments.However,the increased adoption of RESTful APIs has simultaneously exposed these interfaces to significant security threats that jeopardize the availability,confidentiality,and integrity of web services.This survey focuses exclusively on RESTful APIs,providing an in-depth perspective distinct from studies addressing other API types such as GraphQL or SOAP.We highlight concrete threats-such as injection attacks and insecure direct object references(IDOR)-to illustrate the evolving risk landscape.Our work systematically reviews state-of-the-art detection methods,including static code analysis and penetration testing,and proposes a novel taxonomy that categorizes vulnerabilities such as authentication and authorization issues.Unlike existing taxonomies focused on general web or network-level threats,our taxonomy emphasizes API-specific design flaws and operational dependencies,offering a more granular and actionable framework for RESTful API security.By critically assessing current detection methodologies and identifying key research gaps,we offer a structured framework that advances the understanding and mitigation of RESTful API vulnerabilities.Ultimately,this work aims to drive significant advancements in API security,thereby enhancing the resilience of web services against evolving cyber threats.
基金supported by the Supported by Joint Open Research Fund Program of State Key Laboratory of Hydroscience and Engineering and Tsinghua-Ningxia Yinchuan Joint Institute of Internet of Waters on Digital Water Governance(No.sklhse-2022-Iow04)Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin,China Institute of Water Resources and Hydropower Research(No.IWHR-SKL-202217)。
文摘Climate change and economic development impact the population expansion and water shortage in the middle reaches of the Yangtze River Basin(MYRB),leading directly to drought aggravation-expansion and impacting agricultural production in the MYRB.Therefore,this study quantitatively evaluated agricultural drought vulnerability in the MYRB based on the variable fuzzy evaluation model(VFEM).The main conclusions are as follows:(1)The arable land in the MYRB gradually decreased between 2005 and 2020,whereas the forest cover decreased and then increased;(2)precipitation and evapotranspiration were the key factors affecting the agricultural drought vulnerability(e.g.,weights of 0.24 and 0.21),whereas arable land density and gross domestic product(GDP)per capita played less significant roles;and(3)the agricultural drought vulnerability in the MYRB during 2005-2020 was mainly at level 3 and below,with higher drought vulnerability in the western and northern regions,which had a higher drought risk potential.
文摘Assessing the vulnerability of a platform is crucial in its design.In fact,the results obtained from vulnerability analyses provide valuable information,leading to precise design choices or corrective solutions that enhance the platform's chances of surviving different scenarios.Such scenarios can involve various types of threats that can affect the platform's survivability.Among such,blast waves impacting the platform's structure represent critical conditions that have not yet been studied in detail.That is,frameworks for vulnerability assessment that can deal with blast loading have not been presented yet.In this context,this work presents a fast-running engineering tool that can quantify the risk that a structure fails when it is subjected to blast loading from the detonation of high explosive-driven threats detonating at various distances from the structure itself.The tool has been implemented in an in-house software that calculates vulnerability to various impacting objects,and its capabilities have been shown through a simplified,yet realistic,case study.The novelty of this research lies in the development of an integrated computational environment capable of calculating the platform's vulnerability to blast waves,without the need for running expensive finite element simulations.In fact,the proposed tool is fully based on analytical models integrated with a probabilistic approach for vulnerability calculation.
文摘The negative impacts of natural hazards on communities at all scales have been increasing.Floods comprise one such natural hazard that has emerged as one of the most destructive in the US and worldwide.While a lot of damage is estimated in terms of the cost of rebuilding infrastructure and direct loss of economy,the negative impacts of such disruptions go beyond the physical infrastructure.The impact on(and of)the social and institutional framework is rarely examined in conjunction with the physical and technical aspects.This paper examines flood vulnerability and risk of a community at an intersection of social,ecological,technical,and intuitional perspectives,and presents a framework for a holistic flood vulnerability and risk assessment that has a strong foundation in all four aspects of a resilient community.The study builds on the existing risk,vulnerability,and hazard assessment approaches,and refines them with a holistic perspective.The study uses a mixed method approach with qualitative and quantitative methodologies to assess flood occurrence probabilities,vulnerability,and risk from the social,ecological,technical,and institutional perspectives.A case study of the City of Atlanta is conducted using the framework to assess the overall vulnerability and risk of the city.The results of this analysis show that the regions that have the highest probability of flood hazard occurrence also appear to have the highest social,ecological,and technical vulnerabilities in the Atlanta area.While the results are intuitive,the applications support a focus on holistic resilience building across these four criteria.This study is potentially useful to practitioners,researchers,government agencies,and community organizations working to mitigate flood risk particularly as this risk continues to evolve with the changing climate.
