Search Results (1,803)

Critical infrastructure restoration (CIR) is a disaster-management and sustainability challenge because prolonged disruption of energy, water, transport, communications, healthcare, and public-administration services can amplify social, economic, and environmental losses. This PRISMA 2020-reported systematic review synthesizes post-2016 scientific literature and official policy, legal, standards, and technical documents on CIR and AI decision support. The review identified 55 records, removed 1 duplicate, excluded 1 ineligible record, and retained 53 core sources for qualitative synthesis, including 31 scholarly publications and 22 official documents. Manual screening was used; no automated screening or AI-assisted exclusion tools were applied. The results are organized around four research questions covering regulatory frameworks, recovery practices, supporting systems, and AI model families. The synthesis shows that CIR is shaped by layered governance through NIS2, the CER Directive, the AI Act, and national measures; by operational recovery practices such as continuity planning, cyber crisis coordination, interdependency mapping, and model-supported restoration; by digital platforms including SCADA/ICS, IoT sensing, GIS/common operating pictures, decision-support systems, simulation environments, and digital twins; and by AI methods ranging from classical machine learning and computer vision to reinforcement learning and generative assistants. However, evidence maturity remains uneven, with many AI applications still simulation-based, sector-specific, or weakly validated in real restoration settings. The review contributes an integrated CIR-oriented framework showing that AI creates practical value when embedded in interoperable, human-supervised, regulation-aware, and empirically validated restoration architectures that support sustainable service continuity rather than isolated automation. Full article

Due to its unique hydrogeological conditions, the Southwest Karst Area (SKA) in China experiences droughts far more frequently than non-karst regions. Exploring the distribution patterns and driving factors of different drought types is crucial for enhancing the region’s disaster prevention and mitigation capabilities and effectively addressing climate change risks. Using meteorological data from 1979 to 2023 in the SKA—including precipitation, temperature, humidity, potential evapotranspiration, and soil moisture—this study employed Copula theory to construct the Standardized Temperature Deficit Index (SDTI), the Standardized Humidity–Temperature Deficit Index (SDHTI), and the Standardized Atmosphere–Soil Index (SASI). Based on these indices and run theory, this study revealed the spatial distribution characteristics of different drought types (general, atmospheric, and composite) in terms of intensity, frequency, severity, and duration. Furthermore, the Mann–Kendall test and random forest analysis were applied to investigate drought trends and primary driving factors. The results indicate that droughts in the SKA exhibit significant regional characteristics and an overall worsening trend. Among them, droughts in karst-developed regions are generally more severe, though their manifestations vary across areas: compound droughts are particularly severe on the western Sichuan Plateau but relatively mild in Guangxi. In contrast, atmospheric droughts are more pronounced in Guangxi. Regarding trends, the rate of drought intensification was relatively moderate in Guangxi and the western Sichuan Plateau but more pronounced in other regions, with the maximum increase reaching 0.59. However, this upward trend is not statistically significant. Additionally, drought in karst areas was characterized by high frequency and intensity but shorter duration and lower severity, whereas the opposite was true in non-karst areas. Random forest analysis revealed that temperature is the primary driver of SDTI (2.60), while relative humidity and temperature have significant impacts on SDHTI (3.21 and 2.42, respectively). Soil moisture and temperature contribute most significantly to SASI (2.08 and 1.48, respectively). These findings provide important insights to guide the rational allocation of regional water resources and optimize agricultural management strategies. Full article

