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The occurrence of extreme heat and its adverse effects will be exacerbated with the trend of global warming. An increasing number of researchers have been working on aggregating multiple heat-related indicators to create composite indices for heat vulnerability assessments and have visualized the vulnerability through geographic information systems to provide references for reducing the adverse effects of extreme heat more effectively. This review includes 15 studies concerning heat vulnerability assessment. We have studied the indicators utilized and the methods adopted in these studies for the construction of the heat vulnerability index (HVI) and then further reviewed some of the studies that validated the HVI. We concluded that the HVI is useful for targeting the intervention of heat risk, and that heat-related health outcomes could be used to validate and optimize the HVI. In the future, more studies should be conducted to provide references for the selection of heat-related indicators and the determination of weight values of these indicators in the development of the HVI. Studies concerning the application of the HVI are also needed.

ContextA significant forest decline has been noticed these last years in Europe. Managers need tools to better anticipate these massive events.ObjectivesWe evaluated the efficiency of easily available data about environmental conditions and stand characteristics to determine different levels of vulnerability.MethodsWe combined remote sensing images, photo-interpretation, and digital models describing environmental conditions within a modelling approach to achieve spatial vulnerability assessment of the stands. We focused on silver fir and Norway spruce stands in the Vosges mountains (8900 km2, northeastern France), where severe symptoms of decline are visible.ResultsSilver fir were predicted highly vulnerable on 7% of their area versus 33% for Norway spruce. Using an independent dataset, we observed ten-times (silver fir) and two-times (Norway spruce) higher mortality rates in the units with a high level of vulnerability than in the others. About half of the model deviance was directly or indirectly explained by variables related to water stress (soils displaying low water availability, having suffered severe drying events these last years). Furthermore, the stands acclimatised to drought conditions were more resilient. Stand characteristics also influenced dieback spread, suggesting that an evolution of silvicultural practices toward mixed stands with broadleaved species and uneven-aged trees can contribute to better adapt to future climate conditions.ConclusionVulnerability maps based on easily available geographic information describing climate, soil, and topography can efficiently discriminate canopy mortality patterns over broad areas, and can be useful tools for managers to mitigate the effects of climate change on forests.

Vulnerability assessment is one of the prerequisites for risk analysis in disaster management. Vulnerability to earthquakes, especially in urban areas, has increased over the years due to the presence of complex urban structures and rapid development. Urban vulnerability is a result of human behavior which describes the extent of susceptibility or resilience of social, economic, and physical assets to natural disasters. The main aim of this paper is to develop a new hybrid framework using Analytic Network Process (ANP) and Artificial Neural Network (ANN) models for constructing a composite social, economic, environmental, and physical vulnerability index. This index was then applied to Tabriz City, which is a seismic-prone province in the northwestern part of Iran with recurring devastating earthquakes and consequent heavy casualties and damages. A Geographical Information Systems (GIS) analysis was used to identify and evaluate quantitative vulnerability indicators for generating an earthquake vulnerability map. The classified and standardized indicators were subsequently weighed and ranked using an ANP model to construct the training database. Then, standardized maps coupled with the training site maps were presented as input to a Multilayer Perceptron (MLP) neural network for producing an Earthquake Vulnerability Map (EVM). Finally, an EVM was produced for Tabriz City and the level of vulnerability in various zones was obtained. South and southeast regions of Tabriz City indicate low to moderate vulnerability, while some zones of the northeastern tract are under critical vulnerability conditions. Furthermore, the impact of the vulnerability of Tabriz City on population during an earthquake was included in this analysis for risk estimation. A comparison of the result produced by EVM and the Population Vulnerability (PV) of Tabriz City corroborated the validity of the results obtained by ANP-ANN. The findings of this paper are useful for decision-makers and government authorities to obtain a better knowledge of a city’s vulnerability dimensions, and to adopt preparedness strategies in the future for Tabriz City. The developed hybrid framework of ANP and ANN Models can easily be replicated and applied to other urban regions around the world for sustainability and environmental management.

