Vulnerability Map Research Articles

Preparedness of the community in facing disasters like earthquakes (Case: Cisarua, Indonesia).

The study findings highlight the village community's earthquake disaster preparedness with the support of integrated spatial mapping of disaster vulnerability. The lack of awareness of the village community in earthquake disaster mitigation increases the level of disaster risk in their area.

Enhancing Sustainability in Road Infrastructure: A Comprehensive GIS Approach to Vulnerability Mapping in the Marand-Bazargan Freeway, Iran

Enhancing Sustainability in Road Infrastructure: A Comprehensive GIS Approach to Vulnerability Mapping in the Marand-Bazargan Freeway, Iran

Assessment of coastal vulnerability using integrated fuzzy analytical hierarchy process and geospatial technology for effective coastal management.

The vulnerability of coastal regions to climate change is a growing global concern, particularly in Bangladesh, which is vulnerable to flooding and storm surges due to its low-lying coastal areas. In this study, we used the fuzzy analytical hierarchy process (FAHP) method to assess the physical and social vulnerability of the entire coastal areas of Bangladesh, using 10 critical factors to evaluate the coastal vulnerability model (CVM). Our analysis indicates that a significant portion of the coastal regions of Bangladesh is vulnerable to the impacts of climate change. We found that one-third of the study area, encompassing around 13,000 km2, was classified as having high or very high coastal vulnerability. Districts in the central delta region, such as Barguna, Bhola, Noakhali, Patuakhali, and Pirojpur, were found to have high to very high physical vulnerability. Meanwhile, the southern parts of the study area were identified as highly socially vulnerable. Our findings also showed that the coastal areas of Patuakhali, Bhola, Barguna, Satkhira, and Bagerhat were particularly vulnerable to the impacts of climate change. The coastal vulnerability map we developed using the FAHP method showed satisfactory modeling, with an AUC of 0.875. By addressing the physical and social vulnerability factors identified in our study, policymakers can take proactive steps to ensure the safety and wellbeing of coastal residents in the face of climate change.

Assessment of groundwater vulnerability to pollution using DRASTIC and the SI methods: case of the alluvial aquifer in Tadjenanet-Chelghoum laid (East Algeria)

The region of Chelghoum Laid - Tadjenanet is located in the east of Algeria in the high southern plains of Setif, characterized by a highly vulnerable shallow alluvial aquifer. The vulnerability of groundwater to pollutants is a relative concept, not measurable or dimensional. The nature, quality, and reliability of used data used have a major impact on the correctness of its assessment. Its classification is usually based on the estimation of many more or less essential factors, such as the characteristics of soil and unsaturated zone, the saturated zone, the recharge , the topography and the hydraulic conductivity. The vulnerability of the aquifer to pollution was investigated using two intrinsic vulnerability methods: DRASTIC and susceptibility index (SI). SI method is an adaptation of the DRASTIC specifically design for nitrate-based pollution. The parameters used as input data are, among others, the depth of the groundwater, the soil type, the topographic slope and the groundwater recharge. The validity of the two methods for assessing the vulnerability to nitrates was established by comparing the distribution of these elements in groundwater with the distribution of the various vulnerability classes. GIS techniques were used to implement these methods. Vulnerability maps created using the DRASTIC and SI method’s depict the potential for pollutants to penetrate and spread in these locations depending on the terrain encountered on the surface and the depth of the aquifer. The comparison revealed that the SI technique is the most accurate in the studied alluvial aquifer. The establishment of the pollution vulnerability map highlighted an area of great vulnerability in the center of the plain, reflected by the fragility of the soil and the shallow depth of the water. While the average vulnerability areas are in the center, and east at the periphery of Wadi Rhumel, the rest of the field is slightly vulnerable. The vulnerability and contamination risk maps created for this study are valuable tools for environmental planning and can be used for predictive management of groundwater resources. This case study suggests that the approach may be applicable to other areas as part of efforts to target groundwater management efforts.

Land degradation vulnerability mapping in a west coast river basin of India using analytical hierarchy process combined machine learning models.