Under the combined impacts of climate change and urbanization, flood disasters have exhibited increasing non-stationarity, low-frequency but high-impact characteristics, and enhanced spatial dependence. Traditional indemnity-based flood insurance has certain limitations in claim efficiency and loss assessment. In contrast, index-based flood insurance, characterized by objective triggering mechanisms, rapid claim settlement, and low operational costs, has gradually become an important tool for flood catastrophe risk management. Based on a literature review approach, this study systematically reviews the index system, pricing mechanisms, and basis risk of index-based flood insurance, and provides a comprehensive analysis from the perspectives of index construction, threshold determination, and payout design. The results indicate that index systems have evolved from single hazard indicators to coupled indices integrating hazard characteristics and loss information, and multiple pricing approaches have been developed, including fixed, linear, piecewise payout, and probabilistic payout schemes (payouts determined by loss probabilities rather than fixed thresholds). Among the reviewed approaches, inundation-area-based indices generally show stronger consistency with actual losses at urban scales, whereas precipitation-based indices are more suitable for large-scale regional applications due to their rapid triggering capability. However, basis risk remains a critical issue, mainly arising from index errors, spatial scale mismatches, and inappropriate threshold settings. Therefore, to address the identified limitations of basis risk, threshold uncertainty, and spatial mismatches, future research should focus on multi-dimensional risk indices, dynamic threshold setting, and optimized spatial risk zoning, as well as the integration of remote sensing and machine learning methods to improve the consistency between indices and actual losses. The findings provide practical guidance for insurers in product design, for policymakers in regional flood risk financing, and for disaster managers in improving climate adaptation strategies. Full article

Flash droughts, characterized by rapid onset and intensification, are occurring more frequently under global warming. Accurately identifying the frequency and hazard severity of flash droughts remains challenging, as they are influenced by multiple hydroclimatic drivers, including precipitation deficits, temperature increases, and soil moisture depletion. We developed a daily-scale Flash Drought Hazard Index (FDHI) by integrating the interactive effects of multiple driving factors, aiming to assess the spatiotemporal patterns of flash drought hazard in the Black Soil Region of Northeast China during the period 2000–2020. The FDHI employs the daily Standardized Precipitation Evapotranspiration Index, Standardized Soil Moisture Index, Standardized Soil Temperature Index, and Standardized Runoff Index to characterize short-term anomalies in multiple hydrometeorological variables. Results showed that flash droughts occurred most frequently in the southern part of the Black Soil Region of Northeast China, particularly in the Songnen Plain and the Liaohe Plain, with annual frequencies of 5.98 and 5.80 events, respectively. Flash drought severity in the Liaohe Plain exhibited a significant increasing trend during the past decade. Moreover, the dominant driving factors varied substantially among regions. Flash droughts in the Liaohe Plain were mainly associated with precipitation deficits and enhanced evapotranspiration, whereas soil moisture depletion and temperature anomalies played a more important role in the Songnen Plain. These results reveal pronounced regional heterogeneity in flash drought mechanisms across the Black Soil Region of Northeast China and demonstrate the effectiveness of the proposed FDHI for daily-scale agricultural flash drought monitoring. The study provides scientific support for agricultural drought risk management and disaster mitigation under climate change. Full article

Surface rock movement can lead to geological or environmental problems such as surface subsidence, ground fissure development, and deformation of engineering structures, and its evolution process exhibits significant spatiotemporal heterogeneity. Therefore, conducting high-precision, spatiotemporally continuous monitoring of surface deformation is of great significance for revealing subsidence mechanisms, assessing potential risks, and guiding disaster reduction decisions. GNSS and InSAR have become mainstream methods for monitoring surface deformation, but they still have limitations in terms of spatial sparsity, 3D deformation inversion capability, and data gaps in areas of strong deformation. To address these issues, this paper takes the Jinchuan copper-nickel mine’s No. 2 mining area as the research object and comprehensively utilizes multi-source monitoring data from GNSS and InSAR to construct a joint inversion model of the surface 3D deformation field based on posterior variance component estimation, achieving adaptive optimization of weight allocation and collaborative solution of 3D deformation. To address the issue of InSAR decorrelation in areas of strong deformation, which leads to missing deformation information, a fitting and estimation approach was applied to supplement six decorrelated points that spatially coincide with GNSS stations. These points are located in key deformation areas, and their reconstruction effectively improves the completeness and reliability of the deformation field in critical regions. Based on this, an automated solution process for the fusion model is implemented using MATLAB R2022b, and the joint inversion yields spatiotemporally continuous 3D deformation fields in the northward, eastward, and vertical directions. The results show that compared with traditional monitoring methods, the proposed fusion model exhibits higher inversion accuracy and stability under different InSAR technology conditions, effectively suppressing the impact of single data source errors on the overall solution results. Among them, SBAS-InSAR shows slightly higher accuracy in the vertical direction, while PS-InSAR achieves higher accuracy in the planar direction, as indicated by lower RMSE and MAE values. The research results improve the accuracy and reliability of surface deformation monitoring in mining areas, providing important technical support for safe mining and refined management. Full article