Soil samples from 31 locations in the Una river spring catchment were subject to chemical extraction analyses. The data were presented as distribution maps of potentially toxic elements (Al, Cu, Mn, Pb and Zn) in the surface soil of the area. To evaluate the vulnerability of the immediate spring zone of the karst catchment, the vulnerability map was derived from the application of the PI methodology proposed by the European COST Action 620. The PI method used to produce the vulnerability map takes into account the protective cover (P) and the infiltration conditions (I). It is based on the origin-pathway-target model. The pi -factor ( pi = P I) describes the vulnerability in the area, subdivided into 5 classes: pi -factor in the range 0-1 implies an area of extreme vulnerability, while pi -factor in the range 4-5 implies an area of very low vulnerability. The extraction procedure for the elements Al, Cu, Mn, Pb and Zn, has been applied in order to determine the potential mobility and redistribution of elements that could influence the groundwater and affect its quality. The applied extraction was the second step of the sequential procedure proposed by TESSIER et al. (1979), i.e. extraction with 1 mol dm super( -3) CH sub( 3)COONa/CH sub( 3)COOH buffer (pH 5). The results provide information on the potential mobility of the studied elements, indicating the possibility of their mobilization through changes in pH. Lead shows the greatest amount of mobility, with a mean of 9% (max. 16%) extracted under an acidic condition. Manganese follows with a mean of 5% (max. 11%) and zinc, copper and aluminium show less than 1% (mean) mobility. The vulnerability map of the karst area was produced in order to predict potential problem areas of karst aquifers. The Una spring catchment area presents generally low to moderate vulnerability; 8% of the studied area can be considered as extremely vulnerable according to the PI-methodology. Based on these data it was possible to delineate areas with a low protection cover i.e. combining the vulnerability map of the karst area with the distribution maps of potentially toxic elements, areas considered extremely vulnerable could be identified.

Heat vulnerability maps are vital for identifying at-risk areas and guiding interventions, yet their relationship with health outcomes is underexplored. This study investigates the uncertainty in heat vulnerability maps generated using health outcomes and various statistical models. We constructed vulnerability maps for 167 municipalities in Korea, focusing on the mild and severe health impacts of heat waves on morbidity and mortality. The outcomes included incidence rates of heat-related outpatient visits (morbidity) and attributable mortality rates (mortality) among individuals aged 65 years and older. To construct these maps, we utilized 11 socioeconomic variables related to population, climate, and economic factors. Both linear and nonlinear statistical models were employed to assign these socioeconomic variables to heat vulnerability. We observed variations in the crucial socioeconomic variables affecting morbidity and mortality in the vulnerability maps. Notably, nonlinear models depicted the spatial patterns of health outcomes more accurately than linear models, considering the relationship between health outcomes and socioeconomic variables. Our findings emphasize the differences in the spatial distribution of heat vulnerability based on health outcomes and the choice of statistical models. These insights underscore the importance of selecting appropriate models to enhance the reliability of heat vulnerability maps and their relevance for policy-making.

This study presents the application of an artificial neural network (ANN) and geographic information system (GIS) for estimating the social vulnerability to earthquakes in the Tabriz city, Iran. Thereby, seven indicators were identified and used for earthquake vulnerability mapping, including population density, household density, employed density, unemployed density, and literate people. To obtain more accuracy in our analysis, all of the indicators were entered into a geographic information system (GIS). After the standardization of the data, an artificial neural network (ANN) model was applied for deriving a social vulnerability map (SVM) of different hazard classes for Tabriz city. The results showed that 0.77% of the total area was found to be very highly vulnerable. Very low vulnerability was recorded for 76.31% of the total study area. The comparison of data provided by (SVM) and the residential building vulnerability (RBV) of Tabriz city indicated the validity of the results obtained by ANN processes. Scatter plots are used to plot the data. These scatter plots indicate the existence of a strong positive relationship between the most vulnerable zones (1, 4, and 5) and the least (3, 7, and 9) of the SVM and RBV. The results highlight the importance of using social vulnerability study for defining seismic-risk mitigation policies, emergency management, and territorial planning in order to reduce the impacts of disasters.

Demarcation of the potential zones for groundwater artificial recharge (GAR) based on the most influential factors is an urgent need for retardation of saltwater intrusion and, thus, sustainability of groundwater resources in the arid zones. This study developed an overlay-index methodology to delineate favorable GAR zones by a linear combination of 11 influential thematic layers in ArcGIS. The proposed methodology was implemented on two coastal aquifer settings Sharif-Abad (SAA) and Qom-Kahak (QKA) aquifers adjacent to Salt Lake, Central Iran. Results indicated that 16.41% of the surface of SAA and 28.58% of QKA were identified as the high potential zone for GAR mainly located in low GW vulnerability parts. Based on the analysis of the area under the receptive operating curve (AUC), the produced GAR map has an accuracy of 0.643, and 0.611 for SAA and QKA aquifers, respectively, which relies on the acceptable limit. Finally, the quantity of water required for GAR to control the intrusion of seawater at the suitable parts of these aquifers was estimated as 25 MCM and 35 MCM, annually. The methodology adopted in this study can serve as a holistic assessment for the detection of SWI in coastal aquifers, and also a comprehensive blueprint for managers to delineate the favorable GAR zones, especially in arid regions.

