Search Results (2,490)

Floods are among the most devastating natural hazards, intensified by climate change and rapid urbanization. This study introduces a novel deep learning framework, Transfer Learning-Enhanced FEDformer (TLE-FEDformer), designed for accurate and temporally consistent flood inundation mapping. The framework integrates pre-trained Xception backbones for robust multi-sensor feature extraction from Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery, a cross-modal fusion module to align heterogeneous modalities, and the Frequency Enhanced Decomposed Transformer (FEDformer) for efficient frequency-domain temporal modeling. This architecture effectively captures long-range dependencies and flood dynamics including onset, peak, duration, and recession, while addressing challenges such as cloud contamination, speckle noise, and limited labeled data. Comprehensive experiments demonstrate superior performance, achieving an overall accuracy of 98.12%, an F1-score of 98.55%, and an Intersection over Union (IoU) of 97.38%, outperforming baselines including Convolutional Neural Networks, Capsule Networks, and transfer learning alone. Ablation studies validate the contributions of each component, while sensitivity analyses confirm robustness across hyperparameters. Uncertainty quantification via Monte Carlo dropout highlights high confidence in core flooded regions. Preliminary generalization tests on independent events yield IoU > 94%, indicating strong transferability. TLE-FEDformer advances operational flood monitoring by providing reliable, scalable, and temporally consistent mapping from multi-sensor remote sensing data. This approach offers significant potential for real-time disaster response, early warning systems, and damage assessment in flood-prone regions worldwide. Full article

To address high uncertainty, dynamic evolution, and limited information in emergency decision-making for major sudden disasters, this paper proposes a sliding-window game-theoretic method with four reference points for emergency response selection. Firstly, interval-valued T-spherical fuzzy sets are adopted to capture decision-makers’ uncertain and hesitant evaluations in interval form. Subsequently, a four-reference-point framework, including the external, internal, average development speed, and ideal proximity reference points, is established to reflect stage-dependent psychological baselines. Furthermore, criterion weights are updated by a sliding-window game-theoretic combination weighting scheme that integrates entropy, anti-entropy, criteria importance through intercriteria correlation, and the coefficient of variation, and performs rolling updates across stages. Prospect values are then computed relative to the four reference points and aggregated to rank alternatives at each stage. Finally, a case study of the 2024 Huludao extreme rainfall event applies the proposed method to evaluate four candidate schemes across six criteria over three decision stages. Results show that rescue cost has the highest weight in all stages, while the importance of rescue speed decreases and social impact increases as the response progresses. The proposed method identifies a comprehensive flood relief scheme led by the People’s Liberation Army and the People’s Armed Police Force as the best option in all stages, because it achieves the highest comprehensive prospect values among all alternatives. Comparative analyses indicate more consistent identification of the optimal scheme than existing approaches, supporting sustainable and resource-efficient disaster management. Full article

Turkey, located on one of the world’s most active fault lines, frequently experiences major earthquakes. The 2023 Kahramanmaraş earthquakes (Mw 7.6 and 7.7) caused significant destruction and housing shortages. Post-disaster shelters are often provided using containers, which require flat and solid ground. This typically involves pouring concrete foundations, but high demand for materials and labor hinders rapid installation. This study investigates screw piles as an alternative foundation system for container settlements. Screw piles can eliminate the need for concrete, offering a faster, cost-effective, and environmentally friendly solution. Finite element analyses using Abaqus were conducted to assess the structural behavior of container foundations with screw piles under real earthquake records. Additionally, a decision-making analysis based on the Analytic Hierarchy Process compares screw piles and concrete foundations in terms of cost, time, sustainability, and safety. Results show that screw piles reduce structural responses and are a more feasible post-disaster foundation solution. Full article