Assessment and modelling of land degradation are crucial for the management of natural resources and sustainable development. The current study aims to evaluate land degradation by integrating various parameters derived from remote sensing and legacy data with analytical hierarchy process (AHP) combined machine learning models for the Mandovi river basin of western India. Various land degradation conditioning factors comprising of topographical, vegetation, pedological, and climatic variables were considered. Integration of the factors was performed through weighted overlay analysis to generate the AHP-based land degradation map. The output of AHP was then used with land degradation conditioning factors to build AHP combined gradient boosting machine (AHP-GBM), random forest (AHP-RF), and support vector machine (AHP-SVM) model. The model performances were assessed through an area under the receiver operating characteristic (AUC). The AHP-RF model recorded the highest AUC (0.996) followed by AHP-SVM (0.987), AHP (0.977), and AHP-GBM (0.975). The study revealed that AHP combined with RF could significantly improve the model performance over solo AHP. High rainfall with high slopes and improper land use were the major causes of land degradation in the study area. The findings of the current study will aid the policymakers to formulate land degradation action plans through implementing appropriate soil and water conservation measures.

Integrated Coastal Vulnerability Assessment: A Study of Karnataka, India

Coastal regions are changing due to environmental pressures and anthropogenic impacts such as coastal development and planning, ultimately becoming sensitive to unpredicted natural and manmade calamities. The scientific assessment requires an understanding of the relative vulnerability of the coast across a region, and, for this purpose, vulnerability indices are often used. This study is an integrated coastal vulnerability index for the Karnataka coastline, incorporating both physical variables and socio-economic indicators. To study the overall vulnerability of a region at the taluka level using the typical physical variables characterizing coastal vulnerability e.g. historical shoreline change, rate of relative sea-level change, coastal regional elevation, coastal slope, mean tidal range, significant wave height and geomorphology. Physical indicators are then combined with the outcomes of an assessment of socio-economic vulnerability developed using population and tourist density and education as indicators like human development index (HDI), composite taluka development index (CTDI) and urban development index (UDI), resulting in a more holistic assessment of vulnerability. The resulting vulnerability map describes the taluka as most vulnerable to physical indicators and also provides information on the nature of vulnerability in terms of its physical and socio-economic components intending to inform adaptation decision-making.

Banana bunchy top disease in Africa—Predicting continent‐wide disease risks by combining survey data and expert knowledge

AbstractAcross Africa, banana bunchy top disease (BBTD) severely impacts banana production and livelihoods of millions of smallholder farmers. Mapping vulnerability of landscapes to monitor BBTD establishment and spread is crucial for proactive measures of disease exclusion. To highlight current and future risks of BBTD in Africa, the relationship between 1160 field observations from 14 BBTD surveys and environmental covariate maps was determined using logistic regression. From these relationships, we inferred the environmental suitability of the African landscape for the possible wider spread of BBTD. Using this information and expert knowledge, we generated a map highlighting the main banana production areas at risk of BBTD entry and establishment. We combined these maps to create a priority map that highlights the areas that need most attention in combating BBTD through surveillance and measures to prevent its spread. Our analysis shows that BBTD is widespread across tropical Africa, with dispersal over several hotspots. Central and Western Africa are most favourable for the development of BBTD. Central, West and South‐East Africa are most at risk of BBTD entry and initial establishment. Areas in West and Central Africa, in the Great Lakes Region in Eastern Africa and in South‐East Africa, particularly in Malawi and Mozambique, score high on the prioritization index for surveillance and mitigation efforts. Recent reports of BBTD presence in north‐western Uganda and western Tanzania support these risk predictions. For these and other not‐yet‐infected areas, measures for close surveillance and proactive management of the disease are needed.

