Search Results (147)

Surface deformation triggered by coal extraction activities, together with the consequent development of unstable slopes within rugged mountainous landscapes, constitutes a critical focus for geological risk assessment and mitigation strategies. Conventional SBAS-InSAR processing pipelines suffer from inadequate tropospheric phase mitigation in topographically complex environments, while existing clustering-based recognition approaches fail to incorporate sufficient geophysical constraints. To overcome these deficiencies, the present investigation introduces a refined methodology that synergizes InSAR measurements with an enhanced clustering scheme for the automated screening of potentially unstable slope units. First, a two-stage coupled atmospheric correction framework is constructed within the SBAS-InSAR processing chain, comprising spatially varying stratified atmosphere estimation based on geographically weighted robust regression (GWRR-M) and turbulent atmosphere compensation based on structure-guided deformation-preserving interpolation (SGDPI); both stages require no external meteorological data and effectively protect deformation signals from overcorrection. Second, a spatiotemporally constrained density peak clustering algorithm (STC-DPC) is developed, which constructs a multi-dimensional feature space integrating spatial location, deformation rate, temporal evolution characteristics, and topographic-geological background, and introduces a spatiotemporally constrained distance metric together with an Unstable Slope Index (USI) to achieve automatic identification and quantitative discrimination of unstable slopes. The proposed method was evaluated using 120 ascending-track Sentinel-1A SAR images acquired from 2019 to 2023 over the coal-mining mountainous areas of Mentougou and Fangshan districts in western Beijing, China. The results show that the improved atmospheric correction reduces the phase standard deviation of a representative interferogram from 1.6 rad to 0.6 rad, with an average reduction of 42.3% across all interferograms. A total of 187 unstable slopes were identified by the STC-DPC algorithm, mainly distributed in abandoned mining areas and steep terrain with gradients of 10–35°, with a mean deformation rate of −25.3 mm/a; field investigations at representative sites confirmed significant deformation evidence (e.g., tension cracks and bulging), providing qualitative support for the identification results. Compared with the identification results obtained without atmospheric correction (79 unstable slopes), the improved method improves the detectability of weak deformation signals in areas with strong topographic relief and diverse deformation patterns. This study provides a practical technical pathway for the early screening and monitoring of geological hazards in coal-mining mountainous areas and holds great significance for mine ecological restoration and regional disaster prevention and mitigation. Full article

Páramo ecosystems are among the most biodiverse and hydrologically critical landscapes on Earth, yet their long-term sustainability is increasingly threatened by climate-driven wildfires. Vegetation Cover Fires (VCFs) in these high-altitude environments degrade carbon stocks, disrupt freshwater regulation, and undermine biodiversity conservation goals central to the UN Sustainable Development Goals (SDGs 13, 15, and 6). Between 2001 and 2023, 128 fire events consumed approximately 815 ha in the Sumapaz locality (the world’s largest continuous páramo), representing 64.9% of all fires recorded across Bogotá’s 20 localities. Despite this disproportionate ecological and social impact, no spatially explicit, operational risk management framework has been available for the region, representing a critical sustainability governance gap. This study addresses that gap by proposing an integrated climate-adaptive risk assessment and management strategy based on (i) the Canadian Forest Fire Danger Rating System Fire Weather Index (FWI), derived from ERA5 reanalysis climate data; (ii) spatial and temporal hotspot analysis of MODIS FIRMS active fire detections; and (iii) IDEAM’s multi-component vulnerability and threat scoring protocol. Spatial data were processed using ArcGIS, and FWI sub-indices were computed for each month of the 2001–2023 period. The FWI averaged 0.78 (low danger) across the study period yet peaked at 13.7 in February 2010 (moderate-to-high danger), consistent with the year of highest recorded fire activity (19 events). High- and very high-risk areas (3.70% combined) coincide with slopes >25%, the presence of the invasive and pyrogenic Ulex europaeus, and proximity to populated and agricultural lands. This study concludes with a three-pillar risk management framework—risk knowledge, risk reduction, and disaster management—providing spatially targeted, operationally viable strategies for local and institutional actors that directly support the sustainable conservation of páramo ecosystem services (water supply, carbon sequestration, biodiversity). The framework is designed to be updatable on a monthly basis using freely available ERA5 data, enabling continuous adaptive governance of wildfire risk as a contribution to long-term territorial sustainability. Limitations regarding MODIS detection uncertainty, ERA5 spatial resolution in complex terrain, and the need for probabilistic modeling are explicitly acknowledged. Full article

