The research, published in the journal Discover Geoscience by Mohd Shuaib and Vikas Pratap Singh of the Civil Engineering Department at the National Institute of Technology Uttarakhand, addresses a persistent gap in Himalayan hazard science. Most previous landslide assessments in the region have been carried out at broad state or district scales or confined to highway corridors, leaving fast-growing towns without the fine-grained information planners need. The new work goes further than most by integrating not just hazard, but also vulnerability and risk, and by validating its predictions against both statistical tests and geomechanical observations in the field.

The stakes are high. Landslides account for roughly nine percent of natural disasters worldwide, killing thousands of people each year and causing property damage estimated at around four billion US dollars annually. India bears about eight percent of global landslide fatalities, and of the country’s approximately 0.42 million square kilometres of landslide-prone terrain, some 0.31 million square kilometres lie within the Himalayas. Uttarakhand has endured a string of catastrophic events in recent decades, including the 2003 Varunavat Parvat landslide in Uttarkashi, the 2013 Kedarnath disaster, the 2021 Rishiganga tragedy in Chamoli and the 2025 Dharali landslides. In 2025 alone, monsoon-driven subsidence and landslides damaged houses built on steep slopes in Srinagar itself, many of them constructed without geotechnical assessment or proper drainage.

To build their hazard maps, the researchers compiled an inventory of 58 historical landslide events from the Geological Survey of India’s Bhukosh portal and analysed their spatial relationships with eleven conditioning factors: slope angle, aspect, elevation, profile curvature, distance to streams, distance to roads, distance to faults, lithology, the stream power index, rainfall and land use and land cover. Data came from a 30-metre digital elevation model obtained through the USGS Earth Explorer platform, GSI geological layers, rainfall grids from the Climatic Research Unit, and Sentinel-2 land cover data resampled to match the resolution of the other layers. The landslide inventory was split randomly, with 70 percent of the events used to train the models and the remaining 30 percent reserved for independent validation.

Two bivariate statistical methods formed the analytical core of the study. The frequency ratio model compares the proportion of landslides falling within each class of a conditioning factor against the proportion of the study area that class occupies, yielding values above one where landslides cluster more densely than chance would predict. The information value model applies a logarithmic transformation to the same relationship, stabilising variance and dampening the influence of extreme values in unevenly distributed classes. Because the information value approach is essentially a normalised version of the frequency ratio, running both models allowed the team to cross-check their hazard zonation and quantify the uncertainty that comes with any single statistical method.

The factor analysis revealed clear patterns in where and why slopes fail. Slopes steeper than 45 degrees recorded the highest frequency ratio of any slope class at 6.47, reflecting the greater gravitational stress and reduced shear strength on near-vertical terrain. Areas within 50 metres of roads showed a frequency ratio of 3.04, a signature of the slope cutting, excavation and vegetation removal that accompany mountain road construction. Zones within 250 metres of streams registered a ratio of 2.44, pointing to fluvial undercutting and toe erosion, while the 500-to-1,500-metre band around faults scored 2.80, a consequence of the fractured and weakened rock produced by tectonic activity. Perhaps most strikingly, hard igneous rocks yielded the highest lithological ratio at 7.51, which the authors attribute to intense fracturing and weathering that enable structurally controlled failures even in otherwise strong rock. When the factors were ranked by their overall predictive weight, lithology dominated with a prediction ratio of 4.554, followed by distance to faults, distance to streams, elevation and slope.

The two hazard maps told somewhat different stories about how much of the region is in danger. The frequency ratio model classified 18.88 percent of the area, or 13.14 square kilometres, as high hazard and 4.87 percent, or 3.39 square kilometres, as very high hazard, with the moderate class covering the largest share at 28.68 percent. The information value model drew a more alarming picture, placing 28.66 percent of the territory in the high hazard class and 16.02 percent, some 11.15 square kilometres, in the very high category, for a combined 44.68 percent of the landscape flagged as dangerous. The discrepancy suggests the information value model is more sensitive to the critical conditioning factors, a difference the authors treat as informative rather than contradictory, since both models converge on the same underlying controls: steep slopes, fractured rock, and proximity to streams, roads and faults.

