Search Results (138)

Data on wildfires (burned area ≥ 100 ha) in South Korea were compiled for 2000–2025 and analyzed together with the national geospatial inventories of hazardous fuel facilities to characterize wildfire-triggered Natech exposure and potential consequence distances. In total, 47 large wildfire events were identified, burning approximately 139,800 ha, with all events occurring during the late winter–spring window (February–May). The spatial overlays of wildfire footprints with facility locations identified 805 gasoline/diesel stations and 227 LPG filling stations located within wildfire-affected districts, corresponding to 14.1% of gas stations and 11.5% of LPG stations in the nationwide facility dataset. Facility exposure was geographically clustered, with the highest concentrations occurring in the eastern and southeastern wildfire hotspots. To quantify potential technological impact extents under wildfire escalation, ALOHA simulations were conducted for a wildfire-induced BLEVE/fireball scenario involving a 10,000 L mobile tank with representative fuels (propane for LPG, n-octane for gasoline, and n-dodecane for diesel). The modeled thermal radiation threat zone radii (10, 5, and 2 kW·m⁻²) were 228/322/502 m for propane, 250/353/550 m for n-octane, and 254/358/559 m for n-dodecane. Together, the event-based wildfire dataset, facility overlay results, and scenario-based impact distances provide an integrated, quantitative basis for assessing wildfire-triggered Natech conditions at the wildland–urban/industrial interface in South Korea. Full article

Flood risk maps play a critical role in land-use regulation, infrastructure planning, and community preparedness, which are key components of sustainable and climate-resilient urban development. Their production, however, remains costly, labor-intensive, and time-demanding as it relies on simulation-driven workflows that combine hydrodynamic modeling with expert interpretation and extensive validation. To address this issue from a sustainability perspective, we develop a novel, practical, and near-real-time large language model (LLM)-based framework to support property-level flood risk assessment. This framework, which synthesizes geospatial, hydrological, infrastructural, and historical flood information, extends existing research and explores novel risk estimation methods for use in planning practice. Using Brisbane, Australia, as a case study, we develop Flood-LLM, a multi-agent system that transforms multi-source urban datasets into structured textual representations, models diverse neighborhood conditions, and fine-tunes a reasoning model using expert-assessed risk classifications. The results show that Flood-LLM can reproduce official flood risk labels for creek, river, storm tide, and overland-flow hazards with reasonable accuracy, outperforming classical machine learning, deep learning, and untuned LLM baselines. Visual and quantitative analyses indicate that the framework demonstrates a qualitatively nuanced capability to capture salient spatial patterns present in the official maps, while generating a textual chain-of-thought providing a transparent audit trail for its labeling decisions. These findings suggest that such LLM-based approaches can produce potential complementary tools to expert-reviewed planning classifications and support more sustainable, adaptive flood risk management by enabling timely map production and updates that facilitate informed decision-making in rapidly changing environmental conditions. Full article

The global expansion of continuous GNSS networks has generated large-scale spatiotemporal datasets whose analysis requires robust mathematical and statistical tools. This study introduces a geospatial, multivariate statistical framework for classifying 21,548 GNSS stations from the University of Nevada repository. The methodology integrates harmonic regression, stochastic noise modeling, quality assessment, and slope estimation into a unified feature space suitable for high-dimensional analysis. Using unsupervised learning clustering computed with our custom-developed code, based entirely on free and open-source software, we identify homogeneous station groups that reflect dominant signal properties—periodicity, noise structure, data quality, and long-term velocity—together with their spatial context. The resulting clusters exhibit strong mathematical coherence and reveal continental-scale patterns driven by seasonal forcing, tectonic regime, climatic variability, and monument stability. By grouping stations with similar statistical behavior, the proposed framework improves reference-site selection, enhances deformation-field interpretation, and supports the detection of anomalous or hazard-related behavior. Overall, this approach provides a scalable, data-driven mathematical tool for analyzing complex spatiotemporal signals and contributes to more reliable deformation modeling and environmental risk assessment. Full article