Smart cities require effective disaster management (like flooding, solar storms, sandstorms, or hurricanes), as it directly impacts people’s lives. The key challenges of disaster management are timely detection and effective notification during the crisis. This research presents a smart multi-layer framework for notification classification and management before and during flooding disasters. The framework includes an early detection module as the main phase in the alerting process. This step depends on an Ensemble Learning (EL) model based on a triad of the three best selected models (Deep Learning (DL), Random Forest (RF), and K-nearest Neighbor (KNN)) to analyze data collected continuously from the Internet of Things (IoT) layer. In the boosting phase, the framework utilizes Large Language Models (LLMs) with DL to analyze social textual crowdsourcing data. The results will enable the framework to identify the most affected areas during a flood. The framework adds a fog computing layer alongside a cloud layer to enable instantaneous processing of user responses and generate specialized alerts based on contextual factors such as location, time, risk level, alert type, and user characteristics. Through testing and implementation, the proposed algorithms demonstrated an accuracy rate of over 98% in detecting threats using a dataset of real, collected weather and flooding data. Additionally, the framework proposes a centralized control panel and a design of a smartphone application that offers essential services and facilitates communication among managed civil defense teams, citizens, and volunteers. Full article

This study evaluates the tracking performance of structural damages in disaster environments by combining YOLOv8 detection with the BoT-SORT tracker. Cracks and exposed rebar, characterized by fine and irregular structures, showed high sensitivity to viewpoint changes, with camera motion compensation (CMC) improving $IoU$ by +19.63% and +20.23%. For exposed rebar, the joint use of CMC and re-identification (Re-ID) further increased $IDF1$ by +37.73%, emphasizing the effectiveness of appearance-based matching. In contrast, delamination and concrete debris, with stable morphology and clear boundaries, exhibited limited benefits from CMC, improving $IoU$ by +11.17% and +3.28%. Analysis of $MOTA$, $IDF1$, and $HOTA$ confirms that fine-grained damages require motion- and appearance-based strategies, while stable types maintain high performance through detection consistency. These results highlight the importance of tailored tracking strategies for enhancing disaster-response robots and structural monitoring systems. Full article

Marine storm surge disasters occur frequently with complex and variable scenarios, causing severe casualties and economic losses in coastal areas. However, existing research still has limitations in the integrated analysis of event chain and emergency plan knowledge, the efficiency and accuracy of disaster knowledge extraction, and the intelligence level of knowledge reasoning methods. To address these challenges, this study proposes a “scenario-response” knowledge reasoning method for marine storm surge disasters that integrates event chains and emergency plans. First, disaster event chains and emergency plan processes are structurally modeled to enable unified semantic representation, and a knowledge fusion mechanism is designed to integrate event chains with emergency response procedures. Second, an improved OSS-CasRel knowledge extraction model, enhanced with a domain-specific dictionary, is constructed to extract entities and relations from marine storm surge texts and to build a spatiotemporal knowledge graph. Third, a knowledge reasoning approach based on BERT and downstream text matching models is implemented to generate adaptive and visualized emergency response plans. Experimental results demonstrate that the OSS-CasRel model achieves an accuracy of 80% in entity and relation extraction; in the knowledge graph, the matching overlap rate between the “response” text generated by model reasoning and the original node information exceeds 90%. This study can effectively improve the intelligent emergency response capability for marine storm surge disasters and provide scientific support for emergency decision-making in coastal areas. Full article