Social Vulnerability Contours (SVCs) for Disaster Vulnerability and Resilience Mapping

Mass flooding events are predominant hydrometeorological disasters in the modern world. This situation has worsened due to the accelerating climate change scenarios. This is because social vulnerability to flood disasters has become one of the greatest challenges in the world. Similarly, Sri Lanka as a middle-income country in the developing world, has been experiencing mass flooding events for decades. Many emerging studies have paid their upmost attention to model the flood disaster vulnerability. But a little has been explored about the understanding of the “Spatiality” or “Geographies” of social vulnerability and resilience status and distributional patterns. In light of the existing body of literature, this study aims to demonstrate a new mechanism for mapping the spatial patterns of social vulnerability and resilience for flooding called “Social Vulnerability Contours (SVCs)”. This study occupied with the multi-facet composite social vulnerability index (MFCSVI) in order to measure the values of each and every surveyed household for generating SVCs. The 213 flood affected households situated in eight Grama Niladari Divisions (GNDs) were considered for the empirical data collection. General normalization procedure was used to scale up all the variable values with different measures to a single scale. Study occupied with ArcGIS 10.4 for generating SVCs were based upon households’ MFCSVI values. The resultant SVCs revealed the significant spatial distribution patterns of social vulnerability and resilience through sampled GNDs. A good consistency of data distribution can be expected with increasing the sample size among GNDs.

LANDSLIDE VULNERABILITY MAP OF BARANGAY FATIMA, PANTABANGAN, NUEVA ECIJA

Landslides are catastrophic geological risks that are unexpected. This study aims to develop a Landslide Vulnerability Map of Barangay Fatima, Pantabangan, Nueva Ecija. Landslide Mapping has been developed to mitigate the adverse effect of landslide. This study interprets and integrate landslide factors; elevation, slope, rainfall, soil classification, land cover, and proximity to road and fault lines to generate a landslide vulnerability map. The eigenvectors rendered by experts engaged are evaluated for consistency to obtain credible and acceptable findings using AHP. The results showed that rainfall had the largest eigenvector (32.66%), then slope (20.80%), soil texture (15.96%), proximity to fault line (14.02%), land cover (8.27%), proximity to a road (4.63%), and elevation (3.66%). This suggests that rainfall and slope are more important than the other factors. Moreover, monitoring of landslide occurrences in the area is advised so that future research can confirm the model's conclusions.

Cumulative Spatial and Temporal Analysis of Anthropogenic Impacts in the Protected Area of the Gran Paradiso National Park in the NW Alps, Italy

Anthropogenic threats are responsible for habitat degradation and biodiversity decline. The mapping of the distribution and intensity of threats to biodiversity can be useful for informing efficient planning in protected areas. In this study, we propose a cumulative spatial and temporal analysis of anthropogenic impacts insisting on an alpine protected area, the Gran Paradiso National Park. The applied methodology starts with the construction of a spatial and temporal dataset of anthropogenic impacts and normalization based on relative intensity. The impacts analyzed include overgrazing, helicopter flights, road networks, built-up areas, worksites, derivations and discharges, sports activities, and dams and hydroelectric power plants. Each impact was assigned a weight based on its temporal persistence. Threats maps obtained from the collected, normalized, and weighted geodata are thus obtained. Finally, the risk map is calculated by combining the impact map with the vulnerability map, estimated through the methodology outlined in the Green Guidelines of the Metropolitan City of Turin. The risk map obtained was cross-referenced with the Park’s cartography to highlight any critical issues to specific habitats. Results show that most of the territory falls in low-risk (63%) or no-risk (35%) areas. However, there are some habitats that are totally or nearly totally affected by some degree of risk, although different to zero, such as the “Lentic waters with aquatic vegetation [incl. cod. 3130]”, the “Lentic waters partially buried”, the “Mountain pine forests (Pinus uncinata) [cod. 9430]”, and the “Mixed hygrophilous woods of broad-leaved trees [incl. cod. 91E0]”. This study highlights both the potential of these analyses, which enable informed management and planning of the fruition of protected areas, and the limitations of such approaches, which require in-depth knowledge of the territory and ecosystems and how they respond to threats in order to refine the model and obtain realistic maps.

Coastal vulnerability assessment for the coast of Tamil Nadu, India-a geospatial approach.