Education on natural hazards is a non-structural component of Disaster Risk Reduction (DRR), but the evidence base spans curriculum studies, risk-perception research, educational interventions, geospatial approaches and analyses of education-system continuity. This systematic narrative review synthesizes 27 outcome-bearing studies published between 2013 and the partial year 2026. PRISMA 2020 was used as a reporting framework, while PRISMA-S informed a retrospective audit of the search documentation. A structured design-sensitive appraisal recorded evidence family, comparison or temporal structure, outcome directness, permitted inference and principal limitation. The studies were coded into six mutually exclusive primary axes: reviews and frameworks; curriculum and policy; knowledge and risk perception; educational interventions and active methodologies; GIS and geospatial technologies; and educational continuity and system resilience. The included literature suggests that locally situated problems, maps, simulations and inquiry can support knowledge, risk appraisal and preparedness intentions, although demonstrated effects on sustained performance or actual preparedness behavior remain limited. Cross-cutting gaps include weak longitudinal assessment, sparse attention to teacher professional development, limited treatment of indigenous or local knowledge, and no core study centered on learners with disabilities or special educational needs. The review defines critical territorial risk literacy as the capacity to interpret hazard, exposure, vulnerability, capacity and uncertainty through spatial evidence; evaluate their unequal territorial distribution; and translate that understanding into inclusive, proportionate preparedness and collective action. Full article

The active development of the foothill areas of the Almaty agglomeration (Southeastern Kazakhstan) requires reliable methods for landslide susceptibility assessment. This study aims to identify the spatial association between landslide occurrence and selected environmental and anthropogenic factors using the Frequency Ratio (FR) method and geographic information systems. The analysis is based on an inventory of 157 landslides, the SRTM digital elevation model, Landsat imagery, WorldClim climate data, geological maps, and OpenStreetMap data. Ten conditioning factors were analyzed: elevation, slope, aspect, precipitation, lithology, distance to faults, rivers and roads, the Normalized Difference Vegetation Index (NDVI), and land use. FR values were calculated for each factor and integrated to produce a landslide susceptibility map. Model performance was evaluated using Receiver Operating Characteristic (ROC) analysis and the Area Under the Curve (AUC). The resulting susceptibility map identifies areas with different levels of landslide susceptibility and provides a scientific basis for sustainable land-use planning, engineering-geological investigations, safer infrastructure development, natural hazard assessment, and disaster risk reduction in rapidly developing foothill regions. By supporting risk-informed land-use decisions and targeted mitigation measures, the study contributes to the long-term environmental safety and resilience of the Almaty agglomeration. Full article

Tailings dam safety is a cornerstone of green and sustainable mine development. The instability of such facilities directly threatens the safety of surrounding ecosystems, human lives, and property, contravening the Sustainable Development Goals regarding clean water and terrestrial ecosystem protection. Rainfall is the primary external factor triggering tailings dam instability, yet the failure mechanisms may differ fundamentally depending on the temporal distribution of rainfall. Against the background of the Poyang Lake Basin, this study integrates observed rainfall data from 2020 to 2025 with a survey of 101 active tailings dams (of which Class IV dams account for 43.6% and upstream-method construction accounts for 88.1%), on the basis of which a representative Class IV upstream-method tailings dam is selected as the prototype. A coupled unsaturated seepage and stability numerical model is constructed, and two sets of rainfall scenarios are designed: one varying the rainfall peak position coefficient t and the other varying the rainfall duration. The results indicate that infiltration induced by short-duration intense rainfall is confined to shallow strata. The earlier the rainfall peak, the more fully the deep infiltration develops and the greater the factor of safety reduction, with the critical variable governing stability degradation being the length of time available for deep redistribution after the rainfall peak. Long-duration sustained rainfall drives an overall rise of the phreatic line, and the seepage response is dominated by the average rainfall intensity. Extending the duration cannot compensate for the attenuation of the infiltration driving force caused by the reduced rainfall intensity, and the core factor determining disaster risk is whether the average rainfall intensity can exceed the drainage threshold of the deep low-permeability layers. Within the Poyang Lake Basin, concentrated heavy continuous rainfall during the Meiyu period lasting 20 to 40 days represents the decisive working condition governing tailings dam stability and should be prioritized in flood-season safety supervision. The rainfall-driven instability mechanisms revealed in this study provide a theoretical basis for establishing differentiated monitoring and early-warning thresholds and for the sustainable operation of tailings dams. Full article