Hazard alone does not determine disaster, and the vulnerability assessment brought human exposure into the equation. Using an index overlay approach, the researchers combined population density, distance to roads and land cover into a composite vulnerability index, weighting built-up areas, dense infrastructure and crowded settlements most heavily. The results showed that most of the study area, 58.37 percent, falls into the low vulnerability class, with high and very high vulnerability confined to 8.27 percent and 1.28 percent of the terrain respectively. These exposed hotspots cluster along road corridors, around settlements and across sparsely vegetated rangelands where slope modifications have destabilised the ground. Multiplying the vulnerability index by each hazard index produced the final risk maps, which identified 21.14 percent of the area as high risk and 3.71 percent as very high risk under the frequency ratio model, and 18.65 percent and 4.33 percent respectively under the information value model.

Statistical validation confirmed the models’ competence, but the most convincing evidence came from the field. Receiver operating characteristic analysis showed success rate areas under the curve of 0.827 for the frequency ratio model and 0.885 for the information value model, while independent prediction rates reached 0.734 and 0.783 respectively, figures consistent with comparable Himalayan studies. More importantly, the team visited landslide sites within the mapped high and very high risk zones, where large rock blocks had to be removed from roadways to restore traffic, and performed a kinematic analysis of a vulnerable slope using discontinuity orientation data from joint sets, bedding planes and the slope face. The stereonet analysis placed the slope inside the critical failure envelope and identified a wedge failure mode between two joint sets whose line of intersection plunges toward the slope face at an angle shallower than the slope dip. This structurally controlled failure mechanism, observed on the ground exactly where the models predicted the greatest danger, provides a rare and rigorous ground-truth check that most statistical hazard studies lack.

The authors are careful to frame the work as a preliminary but robust foundation rather than a final word. They suggest that future research should explore artificial intelligence and machine learning techniques to push predictive accuracy higher, and that detailed site-specific investigations using numerical slope stability modelling, combined with real-time monitoring, could underpin early warning systems for the region. For now, the maps offer town planners and disaster managers in Srinagar something they have never had before: a micro-scale, field-verified picture of where landslides are most likely, where people are most exposed, and where mitigation, land-use regulation and infrastructure investment should be concentrated first. As Himalayan towns continue to grow up steep and fractured slopes under intensifying monsoon rainfall, the framework demonstrated here, pairing GIS-based statistics with geomechanical field validation, offers a cost-effective template that other landslide-prone mountain communities around the world can adapt.

Subject of Research: Integrated landslide hazard, vulnerability and risk mapping in the Srinagar region of Uttarakhand, Lesser Himalayas

Article Title: Integrated landslide hazard, vulnerability, and risk mapping in Srinagar region of Uttarakhand, Lesser Himalayas

Article References: Integrated landslide hazard, vulnerability, and risk mapping in Srinagar region of Uttarakhand, Lesser Himalayas. (n.d.). https://doi.org/10.1007/s44288-026-00690-4

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00690-4

Keywords: landslide hazard, vulnerability mapping, risk assessment, frequency ratio, information value model, GIS, Uttarakhand, Lesser Himalaya, kinematic analysis, ROC-AUC validation, urbanisation, slope stability

The research team, led by Meriem Khelali of Eötvös Loránd University in Budapest together with colleagues from Algeria and Hungary, chose Gironde for good reason. Covering roughly 10,725 square kilometers, it is the largest department in metropolitan France and hosts the Landes de Gascogne Forest, one of the biggest artificial forest plantations in Europe. The landscape is dominated by maritime pine, Pinus pinaster, a resin-rich species whose dense canopies, accumulated litter, and flammable needles create continuous fuel beds that favor large, fast-moving crown fires. Forest covers about 46 percent of the department, and when this uniform fuel matrix is combined with sandy, fast-draining soils, recurrent summer droughts, and strong Atlantic winds, the conditions for catastrophic fire become almost textbook.

To capture the full complexity of the fire environment, the researchers assembled a geodatabase of twelve conditioning factors spanning four dimensions: vegetation and fuel characteristics, climate and weather, topography, and human influence. Sentinel-2 satellite imagery processed in Google Earth Engine yielded the Normalized Difference Vegetation Index and the Normalized Difference Moisture Index, which track fuel availability and vegetation water content respectively. Climate variables, including temperature, wind speed, and components of the Fire Weather Index, came from the ERA5 reanalysis datasets, while drought conditions were characterized using the Standardized Precipitation Index derived from TerraClimate. Land cover and fuel types were mapped from the CORINE Land Cover database, road networks and settlements were drawn from OpenStreetMap, and slope was generated from the 25-meter EU-DEM digital elevation model.