Background: This paper addresses the need to clarify and highlight the vital roles non-state actors play in strengthening the disaster management ecosystem, drawing on knowledge and experience across sectors and entities. The objective is to underscore the irreplaceable roles of non-state actors in disaster response and the need for shared capacities through the coordination, adoption, and application of agreed-upon protocols across actors and contexts. The research’s ultimate goal is to provide policymakers, crisis managers, non-state actors, and volunteer coordinators with a comprehensive overview of the functional areas, competencies, and capacities of civic organisations across all phases of disaster management. Integrating these organisations into existing governmental crisis management systems offers an opportunity to enhance community resources and capacities through unified communication and interoperability protocols based on existing technical and ethical standards. Methods: The research reviews academic literature, legal and policy frameworks, and grey literature, including recommendations and experiences documented in a repository of 140 CORDIS EU-funded initiatives that illustrate expert and institutional opinions on disaster management. The manuscript also relies on secondary data analyses presenting the opinions collected from 50 participants in an interactive group exercise on the role of non-state actors and volunteers. It further draws on aggregated knowledge from nine consultative workshops involving 20 civic and governmental organisations, synthesising practices, formal standards, robust coordination frameworks, and command-and-control system rules into an innovative voluntary disaster response protocol for non-state actors and volunteers. The findings demonstrate the value of non-state actors in disaster management and how gaps in their engagement can create opportunities to strengthen the disaster management ecosystem by enhancing the cohesion of capacities and resources. Compared with international standards (INSARAG, etc.), a protocol incorporating technical and integrity norms in an accessible, adaptable format emphasises the importance of integrating non-state actors into the formal disaster crisis management system. Conclusions: Establishing a set of standards for coordinated awareness and response, facilitated by continuous communication of roles and competencies among disaster responders at both local and international levels, is essential for the sustainable mitigation of negative impacts before, during, and after emergencies or catastrophic events. Full article

Systems that operate in dangerous environments are becoming essential in case of emergencies. This survey reviews the latest ground reconnaissance robots using computer vision (CV), machine learning (ML), MCU-based control, LoRa communication, DC motors, and dual-power systems. The analysis includes hardware and algorithms, and their performance in the field and lab. There has been clear progress in navigation, sensor fusion, and situational awareness. The main challenges which remain include the use of energy and standardization of benchmarks. This survey focuses exclusively on Unmanned Ground Vehicles (UGVs) for disaster reconnaissance, examining recent advances in hardware, software, and autonomy. The survey highlights the improvements in navigation, sensor fusion, and intelligence, and identifies remaining challenges such as energy limitations, robustness in harsh conditions, and the lack of standardized benchmarks. The analysis synthesizes findings from over 190 recent studies (2020–2025) in ground-based disaster robotics, providing a comprehensive overview of current capabilities and research gaps. It encapsulates all issues with their remedy for future disaster-response systems. Full article

The increasing frequency and intensity of extreme climate events have posed more geohazards worldwide. It is therefore crucial to quantify and map risk to reduce disaster-related losses. The main objective of this study is to propose a quantitative framework to conduct risk assessment of buildings and infrastructures impacted by geohazards. A debris flow hazard in Tianjin, North China was taken as a case study. A physically based model and the Gumbel extreme value distribution were utilized to construct a range of extreme rainfall and runoff scenarios. The FLO-2D and ABAQUS software were subsequently employed to simulate the surging behavior of the debris flow and assess the structural vulnerability of buildings, respectively. Furthermore, the number of elements at risk and economic values were estimated to generate risk maps. The results revealed that variations in peak discharge in the channel evidently affected flow velocity and depth, thus elevating the debris flow intensity and the likelihood of the materials threatening buildings. The stiffness degradation of concrete was strategically used as the indicator to quantify structure vulnerability and effectively present the dynamic responses under the impacts of the debris flow. Under a 100-year return period rainfall scenario, the proportion of very high- and high-risk areas reached 31%, with the estimated economic loss approximately ¥167.7 million. This highlighted the critical role that extreme rainfall played in shaping both the spatial distribution and severity of debris flow risks. The proposed method provides a scientific basis for enhancing the resilience of mountainous regions to compound natural disasters exacerbated by climate change. Full article