A coastal region is a section of land that borders a significant body of water, often the sea or ocean. Despite their productivity, they are sensitive to even little alterations in the outside environment. This study aims to develop a spatial coastal vulnerability index (CVI) map for the Tamil Nadu coast of India, which has diverse coastal and marine environments that are ecologically fragile zones. Climate change is expected to increase the intensity and frequency of severe coastal hazards, such as rising sea levels, cyclones, storm surges, tsunamis, erosion, and accretion, severely impacting local environmental and socio-economic conditions. This research employed expert knowledge, weights, and scores from the analytical hierarchy process (AHP) to create vulnerability maps. The process includes the integration of various parameters such as geomorphology, Land use and land cover (LULC), significant wave height (SWH), rate of sea level rise (SLR), shoreline change (SLC), bathymetry, elevation, and coastal inundation. Based on the results, the very low, low, and moderate vulnerability regions comprise 17.26%, 30.77%, and 23.46%, respectively, whereas the high and very high vulnerability regions comprise 18.20% and 10.28%, respectively. The several locations tend to be high and very highdue to land-use patterns and coastal structures, but very few are contributed by geomorphological features. The results are validated by conducting a field survey in a few locations along the coast. Thus, this study establishes a framework for decision-makers to implement climate change adaptation and mitigation actions in coastal zones.

Exploration of the factors that influence total phosphorus in surface water and an evaluation of surface water vulnerability based on an advanced algorithm and traditional index method

Due to the continuous influence of human activities, phosphorus pollution in surface water has become a persistent problem that needs to be addressed since phosphorous entails certain risks and degrees of damage to ecosystems and humans. The presence and accumulation of total phosphorus (TP) concentrations in surface waters is the result of a combined effect of many natural and anthropogenic factors, and it is often difficult to intuitively identify the individual importance of each factor in regard to the pollution of the aquatic environment. Considering these issues, this study provides a new methodology to better understand the vulnerability of surface water to TP pollution and the factors that influence TP pollution through the application of two modeling approaches. This includes the boosted regression tree (BRT), an advanced machine learning method, and the traditional comprehensive index method (CIM). Different factors, such as natural variables (including slope, soil texture, normalized difference vegetation index (NDVI), precipitation, and drainage density) and point and nonpoint source anthropogenic factors were included to model the vulnerability of surface water to TP pollution. Two methods were used to produce a vulnerability map of surface water to TP pollution. Pearson correlation analysis was used to validate the two vulnerability assessment methods. The results showed that BRT was more strongly correlated than CIM. In addition, the importance ranking results showed that slope, precipitation, NDVI, decentralized livestock farming and soil texture had a greater influence on TP pollution. Industrial activities, scale livestock farming and population density, which are all contributing sources of pollution, were all relatively less important. The introduced methodology can be used to quickly identify the area most vulnerable to TP pollution and to develop problem specific adaptive policies and measures to reduce the damage from TP pollution.

A spatial indicator of environmental and climatic vulnerability in Rome

BackgroundUrban areas are disproportionately affected by multiple pressures from overbuilding, traffic, air pollution, and heat waves that often interact and are interconnected in producing health effects. A new synthetic tool to summarize environmental and climatic vulnerability has been introduced for the city of Rome, Italy, to provide the basis for environmental and health policies. MethodsFrom a literature overview and based on the availability of data, several macro-dimensions were identified on 1,461 grid cells with a width of 1 km2 in Rome: land use, roads and traffic-related exposure, green space data, soil sealing, air pollution (PM2.5, PM10, NO2, C6H6, SO2), urban heat island intensity. The Geographically Weighted Principal Component Analysis (GWPCA) method was performed to produce a composite spatial indicator to describe and interpret each spatial feature by integrating all environmental dimensions. The method of natural breaks was used to define the risk classes. A bivariate map of environmental and social vulnerability was described. ResultsThe first three components explained most of the variation in the data structure with an average of 78.2% of the total percentage of variance (PTV) explained by the GWPCA, with air pollution and soil sealing contributing most in the first component; green space in the second component; road and traffic density and SO2 in the third component. 56% of the population lives in areas with high or very high levels of environmental and climatic vulnerability, showing a periphery-centre trend, inverse to the deprivation index. ConclusionsA new environmental and climatic vulnerability indicator for the city of Rome was able to identify the areas and population at risk in the city, and can be integrated with other vulnerability dimensions, such as social deprivation, providing the basis for risk stratification of the population and for the design of policies to address environmental, climatic and social injustice.