Urban hydrological risks are endangering vulnerable populations, particularly in densely populated metropolitan areas undergoing rapid land use transformation. This study uses geospatial analysis to identify zones in the Athens metropolitan area that are prone to surface water accumulation and stream flow development during extreme rainfall events. Two spatial indices were developed by integrating digital elevation models, flow accumulation, slope, aspect, the topographic wetness index, and classified road network data: a Surface Water Accumulation Index and a Stream flow Pathway Index. Roads were categorized based on their orientation relative to the direction of the slope, which allowed for an assessment of their influence on hydrological flow. Both indices were classified into five risk levels representing gradients of hydrological vulnerability. The spatial patterns revealed by this analysis show strong correlations with flood-prone areas and natural drainage systems. These insights are essential for guiding urban planning efforts aimed at reducing hydrological hazards, particularly for at-risk groups such as the homeless. This approach offers a valuable tool for promoting sustainable, socially inclusive landscape management. Full article

This study presents the first integrated quantification of mud diapirism susceptibility in the Membrillal sector of Cartagena de Indias, Colombia, through a multidisciplinary approach combining geospatial, geotechnical, hydrogeochemical, and socio-structural analyses. Using GIS-based multicriteria modeling, household surveys (n = 240), and temporal satellite imagery from 2013 to 2024, the research identifies spatial and temporal dynamics of active mud volcano reactivation. Field sampling of vent waters and gases followed ISO/IEC 17025 and APHA–AWWA–WEF standards, revealing high-salinity fluids (TDS = 13,220 mg/L; EC = 20.4 mS/cm; pH = 8.0) with elevated chloride (6996 mg/L) and low sulfate (1.67 mg/L) under reducing conditions, though a significant charge-balance discrepancy (Na⁺ = 8 mg/L) indicates either sample dilution during the collection or presence of unmeasured cationic species, and low free-gas flux constrained by high-density brine sealing. Principal component analysis of 240 georeferenced dwelling surveys yielded dimension-specific reliability (α = 0.68–0.76) and strong spatial correlation (Spearman ρ = 0.61–0.87) between vent proximity and structural damage—46.9% of dwellings exhibited visible cracking, with 27.2% severe (width > 1.5 mm). Satellite differencing documented 233% increase in active vents (3→10) and 35% vegetation reduction correlated with informal settlement expansion into moderate-to-high susceptibility zones. Weighted overlay GIS modeling (validated Kappa = 0.82) classified four hazard classes; high-susceptibility zones (18% of the study area) encompassed all ten active vents. Findings underscore anthropogenic pressurization drivers—primarily surface loading from settlement densification—and the need for continuous InSAR deformation monitoring, piezometric observation, complete hydrogeochemical characterization (including alkalinity and unmeasured cations), and establishing early-warning thresholds for community risk mitigation. Full article

Climate change has among its effects the increasing frequency and intensity of natural disasters, such as landslides, floods, erosion and fires, with clear implications on both natural and anthropic hazards and risks. These natural phenomena pose a growing threat to archaeological heritage through increased rates of soil erosion, flooding, and landslides. This study presents a multidisciplinary approach to assess the erosion risk affecting medieval rural settlements in the Basilicata Region of Southern Italy. This area is characterised by high-impact natural phenomena that have influenced settlement patterns in the past. The focus is on rural settlements that arose during the Middle Ages, some of which were abandoned as early as the late Middle Ages. This study has the dual objective of analysing the natural causes that may have led to the abandonment of many sites in ancient times and producing a predictive multi-risk map of the possible loss of cultural heritage sites. By integrating archaeological data, remote sensing, historical sources, and geospatial modelling, a multi-risk map was developed to identify areas at the highest risk. The results demonstrate the urgent need for proactive conservation strategies in the face of ongoing climatic change. Full article

The paper addresses the limitations of fragmented and delayed hydrological information systems in supporting timely disaster risk mitigation. The paper introduces the RoWaterAPI, a framework that integrates near real-time hydrological measurements with geospatial analytics to improve awareness during flood-related events. The methodology utilizes open-source technologies, including Django, Kafka, and PostGIS, to support scalable data ingestion and hazard mapping. Initial baseline evaluation under a simulated bursty workload indicates an end-to-end latency of ≈1–3 s and a peak throughput of ≈6000–8500 messages/s. This performance supports real-time alerts for data variations, bridging advanced geoprocessing with user-centered design for public and institutional stakeholders. Ultimately, RoWaterAPI provides a transferable implementation model that can be adapted to any national context facing similar constraints in data fragmentation and operational accessibility. Full article