Each of the twelve layers was reclassified onto a common vulnerability scale from 1, representing very low vulnerability, to 7, representing very high vulnerability, and resampled to a uniform 100-meter grid. The team then applied the Analytical Hierarchy Process, a multi-criteria decision-making method that decomposes a complex problem into pairwise comparisons. In this case, a complete 12 by 12 reciprocal matrix was constructed to judge the relative importance of each factor, informed by published fire science and the specific dynamics of Gironde’s pine-dominated ecosystems. Forest fuel type, for example, was judged substantially more influential than temperature or the Fire Weather Index, reflecting the dominant role of fuel continuity in regional fire propagation. The matrix achieved a Consistency Ratio of 0.046, well below the accepted threshold of 0.10, indicating that the expert judgments were internally coherent rather than arbitrary.

The weighting scheme that emerged from the analysis is striking in what it reveals. Fuel-related variables, including forest fuel type, land cover, and the two vegetation indices, collectively accounted for roughly 60 percent of the total model weight. Human-related factors, such as distance to roads, settlement density, and proximity to agricultural land, contributed about 23 percent, underscoring the fact that most ignitions in Europe occur where people and forests meet. Climatic and weather variables contributed around 9 percent, and topography just 8 percent, largely because Gironde’s terrain is flat and its summer climate is spatially uniform. In other words, what makes Gironde dangerous is not primarily its weather but what grows there and who travels through it.

Combining the weighted layers through a weighted sum model produced the Forest Fire Vulnerability Index, which was classified into five categories. The results are sobering. The very high vulnerability class alone occupies 3,496 square kilometers, or 35.3 percent of the department, concentrated in the southern and western sectors where maritime pine stands, sandy soils, and Atlantic winds converge. The moderate class covers another 30.4 percent, while the high class adds 12.6 percent. Taken together, high and very high vulnerability zones encompass 47.9 percent of Gironde. At the opposite extreme, only 76 square kilometers, less than one percent of the department, fall into the very low category, restricted to wetlands, water bodies, and dense urban areas with little burnable fuel.

Crucially, the model was not left untested. The researchers validated it against 699 active fire detection points recorded by NASA’s Fire Information for Resource Management System in July 2022, the very month the megafires raged. When these ignition points were overlaid on the vulnerability map, 87 percent fell within the high and very high classes, and 64.1 percent occurred within the very high class, which covers just 35.3 percent of the study area. Receiver Operating Characteristic analysis yielded an area under the curve of 0.721, indicating good discriminatory performance, while a Success Rate Curve analysis produced an even stronger AUC of 0.86, confirming excellent agreement between predicted vulnerability and observed fire activity. The spatial pattern also aligns closely with the areas most affected during the 2022 fire season, lending further ecological credibility to the framework.

The choice of the Analytical Hierarchy Process over machine learning alternatives is itself a deliberate methodological statement. While statistical and artificial intelligence approaches have become popular in fire prediction, they often operate as black boxes whose internal logic is difficult to interrogate. The AHP framework, by contrast, makes the contribution of every factor explicit through pairwise comparisons, allowing forest managers to understand, justify, and adjust the weights as conditions change. This transparency is particularly valuable in data-scarce regions where long historical fire inventories may not exist, and it makes the resulting vulnerability map a practical decision-support tool rather than a purely academic exercise. The authors note that their AUC value sits comfortably within the 0.70 to 0.85 range typically reported for comparable knowledge-driven GIS studies.

The study does acknowledge limitations. The ERA5 climate data are coarser than the Sentinel-2 imagery, which may smooth out local weather variability, and vegetation moisture was represented with static satellite-derived indicators rather than continuously updated observations. The model also does not incorporate post-fire severity or burned-area dynamics. Future refinements could draw on near-real-time live fuel moisture estimates from fused Sentinel-1 and Sentinel-2 data, downscaled climate products, and machine learning algorithms such as Random Forest or XGBoost, potentially combined with future climate scenarios to support long-term risk assessment under a changing climate.