Robust tsunami casualty estimation is vital for Peru’s central coast. While static maps ignore evacuation dynamics, precise agent-based models (ABMs) are often too computationally demanding for rapid screening. To bridge this gap, we propose an efficient geospatial workflow coupling TUNAMI-N2 simulations with shortest-path routing. Evaluating four subduction scenarios across Chorrillos and Villa El Salvador, the model tracks census-block evacuation progress. By intersecting evacuation trajectories with tsunami arrival times, casualties are calculated using empirical depth-dependent fragility functions. Results highlight that delayed reaction times significantly increase mortality. Furthermore, a counterintuitive dynamic emerges in spatially constrained corridors lacking vertical evacuation: higher walking speeds can paradoxically increase fatalities by advancing evacuees into deeper inundation zones before being overtaken. This highlights that behavioral preparedness must be coupled with structural urban interventions. Ultimately, our scalable approach enables DRR (Disaster Risk Reduction) managers to rapidly map mortality hotspots and prioritize critical infrastructure improvements in highly exposed coastal zones. Full article

Grassland fire is one of the major disasters threatening regional ecological security. Its occurrence, development, and spread are closely related to the spatial distribution and coverage of surface vegetation and snow cover across grassland areas. As the primary combustible fuel source, higher vegetation coverage increases fuel load and continuity, thereby directly determining grassland fire danger levels and accelerating fire spread velocity. In contrast, snow cover imposes an indirect regulatory effect on the spatiotemporal pattern of fire danger factors: it lowers surface temperature, raises near-surface humidity, and restricts the germination and growth of herbaceous vegetation in cold seasons, which effectively reduces available combustible materials and weakens regional fire hazard conditions. Therefore, accurately obtaining the coverage status of vegetation (direct combustible fuel factor) and snow cover (indirect fire-regulating factor) in complex grassland scenarios is the essential premise for reliable grassland fire danger monitoring, early warning, disaster prevention and control, and regional ecological management. Aiming at the practical problems in complex grassland scenarios (such as undulating terrain, uneven vegetation growth, large differences in snow depth, and complex lighting conditions), including difficulty in extracting vegetation and snow-covered areas, blurred and confusing boundaries, and low accuracy in coverage calculation, which seriously restrict the technical bottleneck of precise monitoring of grassland fire danger factors, this study takes near-ground images collected by grassland fire danger factor monitoring stations as the core data source, and proposes an improved UNet image segmentation model combined with image segmentation technology and deep learning methods to realize precise extraction of vegetation and snow-covered areas and efficient calculation of coverage in complex scenarios. To improve the model’s feature extraction ability, boundary localization accuracy, and reduce model parameters and computational overhead, the CBAM-ASPP (Convolutional Block Attention Module—Atrous Spatial Pyramid Pooling) module is integrated at the end of the encoding path. The attention mechanism is used to enhance the weight of key features, and the multi-scale receptive field of atrous spatial pyramid pooling is utilized to strengthen the model’s ability to fuse features of vegetation and snow areas of different scales. The residual attention mechanism is introduced in the upsampling stage to effectively alleviate the gradient disappearance problem, improve the model’s ability to accurately locate the boundaries of vegetation and snow areas, and reduce segmentation errors. In the training process, a dynamically weighted hybrid loss function is adopted to dynamically adjust the weights according to the segmentation difficulty of different types of samples during training, optimize the model training effect, and improve the segmentation accuracy and generalization ability. Experiments were conducted using near-ground images of typical complex grassland scenarios as the dataset, and the performance of the proposed model was verified through comparative experiments. The results show that in the vegetation segmentation task, the mean Intersection over Union (mIoU) of the model reaches 84.70%, and the accuracy rate is 91.28%, which are 1.48 and 1.58 percentage points higher than those of the standard UNet model, respectively. In the snow segmentation task, the mIoU of the model reaches 92.74%, and the accuracy rate is 94.19%, which are 2.39 and 2.36 percentage points higher than those of the standard UNet model, respectively. At the same time, the number of parameters of the model is reduced by 12.85% compared with the standard UNet. Also, its comprehensive performance is significantly better than that of mainstream image segmentation models such as FCN, SegNet, and DeepLabv3+. Based on the standardized time-series data retrieved by the optimized segmentation model, this study further constructs a Grassland Fire Risk Index (GFRI) using the Analytic Hierarchy Process (AHP). Pearson correlation verification confirms that the GFRI has an extremely significant positive correlation with historical fire frequency, accurately capturing the seasonal dynamic rhythm of regional grassland fire occurrence. This integrated framework of intelligent segmentation and fire risk quantification provides a reliable technical solution for grassland fire factor monitoring, dynamic fire risk assessment, early warning systems, and refined regional ecological management. Full article