Natural and man-made disasters at the urban level represent a major challenge to increasing resilience. According to the UN Agenda 2030, Disaster Risk Reduction (DRR) is a priority at the global level. Different actions can be planned and implemented before and after a disastrous event. The focus of this research is on actions to take before the event to increase preparedness. This paper focuses on exercises and training activities designed to reduce the gaps between actions performed before and after disastrous events. These actions include discussion-based and operation-based actions, classified by increasing levels of complexity and capability. Serious Games (SGs) represent a discussion-based action with the greatest level of complexity and capability. The objective of this paper is to investigate the potential contribution of SG to increasing preparedness for implementing evacuation procedures and thereby enhancing urban resilience. This implies the knowledge of urban mobility in evacuation conditions. This class of SGs combines Transport Risk Analysis (TRA), Transport System Models (TSMs), and emerging Information and Communication Technology (e-ICT) to reproduce, in a virtual environment, a transport system under evacuation conditions. In this way, it is possible to experiment with evacuation planning procedures in a virtual environment. The principal results of a pilot experiment are presented. The SG framework and its pilot implementation show a contribution to reducing orientation time, which may lead to increasing awareness and reducing exposure at the urban level. The paper is of interest to urban scientists and public and private decision-makers involved in disaster risk planning processes. Full article

Indonesia is one of the world’s most seismically active countries, experiencing frequent earthquakes that make assessing regional resilience essential for disaster risk reduction. This study dynamically evaluates socioeconomic resilience in 28 regencies/municipalities affected by destructive earthquakes between 2016 and 2022. Resilience was quantified using the Adjusted Mazziotta–Pareto Index (AMPI) at three periods: pre-event (T₀), during the event (T₁), and post-event (T₂). Index changes were interpreted as resistance (Δ₁ = T₁ − T₀), recovery (Δ₂ = T₂ − T₁), and adaptive capacity (Δ₃ = T₂ − T₀). Results show substantial regional differences in resilience trajectories: some areas experienced only minor declines during the earthquake, while others were heavily affected but recovered quickly. Cluster analysis revealed distinct typologies, including consistently high-resilience regions, rapid-recovery regions, and persistently vulnerable regions. These disparities are associated with variation in economic capacity, social vulnerability, labor market conditions, and access to health services. Overall, the findings highlight the value of a multidimensional, time-sensitive approach to measuring socioeconomic resilience. The study advances an AMPI-based temporal measurement framework and offers policy insights for development planning and disaster mitigation. Full article

Developing context-specific drought regionalization is crucial for targeted risk management, as drought evolves as a cascading hazard driven by complex land–atmosphere interactions rather than isolated climatic anomalies. However, conventional regionalization frameworks remain largely static and fail to capture the dynamic propagation from meteorological forcing to agricultural impacts. To address this limitation, we developed a framework that links continuous drought dynamics to discrete drought events, enabling identification of propagation patterns and their associated environmental mechanisms across the Loess Plateau, China. By integrating run theory, dimensionality reduction, clustering, and explainable machine learning, we identified three distinct drought propagation regimes: Propagation Blocked, Disaster Amplified, and Response Desensitized zones. At the regional scale, eco-hydrological factors, particularly vegetation productivity and soil moisture dynamics, showed the strongest attribution signals for differentiating drought propagation regimes. However, regime-specific environmental associations differed substantially: (i) propagation blockage was associated with terrain–vegetation interactions; (ii) disaster amplification was associated with low ecological productivity and declining soil moisture; and (iii) response desensitization was associated with intensive agricultural activities and relatively favorable soil moisture conditions, which may partly buffer vegetation responses to thermal and meteorological stress and create apparent resilience that may mask underlying hydrological vulnerability. SHAP analysis further indicated that topography and thermal conditions were strongly associated with broad-scale differentiation, while eco-hydrological conditions showed stronger associations with local regime-specific responses. Anthropogenic activities may also be associated with altered drought propagation pathways and potential risks of unsustainable water use. These findings highlight drought as a dynamic propagation process rather than a static hazard and provide a basis for targeted drought management strategies. Full article