For now, the map stands as a timely warning and a practical guide. Since 2017, both the number of ignitions and the total burned area in Gironde have increased substantially, a trend driven by the combined effects of frequent droughts, heatwaves, and powerful Atlantic winds, all amplified by a warming climate. With nearly half the department classified as highly or very highly vulnerable, the stakes extend beyond the forest itself: these woodlands protect coastal dunes and wetlands, sustain biodiversity, support a major timber industry, and help stabilize the Atlantic coastline against erosion. A transparent, validated, high-resolution vulnerability map gives fire prevention services, land-use planners, and fuel managers a common evidence base for deciding where to thin stands, maintain firebreaks, position resources, and issue early warnings before the next Landiras-scale disaster arrives.

Subject of Research: Forest fire vulnerability assessment in Gironde, France using remote sensing and the Analytical Hierarchy Process

Article Title: An integrated remote sensing and AHP framework for forest fire vulnerability assessment in Gironde, France

Article References: Khelali, M., Bensekhria, A., Bouhata, R., Djabri, A. D., Hanini, W., Sanga, A., Ominde, J., & Jung, A. (2026). An integrated remote sensing and AHP framework for forest fire vulnerability assessment in Gironde, France. Natural Hazards, 122(18), Article 626. https://doi.org/10.1007/s11069-026-08402-4

Image Credits: AI Generated

DOI: 10.1007/s11069-026-08402-4

Keywords: forest fire, vulnerability mapping, remote sensing, AHP, GIS, Gironde, France, maritime pine, Sentinel-2, wildfire risk, Natural Hazards, megafires

The research, conducted by Alireza Shakiba, Neda Esfandiari, and Babak Mirbagheri of the Center for Remote Sensing and GIS Research at Shahid Beheshti University in Tehran, was funded by the Iran National Science Foundation. Rather than treating heatwaves as a single, uniform phenomenon, the team set out to answer two linked questions: how many distinct types of heatwave behavior exist across Iran, and how does vulnerability to those events change when different analytical perspectives are applied? The answer to the first question was five. The answer to the second was that vulnerability estimates can swing dramatically, with the share of the population living in highly vulnerable areas ranging from roughly 16 percent to as much as 47 percent depending on the scenario and age group considered.

To characterize heatwave behavior, the researchers jointly analyzed four key characteristics of heatwave events: intensity, duration, frequency, and timing. Each of these dimensions matters for different reasons. Intensity determines the physiological stress imposed on the human body and on crops; duration governs whether nighttime cooling can provide relief; frequency controls how often populations are exposed and how little recovery time they receive between events; and timing influences whether heatwaves strike during critical periods such as harvest seasons or peaks in outdoor labor. Analyzed in isolation, these characteristics can give contradictory signals, so the team used principal component analysis, a statistical technique that compresses many correlated variables into a smaller set of independent axes, to distill the essential structure of the data. Clustering techniques were then applied to those components, grouping regions of Iran that share similar heatwave signatures.

The result was the identification of five distinct heatwave regimes spanning the country. What makes this finding striking is that these regimes do not necessarily correspond to Iran’s conventional climatic zones. Average climatic conditions, such as long-term temperature and precipitation norms, have long been the default framework for describing Iran’s environmental diversity, from the humid Caspian coast to the arid central plateau. But heatwave behavior, the study shows, exhibits a spatial organization of its own. Two neighboring regions with similar average climates can experience fundamentally different heatwave patterns, one dominated by rare but intense events and another by frequent, persistent, and seasonally prolonged heat. For risk managers, this means that maps drawn from average climate data are poor guides to where extreme heat will actually bite hardest.

One of the most consequential findings concerns regions with only moderate heatwave intensity. Intuition suggests that where heatwaves are less severe, danger should be lower. The study found the opposite in several areas: moderate intensity combined with high event frequency, long seasonal persistence, and a substantial cumulative heatwave burden produced elevated vulnerability. In other words, it is the total accumulation of heat stress over a season, not the peak of any single event, that erodes the capacity of communities and ecosystems to cope. This cumulative burden perspective aligns with a growing body of international research showing that health impacts of heat depend heavily on event duration and frequency, and that repeated exposure without adequate recovery can be as damaging as isolated extreme spikes.

The second half of the study tackled vulnerability, a concept that in climate research typically combines three elements: hazard, the physical threat itself; exposure, who and what lies in the path of that threat; and sensitivity, the degree to which exposed populations or systems are susceptible to harm. Rather than committing to a single weighting of these components, the researchers built three alternative scenarios representing different orientations: a climate-oriented perspective, a human-oriented perspective, and a resource-oriented perspective. All three scenarios used the same underlying hazard, exposure, and sensitivity components, but they assigned different relative importance to each dimension. This scenario-based design, grounded in multi-criteria analysis methods, allowed the team to test how sensitive their vulnerability maps were to the analytical lens chosen.