Ensuring the sustainability of ageing water-storage infrastructure is an increasingly urgent challenge under climate-driven hydrological extremes. In the Republic of Korea, approximately 18,000 small and medium-sized agricultural reservoirs—many several decades old—pose escalating risks to downstream communities and threaten progress toward SDGs 6, 11, and 13. This study presents a 0.5 m airborne LiDAR-based, GPU-accelerated two-dimensional shallow-water simulation of a hypothetical breach of the Geumosan Reservoir, South Korea, using a MUSCL + HLL solver verified against the Ritter (1892) and Stoker (1957) analytical dam-break solutions. Two scenarios are compared: Run A with a uniform Manning coefficient (n = 0.035) and Run B with spatially variable roughness derived from the Korean Ministry of Environment land-cover map (mean n = 0.0711). Mass conservation is preserved to within 0.01% during the closed-domain phase. Spatially variable roughness expands the total inundated area by 8.5% (3.05 → 3.31 km²) while reducing the Extreme-hazard zone, defined by the DEFRA hazard rating HR = h(v + 0.5), by 24% (1.49 → 1.14 km²); arrival times in the downstream urban corridor are delayed by up to 30 min. Uniform Manning assumptions therefore systematically overestimate extreme-hazard extents while underestimating the broader shallow-inundation footprint—biases comparable in magnitude to breach-parameter uncertainty. By delivering reproducible, georeferenced hazard, arrival-time, and damage-class maps for emergency action planning, the proposed framework supports risk-informed and sustainable management of ageing reservoir infrastructure and community-level disaster resilience aligned with the Sendai Framework and SDGs 6, 11, and 13. Full article

Drought–flood abrupt alternation (DFAA) has attracted increasing attention because of its severe compound impacts. This study used a daily SWAP index calculated by the precipitation data from 17 meteorological stations in Henan Province from June to September during the period of 1951–2020 to identify and analyze the spatiotemporal evolution of DFAA events. The results show that a drought duration of 10 d, together with a transition interval and a flood duration of 7 d, has a relatively good applicability for identifying DFAA events in Henan Province. The identified DFAA events were generally consistent with historical disaster records. DFAA events were characterized by slight decreasing trends in frequency and duration, with no obvious trend in intensity. The mean annual frequency, mean intensity, and mean duration of drought-to-flood (DTF) events were 2.19 events, 1.09, and 66.33 d, respectively, whereas those of flood-to-drought (FTD) events were 1.36 events, 0.36, and 73.82 d, respectively. Spatially, the distributions of DTF and FTD events exhibit distinct differences in their characteristics of frequency, intensity, and duration. Although the identification results obtained are based on precipitation as a single meteorological factor, the findings may provide a scientific basis for improving the understanding of DFAA evolution in the short term and enhancing regional disaster risk management in Henan Province, China. Full article