Accurate delineation of current landslide extent is important for hazard assessment, sustainable watershed management, and disaster risk reduction in tectonically active mountainous regions. This study presents a pilot machine learning framework for pixel-level landslide classification in the Laonung (Laonong) Creek Watershed, southern Taiwan, using very high-resolution digital elevation model (DEM) derivatives and SPOT-6 multispectral imagery. Thirteen geomorphometric and spectral features, including slope, curvature, and six spectral indices derived from SPOT-6 bands, were extracted from 96 landslide-containing tiles within a pilot subregion of the watershed; no landslide-free tiles were included in model training or evaluation. Landslide annotations followed a geomorphic-unit delineation protocol in which optical imagery provided the primary evidence of current activity and DEM-derived hillshade supported boundary refinement. Three classifiers were evaluated using column-quartile spatially blocked four-fold cross-validation, with each fold comprising a geographically contiguous range of columns, to reduce spatial leakage: logistic regression (LR), random forest (RF), and XGBoost. All three models substantially outperformed the no-skill baseline for the resampled evaluation dataset (average precision, AP $=0.250$), achieving mean AP values of $0.854\pm 0.040$, $0.858\pm 0.033$, and $0.846\pm 0.035$ for LR, RF, and XGBoost, respectively. The convergence of linear and nonlinear model performance suggests that the dominant discriminatory signal is largely captured by relatively simple spectral and topographic predictors within this pilot dataset, rather than reflecting a general property of landslide classification. SHapley Additive exPlanations (SHAP) analysis across all four spatial folds identified SPOT-6 Band 3 (Red) as the dominant predictor in every fold, with NDVI a robust secondary predictor, consistent with the spectral characteristics of fresh bare-soil landslide surfaces and with the optical cues used in the annotation protocol. The results are interpreted in the context of the pilot dataset’s limited spatial extent, the resampled class distribution used for model evaluation, and unquantified label uncertainty. This study provides a transferable methodological baseline for future, larger-scale landslide classification analysis in the Laonung Creek Watershed and highlights the potential contribution of spatially explicit landslide mapping to sustainability-oriented disaster management. Full article

Landslides and collapses seriously threaten the safety of residents in Xiangtan County, Hunan Province, southern China. Local human engineering activities, dominated by slope cutting for housing construction, create steep artificial free faces, greatly weakening slope stability and becoming the key anthropogenic driver aggravating geological hazard risks, while targeted quantitative susceptibility assessment for residential slope units is still lacking for precise disaster prevention. To fill this gap and support proactive geohazard mitigation, this study selects Xiangtan County as the research object. A total of 166 landslide and collapse hazard points and 869 moderately and highly susceptible residential slope units were collected, and 12 conditioning factors, such as relative height difference, average slope and engineering rock mass group, were subsequently screened. Three hybrid intelligence models, namely PSO-BP, PSO-RF and PSO-SVM, were established to map residential slope unit susceptibility across the whole study area. The ROC-AUC and Kappa coefficient were adopted to quantify and compare the predictive performance of each model, and the Jenks natural breakpoint method combined with field survey data was used to classify all 7257 residential slope units into three susceptibility grades. The evaluation results show that the PSO-RF model performs best with an AUC of 0.913 and a Kappa coefficient of 0.64, representing strong predictive reliability. Under this optimal model, moderate-susceptibility units account for 12.94% (939 units) and high-susceptibility units account for 0.69% (50 units), both of which are concentrated in the southwest, southeast and partially northern zones of the county where intensive human slope-cutting activities prevail. This research provides a feasible technical framework for identifying high-risk residential slopes and delivers clear data support for local geohazard risk control and disaster reduction. In summary, the PSO-RF hybrid model is proven suitable for fine-scale susceptibility assessment of residential slopes in hilly regions with frequent small-sized slope failures. Full article