The results were dramatic. Depending on the scenario and the age group examined, between approximately 16 and 47 percent of Iran’s population was found to live within highly vulnerable areas. Yet those areas occupy only about 4 to 9 percent of the national territory. This asymmetry, a large fraction of the population concentrated in a small fraction of the land, carries a clear practical message: targeted interventions in a limited number of places could protect a very large share of the people at risk. It also underscores how much the choice of assessment framework matters. A policymaker relying on a single scenario could underestimate the exposed population by a factor of nearly three, misallocating resources for early warning systems, cooling centers, or agricultural support.

Perhaps the most surprising spatial result is where persistent vulnerability concentrates. Areas that showed high vulnerability across multiple scenarios were primarily associated with agricultural landscapes, water-dependent environments, and resource-based livelihoods, rather than being confined to major urban centers. This finding challenges the common focus of heat research on cities, where the urban heat island effect amplifies temperatures in dense built-up areas. Iran’s cities have indeed been the subject of numerous local heat studies, including analyses of spatial inequality in heat exposure in Tehran and thermal comfort in Esfahan and Tabriz. But the new national assessment suggests that rural and resource-dependent communities, whose incomes, food security, and water supplies are directly tied to land and climate, may represent the country’s structurally vulnerable hotspots. Farmers facing heat-stressed crops, communities dependent on dwindling water resources, and populations whose adaptive capacity is limited by economic precarity all fit this profile.

The demographic dimension adds further nuance. By drawing on high-resolution population data that includes age and sex structure, the researchers could assess vulnerability separately for different age groups, an important refinement given that the elderly and the very young face elevated mortality risk during extreme heat. The range of 16 to 47 percent of the population in highly vulnerable areas reflects precisely this variation: different age groups occupy different places and carry different sensitivities, so the geography of risk shifts depending on whose vulnerability is being measured. A single national number would obscure these distinctions and the targeted health interventions they enable.

The broader significance of the study extends beyond Iran’s borders. Heatwaves are intensifying worldwide, and vulnerability mapping has become a central tool of climate adaptation planning. But this research demonstrates that vulnerability is not a fixed property of a place; it is highly sensitive to the analytical perspective adopted, and no single assessment framework can adequately represent it. By integrating heatwave regime analysis with scenario-based vulnerability assessment, the Iranian team has provided a template that other countries can adapt: first understand the distinct behavioral regimes of the hazard, then test how different value judgments about hazard, exposure, and sensitivity reshape the map of who is at risk. The areas that remain vulnerable under every scenario are the most robust targets for adaptation investment, and in Iran those places are the fields, wetlands, and resource-based communities that conventional heat maps have too often overlooked.

Subject of Research: Heatwave regime characterization and scenario-based vulnerability mapping in Iran

Article Title: Heatwave analysis in Iran: identification of regimes and scenario-based mapping of vulnerability

Article References: Shakiba, A., Esfandiari, N., & Mirbagheri, B. (2026). Heatwave analysis in Iran: identification of regimes and scenario-based mapping of vulnerability. Natural Hazards, 122(20), Article 651. https://doi.org/10.1007/s11069-026-08416-y

Image Credits: AI Generated

DOI: 10.1007/s11069-026-08416-y

Keywords: heatwaves, Iran, vulnerability mapping, climate change, principal component analysis, clustering, scenario analysis, population exposure, agriculture, heat stress, adaptation, Natural Hazards

“In the first 48 hours of a major disaster like a hurricane, responders show up from all over the country to help and are often operating in an information vacuum,” says Brandon McConnell, co-author of the study and an associate research professor in NC State’s Edward P. Fitts Department of Industrial and Systems Engineering. The model was built specifically to address that vacuum, giving operational planners a data-driven starting point for deciding where to send boats, helicopters, and ground teams first, rather than relying on intuition or waiting for distress calls to accumulate.