Due to their advantages of being low-cost, lightweight and flexible, and having wide shooting coverage, UAVs have played an important role in situational awareness in the fields of disaster prevention and mitigation, urban planning and management, etc. In these applications, UAV aerial photography is limited by the field of view, and high-definition panoramic images of the complete target area cannot be obtained. Image mosaic technology is essential, but an image mosaic using only a single UAV cannot meet the high real-time requirements for situational awareness. In response to the above problems, this paper proposes a multi-UAV fast aerial image mosaic method based on key frames. First, the multi-UAV area coverage flight strategy is determined according to the size of the task area and the UAV flight parameters; then, the field of view of the pod, the flight speed, and the flight altitude are used to determine the key frame extraction time period during the UAV aerial photography process. The image matching-rate calculation method is designed and the key frames are extracted during the extraction time period, and the key frames are returned to the ground visual puzzle system; in the ground visual puzzle system, the improved Laplacian pyramid method is used to quickly fuse and stitch the key frames extracted by each UAV to obtain a panoramic stitched map. The experiment shows that the method can quickly obtain high-precision real-scene map information of the task area. Compared with the single-UAV method and the multi-UAV full video stream-splicing method, this method greatly reduces the consumption of computing power and the requirements of communication bandwidth and improves the efficiency and real-time performance of panoramic map acquisition. Full article

Crowd mass disasters occur over a relatively short time, and rescue operations in disasters, such as earthquakes, are challenging because of people’s behavior, type, or location. Therefore, it is essential to devise means and methods to manage such problems to minimize the consequences as much as possible. During disasters, rescue operations should be conducted in a timely conducted to save people’s lives. Otherwise, losses and consequences are severe, and if there are no proper rescuing operation models, the situation worsens, and the consequences are devastating. In particular, the allocation and coordination of limited rescue resources have a critical impact on response times and the number of lives saved. This paper aims to develop an Agent-Based Simulation (ABS) model for rescuing operations in crowd-mass disasters with six main intelligent agents. The proposed model explicitly represents the interactions among victims, rescuers, command-and-control entities, transportation assets, road networks, and affected infrastructure within a GIS-based urban environment. The developed model is based on an enhanced approach to improve rescue agents’ tasks allocation operations that enable modeling and simulation to make critical decisions for people to be rescued in a crowded mass disaster. Our task-allocation mechanism incorporates dynamic accessibility of roads, time-dependent rescue capacity, and context-aware prioritization of victims. Three related task-allocation strategies from the literature are used as baselines under identical scenarios, and performance is compared in terms of average rescue time and number of rescued victims. Results show that the proposed model achieves more efficient and robust rescue operations in most simulated experiments. Full article

Background/Objectives: Disasters have a substantial impact on health systems and populations worldwide, with increasing frequency, mortality, and economic losses associated with natural hazards. The United Nations emphasises that disasters result from the interaction between hazards, exposure, and vulnerability, requiring integrated, people-centred health responses aligned with the 2030 Agenda. However, empirical evidence describing specific nursing interventions, particularly during response and recovery phases, is limited. This study aims to analyse the fundamental nursing care interventions provided to disaster victims in the Autonomous Region of Azores, Portugal. Methods: A qualitative multiple case study was conducted using documentary analysis of the nursing records from two disaster survivors with different clinical trajectories. Data were collected between August 2023 and May 2024 through complete transcription of nursing documentation contained in the clinical files. Data analysis followed Yin’s case study methodology and was theoretically supported by the Fundamentals of Care Framework. Results: The findings indicated a predominance of interventions addressing physiological needs during the acute phase, which progressively evolved to maintenance, psychosocial support, and adaptation needs during prolonged hospitalizations. Nursing care integrates advanced technical skills with relational and person-centred interventions, including emotional support, therapeutic communication, and promotion of patient autonomy. Conclusions: Nursing practice in disaster situations should be conceptualised as integrative, person-centred care grounded in international nursing frameworks. Strengthening disaster-specific nursing education, developing phase-adapted care protocols, and promoting multicentre longitudinal research appear to play a critical role for advancing nursing care models and informing health policies in disaster-prone regions. Full article