The intensification of wildfires in Portugal has highlighted the urgent need for technical tools capable of supporting more effective risk mitigation decisions. In particular, the lack of explicit criteria for prioritizing the implementation of wildfire mitigation programs has contributed to reactive and fragmented interventions that are often misaligned with actual levels of hazard and exposure. This study proposes a spatially explicit methodology for classifying and ranking villages in wildfire-prone territories under two operational programs: Protection of People, Assets and Fuel Management. The framework was applied to eight municipalities across three Portuguese regions with high wildfire recurrence, using a multi-criteria decision analysis approach (AHP) integrated with geospatial data. Five physical and social variables were considered: critical area, vegetation cover, fire history, slope, and population density. Expert-derived weights were incorporated into two program-specific models. Implementation priority levels were generated using standard deviation classification at both municipal and regional scales. The results reveal marked territorial contrasts and strong intra-municipal variability, particularly in heterogeneous landscapes. A high degree of convergence between the two programs was observed (79–90%), although 10–21% of villages shifted between priority classes. The dual-scale analysis shows how a small number of high-hazard municipalities disproportionately shape the overall priority structure. The proposed framework supports more transparent, consistent, and risk-informed prioritization, strengthening territorial wildfire governance and complementing national mitigation programs such as “Safe Villages” and “Safe People” and “Condominium of Villages”. Full article

Detecting rip currents from satellite imagery offers valuable information for the characterization and assessment of this coastal hazard. While recent advances in deep learning have enabled automatic detection from close-view beach images, the broader geospatial context available in far-view satellite imagery has not yet been fully exploited. The main challenge lies in identifying rips as small objects within large and visually complex scenes that include both beach and non-beach areas. To address this, we proposed a detection pipeline which partitions high-resolution satellite images into small regions on which rip currents are detected using a deep learning object detection model that merges the results. The merged results are processed by applying a deep learning classification model to filter out non-beach scenes, followed by applying the detection model on augmented images to remove spurious detection. The proposed pipeline achieved an overall accuracy of 98.4%, a recall of 0.890, a precision of 0.633, and an F2 score of 0.823 on the testing dataset, demonstrating its effectiveness in locating rip currents within complex coastal scenes and its potential applicability to other regions. In addition, a new rip image dataset containing far-view satellite imagery was constructed. With the new dataset, we demonstrated a potential application of the proposed method in characterizing rip occurrences and found that rip currents tended to occur at open beaches under moderate-energy, onshore-directed waves conditions. Overall, the proposed pipeline, unlike existing near-real-time rip current monitoring systems, provides a high-accuracy offline analysis tool for rip current assessment using satellite imagery. Along with the new dataset introduced in this work, it can represent a valuable step towards expanding available resources for improving automated detection methods and rip current research. Full article

This observation-based and explainable approach demonstrates the applicability of multi-source remote sensing for flood assessment in data-scarce regions, offering a robust scientific basis for flood management and spatial planning in monsoon-affected areas. Floods are among the most frequent and devastating natural hazards, particularly in developing countries such as Myanmar, where monsoon-driven rainfall and inadequate flood-control infrastructure exacerbate disaster impacts. This study presents a satellite-driven and interpretable framework for high-resolution flood susceptibility and risk assessment by integrating multi-source remote sensing and geospatial data with ensemble machine-learning models—Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM)—implemented on the Google Earth Engine (GEE) platform. Eleven satellite- and GIS-derived predictors were used, including the Digital Elevation Model (DEM), slope, curvature, precipitation frequency, the Normalized Difference Vegetation Index (NDVI), land-use type, and distance to rivers, to develop flood susceptibility models. The Jenks natural breaks method was applied to classify flood susceptibility into five categories across Myanmar. Both models achieved excellent predictive performance, with area under the receiver operating characteristic curve (AUC) values of 0.943 for XGBoost and 0.936 for LightGBM, effectively distinguishing flood-prone from non-prone areas. XGBoost estimated that 26.1% of Myanmar’s territory falls within medium- to high-susceptibility zones, while LightGBM yielded a similar estimate of 25.3%. High-susceptibility regions were concentrated in the Ayeyarwady Delta, Rakhine coastal plains, and the Yangon region. SHapley Additive exPlanations (SHAP) analysis identified precipitation frequency, NDVI, and DEM as dominant factors, highlighting the ability of satellite-observed environmental indicators to capture flood-relevant surface processes. To incorporate exposure, population density and nighttime-light intensity were integrated with the susceptibility results to construct a natural–social flood risk framework. This observation-based and explainable approach demonstrates the applicability of multi-source remote sensing for flood assessment in data-scarce regions, offering a robust scientific basis for flood management and spatial planning in monsoon-affected areas. Full article