The framework, described in a paper published open access in the International Journal of Disaster Risk Reduction under the title “Anticipating Household Rescue Demand in Hurricanes Using Socio-Demographic Data and Machine Learning,” rests on a straightforward but powerful premise: not all households face the same probability of needing rescue when a hurricane strikes. Decades of disaster research have identified factors that make people more vulnerable during such events, including physical disabilities that limit mobility, fewer financial resources that make evacuation difficult, lack of access to a vehicle, advanced age, and housing located in areas prone to flooding. The research team translated these established vulnerability factors into a predictive computational model.

Technically, the model draws on two complementary streams of publicly available federal data. U.S. Census data provide a fine-grained picture of the socio-demographic composition of each census tract, allowing the model to identify communities where a large share of residents are likely to have trouble evacuating in advance of a storm. National Flood Insurance Program data supply information on which areas face the greatest risk of flooding, indicating where those less-mobile populations are most likely to become trapped by rising water. By combining these inputs, the model produces a ranked map of expected rescue demand across an affected region before responders arrive on the scene.

A distinctive feature of the project is the perspective of its first author. Patrick Leavitt, who began the work while a graduate student at NC State, is an active-duty Coast Guard officer, and his first-hand experience with emergency response operations shaped how the team approached the problem. Rather than designing an abstract academic exercise, the researchers built the framework around the practical constraints responders face: the need for fast computation, the availability of data in real time, and the reality that every area will eventually be checked, but the order in which areas are searched can mean the difference between life and death for people trapped in attics or on rooftops.

“Specifically, we developed a predictive modeling framework to identify census tracts where residents are most likely to require rescue,” says Ben Rachunok, corresponding author of the paper and an assistant professor in NC State’s Fitts Department. “Responders will ultimately look in every area, but which areas are most likely to have people who require rescuing? If we can predict that, we can prioritize search efforts in those areas.” That prioritization logic is what distinguishes the tool from existing hazard maps, which typically show where flooding will occur but not where the intersection of flooding and human vulnerability will generate the greatest demand for rescue.

To test the framework, the researchers conducted a case study focused on Hurricane Harvey, the Category 4 storm that struck Texas in 2017 and caused catastrophic flooding across the Houston metropolitan area. Harvey is a particularly valuable test case because it produced one of the largest urban rescue operations in American history, with thousands of water rescues carried out by the Coast Guard, first responders, and volunteer rescuers. The team fed regional Census data and National Flood Insurance Program data into their model to generate predictions of which areas would be most likely to have residents trapped by floodwaters, and then compared those predicted high-priority zones against publicly available records of where rescues actually took place.

The comparison showed that the model performed well in identifying the areas where rescue demand concentrated. “Our framework did pretty well – it should be useful for responders in practice,” says Rachunok. “It’s not perfect, but even this version would be helpful – and we can put in the work to make it even better.” The researchers emphasize that the model is an initial version, and that its accuracy should improve with better data and continued refinement. Importantly, the model is computationally lightweight: it does not take long to run, which means it could be executed while responders are still deploying, handing them a prioritized list of search areas as soon as they arrive in the disaster zone.

“Being able to achieve these results with this initial version suggests we’re optimistic about its utility if we fine-tune the tool – particularly in instances where we have access to better data,” says McConnell. The team also sees applications beyond the immediate response phase. According to Leavitt, the research could support emergency managers during the planning phases of disaster preparedness, helping them develop response plans and design exercises that reflect realistic patterns of rescue demand. Because the model relies on data that are already collected and publicly available, jurisdictions could use it in advance of hurricane season to identify their most vulnerable communities and pre-position resources accordingly.

The researchers say they are open to collaborating with emergency management and disaster response leaders to improve the model itself and to make the tool more user-friendly for practical field use. As climate change increases the intensity of Atlantic hurricanes and coastal populations continue to grow, the demand for rapid, well-targeted search and rescue operations is only expected to rise. A predictive framework that turns census data and flood risk information into an actionable rescue priority map offers a glimpse of how data science can be woven into the earliest, most chaotic hours of disaster response – when every minute of saved search time can translate directly into lives saved.

Subject of Research: A predictive mathematical model that prioritizes census tracts for post-hurricane search and rescue operations using socio-demographic and flood risk data.

Article Title: New tool helps responders ID highest-risk areas for post-hurricane rescue efforts

Article References: New tool helps responders ID highest-risk areas for post-hurricane rescue efforts. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: hurricane, search and rescue, emergency response, flood risk, machine learning, census data, National Flood Insurance Program, Hurricane Harvey, disaster preparedness, Coast Guard, North Carolina State University, vulnerability mapping