Knowledge Domain and Development Trend of Urban Flood Vulnerability Research: A Bibliometric Analysis

Floods have become the most prevalent and catastrophic natural hazard that plagues cities worldwide. As an inherent characteristic of an urban system that reflects the degree of effect of flooding and the capacity to cope with it, urban flood vulnerability is of great significance when analyzing and managing flood disasters. To produce a better profile of the current status and the development trend of urban flood vulnerability research, this study conducted a bibliometric analysis using CiteSpace software based on 1134 articles sourced from the Web of Science Core Collection database. The results showed that the annual publication number exhibited an ascending trajectory, which was characterized by three stages: the sprouting stage, the stable development stage and the rapid development stage. The United States, China and England were the most productive countries. Among the top co-cited journals were Natural Hazards, Natural Hazards and Earth System Science, Global Environmental Change and the Journal of Hydrology. In addition, the focus of this research can be succinctly summarized as vulnerability assessments and mapping, the impact of climate change and urbanization on urban flood vulnerability and the integration of urban flood vulnerability with flood risk and urban resilience. This study presents a comprehensive analysis of the current status and development trends of research related to urban flood vulnerability, and it contributes an understanding of the key areas of focus in this field as well as insights into potential prospects for future investigation for researchers and practitioners.

Assessment of the vulnerability of hybrid coastal aquifers: application of multi-attribute decision-making and optimization models

ABSTRACT This study introduced an innovative hybrid framework using statistical-based, multi-attribute decision-making (MADM), and multi-objective optimization methods to assess the vulnerability of the Oman's Al-Khoud coastal aquifer without temporal variations.. Firstly, an extra parameter, bedrock topography (BT), was added to a commonly used index model, GALDIT and the parameter of aquifer type was removed from the model. Also, the random forest (RF) method was used to define the relative importance of parameters. Then, both frequency ratio (FR) and stepwise weight assessment ratio analysis (SWARA) methods were applied to modify the GALDIT rates. The GALDIT weights were optimized using the non-dominated sorting genetic algorithm-II (NSGA-II). Finally, the coastal aquifer vulnerability index (CAVI) model was obtained based on the hybrid FR-SWARA and NSGA-II models. The CAVI vulnerability map indicated high vulnerability in the Northern aquifer areas. Furthermore, the Spearman correlation coefficient between the CAVI and total dissolved solids (TDS) obtained 0.78.

Vulnerability hotspot mapping (VHM) of sewer pipes based on deterioration factors

ABSTRACT A decentralized, and sustainable sewer inspection plan at the city scale demands multiple inspection methods with different areas of impact. While sewer deterioration models offer great areas of impact, spatial mapping can assess the vulnerability of the system without prior knowledge of the pipe’s structural health condition. In this study, we assess and prioritize sewer deterioration factors such as pipe age, material, sewer type, node degree, flow velocity, and surface vegetation for vulnerability hotspot mapping of a sewer system in Dresden, Germany. The validation and sensitivity analyses revealed that flow velocity, pipe age, and surface vegetation are the most sensible factors to model, respectively. The linear model resulted in 76% efficiency and a mean squared error of 0.918 while it was improved with a random forest algorithm which points out vulnerability mapping potential as an early sewer inspection method at the city scale.

An Evaluation of Wildfire Vulnerability in the Wildland–Urban Interfaces of Central Portugal Using the Analytic Network Process

Vulnerability assessment is a vital component of wildfire management. This research focuses on the evaluation of wildfire vulnerability in the Central Region of Portugal, an area historically affected by catastrophic fire events. The overall methodology entailed applying an analytical hierarchy process (AHP) to the relevant spatial variables for evaluating vulnerability associated with exposure, sensitivity, and response capacity at landscape and the wildland–urban interface (WUI) scale. Of the selected criteria, the existence of fuel in direct contact with built-up areas, population density, and firefighters’ travel time were considered the most important criteria for inclusion in the vulnerability map. At landscape scale, 31% of the Central Region presents high and very high classes of vulnerability, while 22% of WUIs are classified as highly vulnerable to fire. Although the inland areas emerge as the most vulnerable, this approach enables scattered vulnerable hotspots to be identified in almost all of the Central Region. The results could be very helpful in terms of developing and enhancing local policies to mitigate human and material damage.