Background/Objectives: Penile squamous cell carcinoma (PSCC) is a rare malignancy with poor prognosis in advanced and recurrent disease, and therapeutic options remain limited. Increasing evidence suggests that the tumor immune microenvironment (TIME), including immune cell composition and spatial organization, plays a critical role in tumor progression and survival outcomes. This study aimed to characterize immune cell density and geospatial clustering patterns within the TIME of PSCC and to evaluate their associations with clinical outcomes. Methods: Multiplex immunofluorescence (mIF) was performed on tumor samples from 57 patients with PSCC using a panel of immune markers to identify lymphoid and myeloid cell populations. Immune cell densities were quantified within tumoral and stromal compartments. Spatial relationships among immune cells and between immune cells and tumor cells were analyzed using point pattern analysis. Survival outcomes, including overall survival (OS), recurrence-free survival (RFS), and cancer-specific survival (CSS), were assessed using Kaplan–Meier methods and Cox proportional hazards models, with analyses stratified by nodal and human papillomavirus (HPV) status. Results: Higher intratumoral and stromal densities of pro-immunogenic M1 macrophages were associated with improved OS. Increased densities of CD3⁺CD4⁺ helper T cells in both compartments were also associated with favorable survival outcomes. In contrast, close clustering of pro-tumorigenic M2 macrophages with tumor cells and with one another was associated with worse OS, RFS, and CSS. Bivariate clustering of helper T cells with tumor cells was associated with improved OS, including among patients with node-positive disease. Survival outcomes did not differ significantly by HPV status in patients with high helper T cell clustering. Conclusions: Immune cell density and spatial organization within the TIME are associated with survival outcomes in PSCC. Favorable patterns involving helper T cells and M1 macrophages correlate with improved survival, whereas clustering of M2 macrophages is associated with poorer outcomes, supporting the relevance of spatial immune profiling in this disease. Full article

Monitoring the stability of hydraulic structures such as dams and reservoirs in seismically active regions is essential for ensuring their safety and operational reliability. This study presents a comprehensive geospatial approach combining lineament analysis and geodynamic zoning to assess the structural stability of the Voroshilov and Priyut reservoirs located in the Almaty region, Kazakhstan. A regional lineament map was generated using ASTER GDEM data, while ALOS PALSAR data were used for detailed local analysis. Lineaments were extracted and analyzed through automated processing in PCI Geomatica. Lineament density maps and azimuthal rose diagrams were constructed to identify zones of tectonic weakness and assess regional structural patterns. Integration of lineament density, GPS velocity fields, InSAR deformation data, and probabilistic seismic hazard maps enabled the development of a detailed geodynamic zoning model. Results show that the studied sites are located within zones of low local geodynamic activity, with lineament densities of 0.8–1.2 km/km², significantly lower than regional averages of 3–4 km/km². GPS velocities in the area do not exceed 4 mm/year, and InSAR analysis indicates minimal surface deformation (<5 mm/year). Despite this apparent local stability, the 2024 Voroshilov Dam failure highlights the cumulative effect of regional seismic stresses (PGA up to 0.9 g) and localized filtration along fracture zones as critical risk factors. The proposed geodynamic zoning correctly identified the site as structurally stable under normal conditions but indicates that even low-activity zones are vulnerable under cumulative seismic loading. This demonstrates that an integrated approach combining remote sensing, geodetic, and seismic data can provide quantitative assessments for dam safety, predict potential high-risk zones, and support preventive monitoring in tectonically active regions. Full article