Adversarial patch attacks threaten the reliability of modern vision models. We present PatchMap, the first spatially exhaustive benchmark of patch placement, built by evaluating over 1.5×108 forward passes on ImageNet validation images. PatchMap reveals systematic “hot-spots” where small patches (as little as 2% of the image) induce confident misclassifications and large drops in model confidence. To demonstrate its utility, we propose a simple segmentation-guided placement heuristic that leverages off-the-shelf masks to identify vulnerable regions without any gradient queries. Across five architectures-including adversarially trained ResNet-50-our method boosts attack success rates by 8–13 percentage points compared to random or fixed placements.

Natural hazards and severe weather events are a matter of serious threat to humans, economic activities, and the environment. Flash floods are one of the extremely devastating natural events around the world. Consequently, the prediction and precise assessment of flash flood-prone areas are mandatory for any flood mitigation strategy. In this study, a new hybrid approach of machine learning (ML) algorithm and hydrologic indices opted to detect impacted and highly vulnerable areas. The obtained models were trained and validated using a total of 189 locations from Wadi Ghoweiba and surrounding area (case study). Various controlling factors including varied datasets such as stream transport index (STI), stream power index (SPI), lithological units, topographic wetness index (TWI), slope angle, stream density (SD), curvature, and slope aspect (SA) were utilized via hyper-parameter optimization setting to enhance the performance of the proposed model prediction. The hybrid machine learning (HML) model, developed by combining naïve Bayes (NïB) approach and hydrologic indices, was successfully implemented and utilized to investigate flash flood risk, sediment accumulation, and erosion predictions in the studied site. The synthesized new hybrid model demonstrated a model accuracy of 90.8% compared to 87.7% of NïB model, confirming the superior performance of the obtained model. Furthermore, the proposed model can be successfully employed in large-scale prediction applications.

Fatalities caused by natural hazards are driven not only by population exposure, but also by their vulnerability to these events, determined by intersecting characteristics such as education, age and income. Empirical evidence of the drivers of social vulnerability, however, is limited due to a lack of relevant data, in particular on a global scale. Consequently, existing global‐scale risk assessments rarely account for social vulnerability. To address this gap, we estimate regression models that predict fatalities caused by past flooding events (n = 913) based on potential social vulnerability drivers. Analyzing 47 variables calculated from publicly available spatial data sets, we establish five statistically significant vulnerability variables: mean years of schooling; share of elderly; gender income gap; rural settlements; and walking time to nearest healthcare facility. We use the regression coefficients as weights to calculate the “Global‐Empirical Social Vulnerability Index (GlobE‐SoVI)” at a spatial resolution of ∼1 km. We find distinct spatial patterns of vulnerability within and across countries, with low GlobE‐SoVI scores (i.e., 1–2) in for example, Northern America, northern Europe, and Australia; and high scores (i.e., 9–10) in for example, northern Africa, the Middle East, and southern Asia. Globally, education has the highest relative contribution to vulnerability (roughly 58%), acting as a driver that reduces vulnerability; all other drivers increase vulnerability, with the gender income gap contributing ∼24% and the elderly another 11%. Due to its empirical foundation, the GlobE‐SoVI advances our understanding of social vulnerability drivers at global scale and can be used for global (flood) risk assessments. Plain Language Summary Social vulnerability is rarely accounted for in global‐scale risk assessments. We develop an empirical social vulnerability map (“GlobE‐SoVI”) based on five key drivers of social vulnerability to flooding, that is, education, elderly, income inequality, rural settlements and travel time to healthcare, which we establish based on flood fatalities caused by past flooding events. Globally, we find education to have a high and reducing effect on social vulnerability, while all other drivers increase vulnerability. Integrating social vulnerability in global‐scale (flood) risk assessments can help inform global policy frameworks that aim to reduce risks posed by natural hazards and climate change as well as to foster more equitable development globally. Key Points We develop a global map of social vulnerability at ∼1 km spatial resolution based on five key vulnerability drivers (“GlobE‐SoVI”) We establish vulnerability drivers empirically based on their contribution to predicting fatalities caused by past flooding events Accounting for social vulnerability in global‐scale (flood) risk assessments can inform global policy frameworks that aim to reduce risk