Land trusteeship is an innovative agricultural management model that connects smallholder farmers with modern agriculture. It promotes large-scale agricultural operations, but still faces the impacts of conventional natural disasters. Although agricultural disaster insurance serves as a critical mechanism for farmers to mitigate these natural risks, its risk-mitigation potential remains underutilized due to the persistent challenge of low insurance participation rates. This study develops a decision-making model for farmers’ purchase of agricultural disaster insurance under land trusteeship, drawing on protection motivation theory, market failure theory, and quasi-public goods theory. Using structural equation modeling, we empirically analyze survey data from 319 land-trusteed farmers to uncover the mechanisms and pathways influencing their insurance purchase decisions. The results indicate that: (1) Vulnerability and severity are positively associated with protection motivation through perceived response efficacy and self-efficacy, and protection motivation is directly associated with purchase decisions; (2) Government support has both direct and indirect effects on purchase behavior; and (3) Individual and household characteristics are significantly associated with purchase decisions, with pure farmers, Type I part-time farmers, and farmers with larger landholdings tending to purchase agricultural disaster insurance more often. Full article

In March 2018, seismic events associated with rock salt mining in Maceió, northeastern Brazil, led to the emergency relocation of families from risk areas, resulting in increased companion animal abandonment. This study assessed the association between systematic monitoring and environmental education and the reduction in abandonment during these relocation processes. Between March 2018 and September 2020, 567 animals were recorded in affected households, of which only 245 (43.2%) were relocated with their guardians. In response, the Integra Animal Project was implemented, integrating environmental education, continuous monitoring, sanitary management, and population control. By December 2024, 2559 households and 6673 animals had been monitored. A substantial reduction in abandonment and escape rates was observed over time, with abandonment decreasing from 56.8% to 5.45%. Cats showed significantly higher escape rates than dogs (chi-square test). These findings suggest that integrated strategies combining monitoring and environmental education are associated with improved animal retention during disaster-driven relocation, supporting their relevance for animal welfare, public health, and One Health approaches. Full article

Sustainable regional competitiveness and civil protection have traditionally been treated as distinct fields: the former rooted in regional economics and innovation studies, and the latter in disaster management, public safety, and risk governance. However, increasing climate-related hazards, technological disruptions, geopolitical instability, and the fragility of critical infrastructure demonstrate that competitiveness and resilience are deeply interconnected. This study presents a narrative literature review of publications from 2022 to 2025, integrating insights from evolutionary economic geography, institutional theory, sustainability studies, disaster risk reduction, spatial planning, and governance research. Building on this synthesis, we propose a novel conceptual framework that links civil protection capacity to long-term regional competitiveness. The framework introduces a multi-pillar model encompassing risk governance, institutional quality, critical infrastructure resilience, spatial planning systems, human capital dynamics and demographic stability, social trust and regional legitimacy, innovation driven by risk-management technologies, and multi-level governance coordination. Our analysis highlights how asymmetric threats are characterized by unpredictability, non-linearity, and uneven territorial impacts—interact with structural vulnerabilities, amplifying regional disparities and challenging conventional competitiveness strategies. Importantly, the framework demonstrates that robust institutions and integrated civil protection mechanisms can transform exposure to shocks into opportunities for adaptation, innovation, and structural upgrading. By conceptualizing competitiveness as a dynamic, emergent property shaped by economic, social, and risk-management capacities, this study positions civil protection as a strategic, measurable, and foundational component of sustainable regional development. The framework provides a theoretical foundation for future empirical research, policy design, and multi-criteria assessments aimed at fostering resilience-oriented competitiveness. Full article