Disaster risk reduction, both natural and man-made, is a global priority and is in line with the goals of the 2030 Agenda, defined on the basis of what happened at the beginning of the millennium, starting with the disaster in human lives caused by Katrina in New Orleans. However, there is a scientific gap between the level of risk and the planned risk mitigation measures, with the related training and exercise actions implemented before real-life emergencies happen. In the context of Transport Risk Analysis (TRA), the size of exposure (E), in particular its reduction through evacuation, has been little studied, unlike the dimensions of occurrence (O) and vulnerability (V). This study aims to provide a framework of the scientific literature on the risk exposure component (graphical abstract) in urban transport systems, focusing on four macro areas: planning process; training and exercises; advanced Transport System Models (TSM); and use of emerging ICT (e-ICT). The study has been carried out using forward snowball techniques on a selected sample of the literature. The final set of references comprises 165 publications: 149 retrieved from Scopus (including records assessed at abstract level) and 16 retrieved via Google Scholar and forward-citation snowballing. The publications are distributed across the four macro-areas as follows: 68 concern the planning process, 24 training and exercises, 61 advanced Transport System Models, and 12 the use of e-ICT. The four macro-areas were examined both individually and in their mutual intersections, since several publications address more than one area simultaneously. Each work was classified under its predominant area for tabulation purposes. The analysis shows that the literature examined provides unequal coverage of the four broad areas. Whilst contributions relating to the planning process and modeling are relatively well-established, the integration of ICT with advanced transport system models in the context of transport risk analysis remains extremely limited. Approaches based on forecasts for outdoor evacuation are largely absent, and research into training and drill activities lags behind that on planning models. The framework developed in this work is useful to researchers, policy makers and public sector technicians involved in emergency management, as it offers a clear framework to guide future work in both research and operational action. Full article

Forest fire susceptibility mapping is an important component of disaster risk reduction, particularly in transitional climatic zones such as Anhui Province, China. Traditional approaches often rely on expert weighting (AHP) or linear assumptions, which may be insufficient for capturing the complex, non-linear interactions of fire drivers. This study develops a data-driven framework integrating 816 field-surveyed fuel plots with MODIS active fire data (2000–2025). We applied a systematic preprocessing pipeline, including 1–99% Winsorization to reduce the influence of sensor outliers, Non-Linear Gamma Curvature Normalization to represent asymmetrical risk responses, and a spatial buffer-based pseudo-absence protocol combined with semantic land-cover masking to reduce label ambiguity and macro-environmental bias. Benchmarking against seven machine learning algorithms on a naturally balanced dataset showed that the Random Forest (RF) model achieved the highest test-set performance among the evaluated models (Test AUC = 0.831). Youden’s J statistic was used to define a data-driven risk threshold. The results suggest that topographic configuration and forest stand density act as important baseline constraints and interact with physiological moisture stress indicators to influence fire susceptibility. The species-level risk analysis was broadly consistent with ecological expectations: coniferous forests showed the highest predicted high-risk proportion (79.10%), whereas soft broadleaves showed a substantially lower predicted high-risk proportion (4.29%). Spatial mapping indicated a “South-High, North-Low” pattern associated with topographic forcing and fuel continuity, which may provide useful information for regional fire management and the planning of green firebreaks. Full article