Urban flooding has become a critical environmental challenge under global climate change and rapid urbanization. This study develops a multi-indicator hazard mechanism framework for flood hazard assessment in Rizhao, a coastal city in China, by integrating three fundamental hydrological processes: runoff generation, flow convergence, and drainage. Based on geospatial data—including DEM, road networks, land cover, and soil characteristics—six key indicators were evaluated using the TOPSIS method: runoff curve number, impervious surface percentage, topographic wetness index, time of concentration, pipeline density, and distance to rivers. The results show that extreme-hazard zones, covering 6.41% of the central urban area, are primarily clustered in northern sectors, where flood susceptibility is driven by the synergistic effects of high imperviousness, short concentration time, and inadequate drainage infrastructure. Independent validation using historical flood records confirmed the model’s reliability, with 83.72% of documented waterlogging points located in predicted high-hazard zones and an AUC value of 0.737 indicating good discriminatory performance. Based on spatial hazard patterns and causal mechanisms, an integrated mitigation strategy system of “source reduction, process regulation, and terminal enhancement” is proposed. This strategy provides practical guidance for pipeline rehabilitation and sponge city implementation in Rizhao’s resilience planning, while the developed hazard mechanism framework of “runoff–convergence–drainage” provides a transferable methodology for flood hazard assessment in large-scale urban environments. Full article

Rapid urbanization in developing megacities has exacerbated hydrological imbalances, positioning urban flooding as a major environmental and socio-economic challenge of the twenty-first century. This study investigates the spatial relationship between building density, topography, and flood susceptibility in Jakarta, Indonesia—one of the most flood-prone urban regions globally. Employing geospatial analysis and spatial autocorrelation techniques, the research assesses how variations in land-use concentration and elevation influence the spatial clustering of flood vulnerability. The analytical framework integrates multiple spatial datasets, including Digital Elevation Models (DEMs), building footprint densities, and flood hazard maps, within a Geographic Information System (GIS) environment. Spatial statistical measures, specifically Moran’s I and Local Indicators of Spatial Association (LISA), are utilized to quantify and visualize patterns of flood susceptibility. The findings reveal that zones characterized by high building density and low elevation form statistically significant clusters of heightened flood risk, particularly within the southern and eastern subdistricts of Jakarta. The study concludes that incorporating spatially explicit and statistically rigorous methodologies enhances the accuracy of flood-risk assessments and supports evidence-based strategies for sustainable urban development and resilience planning. Full article

This study investigates temporal and spatial variation in torrential flood hazards and sediment dynamics in two ungauged watersheds in southeastern Serbia from 1991 to 2023. By integrating classical hydrological models with modern geospatial and photogrammetric techniques, watershed responses to environmental and anthropogenic changes are quantified. Torrential flood potential was estimated and peak discharges were calculated using both the rational and SCS-Unit hydrograph methods, while sediment transport was assessed through Gavrilović’s erosion potential model and a modified Poljakov model. A key innovation is the use of UAV-based and close-range photogrammetry for 3D grain-size analysis, marking the first such application in Serbia. The mean torrential flood potential decreased by 4.4% in the Petrova Watershed and 4.2% in the Rasnička Watershed. Specific peak discharges for a 100-year return period declined from 1.62 to 1.07 m³·s⁻¹·km⁻² in Petrova and from 1.60 to 1.34 m³·s⁻¹·km⁻² in Rasnička. Sediment transport during a 1% probability flood was reduced from 4.97 to 2.53 m³·s⁻¹ in Petrova and from 13.87 to 9.48 m³·s⁻¹ in Rasnička. Grain-size analyses revealed immobile coarse bedload in the Petrova and active sediment transport in the Rasnička River, where D50 and D90 decreased between 2023 and 2024. The findings highlight the effectiveness of a synergistic methodological approach for analyzing complex watershed processes in data-scarce regions. The study provides a replicable model for flood hazard assessment and erosion control planning in similar mountainous environments undergoing socio-environmental transitions. Full article