Disaster risk reduction and management are essential for sustainable development in territories highly exposed and vulnerable to natural hazards. Recent disasters in the Canary Islands have highlighted the importance of proactive preparedness and systemic approaches to risk management, emphasizing the need to better understand existing barriers to disaster risk reduction (DRR). This study develops an analysis of risk governance within the current planning instruments in the Canary Islands, the island of Tenerife, and the municipality of Candelaria. The research examines the integration of DRR across strategic, territorial, urban, and emergency planning at the regional, insular, and municipal levels. The findings identify key challenges and opportunities for integrating DRR within existing planning frameworks, highlighting both the potential and the limitations of current instruments as cross-cutting tools for building more resilient territories. While Tenerife has a relatively solid administrative and planning structure that could support a more systemic vision of risk, sectoral fragmentation and coordination gaps remain. Overall, the study contributes to the ongoing discussion on advancing risk governance from a systemic perspective at the local level. The challenges identified delineate the boundaries and directions for improvement, offering a valuable contribution to the existing body of knowledge. Full article

To improve risk control at coal mining faces and reduce accident risks, this study first extracts high–frequency risk factors from 171 valid coal mining face accident cases (2020–2023) and integrates synthesis of the literature to establish a 21–factor risk indicator system covering human–machine–environment–management dimensions, and invites 10 senior experts in coal mine safety–covering mining engineering, safety science and engineering, mine ventilation, geological disaster prevention and coal mine safety management–for evaluation. Secondly, a hierarchical structure of factors is developed based on adversarial interpretive structural modeling (AISM), and the driving force and dependence of each factor are analyzed using the matrix impact cross–reference multiplication applied to a classification (MICMAC). A fuzzy Bayesian network (FBN) model is then constructed with the AISM structure as a topological constraint to clarify factor relationships and quantify the risk propagation uncertainty. Finally, an empirical analysis is conducted using the X Coal Mine. The results indicate that the “illegal and irregular organization of production” is the root control factor. The risk probability of the mining face is 86.1%, with “inadequate specialized prevention and control” having a high occurrence probability, and “illegal operation” and “illegal command” showing the most significant probability changes. Sensitivity analysis identifies “inadequate specialized prevention and control” as the most sensitive factor, which, together with the environmental factors, falls into the Level I unacceptable risk category. This research determines risk control priorities and provides a theoretical basis for coal mine safety management. Full article

Intensive anthropogenic activity leads to ecosystem degradation, necessitating a shift from conventional management approaches towards proactive environmental risk assessment strategies. This study presents a quantitative methodology for calculating a Comprehensive Environmental Risk Indicator (CERI) based on the analysis of types of permitted use (TPU) of land plots in the Russian Federation. The methodology comprises three stages: determining a base risk weight for each TPU, statistically weighting impact factors using multiple regression analysis (MRA), and synthesizing the final CERI. This research identifies five key impact factors: industrial pollution, biogenic and agrogenic effects, landscape and resource changes, anthropogenic and household load, and specific risks and disasters. The results demonstrate that biogenic and agrogenic effects (weight 0.450) and specific risks and disasters (weight 0.398) are the most significant factors. Industrial pollution and landscape changes were excluded from the model due to multicollinearity. The model’s R² (0.194) confirms its statistical validity as a foundational framework for macro-level risk evaluation. Further improvements could address limitations related to cadastral data variability and the integration of localized environmental parameters. The developed CERI was integrated into geographic information systems (GIS) to visualize risk gradients across land parcels. This study concludes that the use of statistically substantiated factor weights enables objective territorial zoning, facilitating a transition from subjective expert assessments to management based on actual environmental consequences. Full article