In GNSS-denied large-scale outdoor environments, UAVs and UGVs that rely solely on visual–inertial odometry (VIO) suffer from accumulated global drift as the trajectory grows. Meanwhile, inter-platform ultra-wideband (UWB) ranging exhibits unknown, time-varying noise under NLOS/multipath, rendering naïve weighting unreliable. This paper presents an anchor-free collaborative localization framework for UAV–UGV teams that fuses pairwise UWB ranges (including UAV–UAV, UAV–UGV, and UGV–UGV) with onboard VIO in a factor-graph backend via a two-stage robust scheme. First, we bound VIO drift using per-agent state covariance and reject UWB outliers with a Mahalanobis gate, preventing early-stage bias when VIO is still accurate. Then, during global optimization, we adaptively estimate the Fisher information of UWB factors from measurement–state residuals, enabling online self-tuning of measurement confidence under time-varying SNR. Real-world experiments with three UAVs and two UGVs over multi-level rooftops and forest–open areas (~1.6 km²) show that, compared to an outlier-only variant, the proposed method further reduces localization RMSE by about 24.6% and maximum error by about 31.2% for both UAVs and UGVs, maintaining strong performance during long trajectories dominated by VIO drift and NLOS ranges. The approach requires no fixed anchors or GNSS and is applicable to UAV–UGV teams for disaster response, cooperative mapping/inspection, and bandwidth-limited operations. Full article

The Hulukou-Xiangbiling section of the Jinsha River is located in a typical high-mountain gorge area characterized by a complex geological environment, rendering it highly susceptible to landslide disasters. To reveal the deformation mechanisms of typical landslides in this region under hydrological effects, this study employed the Small Baseline Subset InSAR (SBAS-InSAR) technique to process multi-track Sentinel-1 SAR images acquired between 2021 and 2024. Long-term deformation time series were extracted for the Xiaxiaomidi and Chenjiatian landslides. On this basis, a systematic multi-scale coupling analysis of the deformation characteristics was conducted using trend-cycle decomposition, Continuous Wavelet Transform (CWT), Cross Wavelet Transform (XWT), and Wavelet Coherence (WTC). The results indicate that although the two landslides are located in the same river section, their deformation mechanisms and hydrological response patterns differ significantly. The deformation of the Xiaomidi landslide is mainly concentrated in the lower part of the slope, exhibiting a characteristic of continuous acceleration. The analysis demonstrates that the evolution of this landslide is primarily controlled by hydrodynamic processes such as toe unloading, water body erosion, and water level fluctuations. In contrast, the Chenjiatian landslide displays a distinct dominant cycle of 365 days, manifesting as a composite mode of long-term creep superimposed with seasonal acceleration. Its deformation shows a high correlation with rainfall (correlation coefficient > 0.9), with a lag effect of approximately 1 to 2 months. This reflects the dominant role of rainfall infiltration and pore pressure transfer in the landslide dynamics. Full article

In Canada and elsewhere, there is an ascendancy of a whole-of-society approach that centres shared responsibility for wildfire management. This article engages the debates on the rise of shared responsibility for wildfire management to argue that this context demands a renewed research focus on understanding how the public allocates responsibility for wildfire management. We illustrate this argument through a case study of public engagement with wildfire risk and shared responsibility in Whistler, British Columbia, western Canada. Our case study draws on evidence from a quantitative survey administered to 1311 participants in the spring and summer of 2024. The study reveals a near-universal concern about wildfires among the participants and a high level of risk perception. This is consistent with community climate and wildfire reports and plans. This level of concern is driving a high level of mitigation activity completion among participants, even though the level of preparedness is mixed. Our study found a marked pattern of responsibility allocation across the phases of wildfire management. Participants put the municipal government at the forefront of mitigation, preparedness, and response. The provincial government was ranked as most responsible for recovery. Homeowner responsibility declined as one moves from mitigation and preparedness through to response and recovery. Private actors, such as insurance, have greater responsibility in the recovery phase. Multivariate General Linear Models (GLMs) show that how respondents allocate responsibility for various aspects of wildfire management is influenced by home ownership, prior wildfire experience, perceived preparedness, and commitment to bearing the costs of FireSmart assessment. We conclude that a sustained research commitment is needed to further elucidate the dynamics of public expectations and attitudes in the context of shared responsibility for wildfire management. Full article