Data mining to aquifer vulnerability assessment

The vulnerability map of aquifer pollution is a part of the early warning system to prevent the deterioration of groundwater quality. The weighted index overlay methods are commonly used to map aquifer vulnerability, but they have a series of drawbacks, indicating the need to apply alternative methods that introduce the least number of a priori considerations in the parameters processing and allow a more accurate interpretation of the final results. The purpose of this study is to use data mining techniques for cluster analysis to evaluate the vulnerability of groundwater pollution in the Almondarez Vento Karst Basin in Havana Province, Cuba, and compare the results with those obtained using the RISK method, which is a weighted index overlay method to study karstic aquifers. The variables selected to apply this unsupervised classification technique were: aquifer lithology, topographic slope of the terrain, soil attenuation index to pollutants, fault density per km2 and presence of direct infiltration zones. The cluster analysis achieved greater spatial discrimination and definition of areas with different degrees of vulnerability, demonstrating its high resolution power.

Survey of extreme heat public health preparedness plans and response activities in the most populous jurisdictions in the United States

BackgroundIncreasingly frequent and intense extreme heat events (EHEs) are indicative of climate change impacts, and urban areas’ social and built environments increase their risk for health consequences. Heat action plans (HAPs) are a strategy to bolster municipal EHE preparedness. The objective of this research is to characterize municipal interventions to EHEs and compare U.S. jurisdictions with and without formal heat action plans.MethodsAn online survey was sent to 99 U.S. jurisdictions with populations > 200,000 between September 2021 and January 2022. Summary statistics were calculated to describe the proportion of total jurisdictions, as well as jurisdictions with and without HAPs and in different geographies that reported engagement in extreme heat preparedness and response activities.ResultsThirty-eight (38.4%) jurisdictions responded to the survey. Of those respondents, twenty-three (60.5%) reported the development of a HAP, of which 22 (95.7%) reported plans for opening cooling centers. All respondents reported conducting heat-related risk communications; however, communication approaches focused on passive, technology-dependent mechanisms. While 75.7% of jurisdictions reported having developed a definition for an EHE, less than two-thirds of responding jurisdictions reported any of the following activities: conducting heat-related surveillance (61.1%), implementing provisions for power outages (53.1%), increasing access to fans or air conditioners (48.4%), developing heat vulnerability maps (43.2%), or evaluating activities (34.2%). There were only two statistically significant (p ≥ .05) differences in the prevalence of heat-related activities between jurisdictions with and without a written HAP, possibly attributable to a relatively small sample size: surveillance and having a definition of extreme heat.ConclusionsJurisdictions can strengthen their extreme heat preparedness by expanding their consideration of at-risk populations to include communities of color, conducting formal evaluations of their responses, and by bridging the gap between the populations determined to be most at-risk and the channels of communication designed to reach them.

An analytical citizen relation management system (CZRM) for social vulnerability mapping and policy recommendation in Brazil

The emerging field of Citizen Relationship Management has shown promise to combine data analytics and intuitive digital platforms to help public managers interpret citizen-driven data and draw out appropriate policy. However, research dates back to only the last ten years, and does not commonly focus on families of extreme social vulnerability or how to bring solutions that increase access to public infrastructure. This research addresses the central challenge of setting strategic intervention plans for these families considering their multiple levels of vulnerability and the limited government resources available to address them. The paper builds on the framework for analytical CZRM to prototype and build a tool that analyzes data about income, nutrition, and housing for over 34,000 low-income families in Ceará, Brazil. Each family is assigned a vulnerability score, which is then used to filter public policy recommendations that address specific vulnerability profiles. The CZRM derives from over one year of interaction with government stakeholders who expressed their needs. Further software validation was completed with a series of test cases which ensured the tool was working and showing accurate results. The interactive mapping, ranking and filtering tool exemplifies an emerging decision support system to build a citizen portfolio and to set strategies to provide public services to different citizen groups.