Lake Sarez in Tajikistan, formed by a major earthquake-induced landslide in 1911, is located in the highly seismically active Pamir–Hindu Kush region. The lake is impounded by the Usoi Dam, one of the largest natural landslide dams in the world, which has raised concerns regarding its long-term stability and associated downstream flood hazards. Due to its geomorphological setting and potential exposure to multiple triggering mechanisms, including seismic activity and landslides, Lake Sarez is widely considered a high-consequence hazard system. Although the dam has remained stable for over a century and is currently monitored using modern geodetic and satellite-based technologies, uncertainties remain regarding its internal structure and response to extreme external forcing. While existing early warning systems enhance preparedness in downstream communities, effective long-term risk reduction requires continued monitoring, improved hazard modeling, and strengthened regional cooperation. This review synthesizes existing studies on the geological setting, hazard potential, stability assessments, and disaster risk management strategies related to Lake Sarez. It highlights the importance of integrated multi-hazard analysis and precautionary risk governance in managing low-probability but high-impact natural dam failure scenarios. Full article

Cultural heritage assets in seismic metropolitan regions are increasingly exposed to interacting natural hazards, yet disaster risk assessments for historic buildings often remain limited to single-hazard interpretations. This study addresses this gap by developing a Geographic Information Systems (GIS)-based multi-hazard risk assessment for Ferhatpaşa Mosque, a sixteenth-century Ottoman heritage asset located in Çatalca, Istanbul. Eight spatial parameters were evaluated at the neighborhood scale: slope, elevation, aspect, precipitation, distance to fault lines, distance to hydrological features, land use, and soil capability. The model was developed through Weighted Overlay analysis and interdisciplinary expert-based weighting. Distance to fault lines and precipitation received the highest weights, each accounting for 17.22% of the model, followed by distance to hydrological features and soil capability, each weighted at 13.89%. The final risk map classified 71.99% of the study area as medium risk, 28% as low risk, and 0.02% as high risk. Ferhatpaşa Mosque was located within the medium-risk zone, approximately 29,600 m from active fault lines, 250 m from the nearest dry streambed, 800 m from the nearest stream, and 320 m from the nearest high-risk zone. These findings demonstrate that the mosque’s risk profile is shaped not by seismic proximity alone, but by the cumulative interaction of topography, precipitation, hydrology, soil conditions, and land-use characteristics. The proposed model provides a spatial decision-support framework for integrating cultural heritage conservation into sustainable disaster risk reduction and local risk mitigation planning. Full article

Earthquakes pose a serious threat to urban areas located in seismically active regions, particularly in developing countries where rapid urbanization and weak enforcement of building regulations increase the vulnerability of the built environment. Pakistan is highly exposed to seismic hazards due to its tectonic setting, and many residential buildings are constructed without adequate seismic design considerations. Therefore, assessing building vulnerability and understanding community perception of earthquake risk are essential for effective disaster risk reduction. This study investigates the relationship between the structural vulnerability of residential buildings and earthquake risk perception among residents in Peshawar, Pakistan. Two contrasting urban settlements were selected as case studies: WAPDA Town, representing a planned residential area, and Hashtnagri, representing an older unplanned settlement. A total of 400 buildings were surveyed through field investigations. Seismic vulnerability was assessed using the Rapid Visual Screening (RVS) method based on structural characteristics such as building age, number of floors, construction materials, structural irregularities, construction quality, and presence of seismic reinforcement features. A Physical Vulnerability Index (PVI) was developed to categorize buildings into different vulnerability levels. In addition, a questionnaire survey was conducted to evaluate earthquake risk perception among residents, and a risk perception index (RPI) was calculated. The results indicate that buildings located in the unplanned settlement exhibit significantly higher seismic vulnerability compared to those in the planned residential area due to poor construction practices, irregular structural configurations, and the absence of seismic-resistant features. Statistical analysis further reveals a positive relationship between physical vulnerability and earthquake risk perception, suggesting that residents living in structurally vulnerable environments tend to perceive higher earthquake risk. The findings highlight the importance of integrating structural vulnerability assessment with community awareness and preparedness programs. Implementation of seismic design provisions and improved enforcement of construction regulations, such as those specified in the Building Code of Pakistan 2022, can significantly reduce earthquake risk in rapidly growing urban areas. However, the present study did not directly evaluate the level of enforcement or compliance with the Building Code of Pakistan 2022 in either WAPDA Town or Hashtnagri. Therefore, the policy recommendations are intended as general implications derived from the observed vulnerability patterns. Full article