Search Results (1,608)

Natural and human-induced disasters can cause significant loss of life and property, particularly at the building level, highlighting the need for effective early detection, real-time monitoring, and rapid post-disaster response. Current disaster management approaches largely rely on citizen reports and manual observations, which may lead to delays and inefficient resource allocation, especially in large-scale events. This study proposes a secure, modular, multi-layer disaster monitoring and decision-support architecture that integrates sensor-based building-edge monitoring units deployed at both the building and apartment levels with a central emergency monitoring system. The architecture comprises three main layers: edge sensing, secure cellular communication, and central decision-making. Building-edge monitoring units collect data related to structural motion and inclination indicators, fire, flooding, and gas leaks, perform preliminary processing, and transmit aggregated data securely to the central system. Communication security is ensured through a certificate-based authentication mechanism supported by a dedicated certificate authority, reducing the risk of unauthorized access and fraudulent data injection. The central system performs automated event detection and separately evaluates physical building condition and communication status, enabling prioritized response planning. To evaluate feasibility, a two-building prototype was implemented and tested through scenario-based experiments involving two independently operating building-edge monitoring units connected to the same central monitoring system. The prototype demonstrated concurrent secure data acquisition and central aggregation from two buildings; however, district- and regional-scale performance requires further validation through larger-scale controlled load tests and field deployments. Under laboratory conditions, the prototype demonstrated sensor-data acquisition, authenticated transmission, and centralized event classification. End-to-end latency and building-edge monitoring unit power consumption were also measured; however, the prototype was not validated under environmental conditions representative of real disasters. Overall, the findings suggest that sensor-based, secure, and centralized monitoring systems may complement traditional disaster management approaches. Full article

The transition from ISO/IEC 27001:2013 to ISO/IEC 27001:2022 is often approached as a certification and documentation update. However, for banking institutions with critical digital operations, the transition may create a distinct form of information security exposure when existing controls, evidence, ownership, and risk treatment decisions are not fully aligned with the revised control structure. This study examines transitional risk exposure in Bank XYZ, a banking institution with an established ISO/IEC 27001:2013-based Information Security Management System (ISMS). Using a qualitative single-case study design, the research analyzes ISMS documents, 96 information security risk records, Statement of Applicability records, control implementation evidence, ISO/IEC 27001:2013-to-ISO/IEC 27001:2022 control mapping materials, and stakeholder validation within the scope of core banking development and operations in data center and disaster recovery center environments. Risk exposure was assessed by comparing inherent and residual risks using a likelihood-impact matrix, while transition readiness was evaluated through control applicability, evidence adequacy, ownership clarity, supplier dependency, human factor readiness, and transition governance. The findings show that Bank XYZ implemented or provided evidence for 109 of 114 ISO/IEC 27001:2013 controls and reduced 96 inherent risks, consisting of 38 Moderate and 58 Moderate-to-High risks, into 13 Low and 83 Low-to-Moderate residual risks. Nevertheless, the transition review revealed that a mature residual risk position under ISO/IEC 27001:2013 does not automatically indicate readiness for ISO/IEC 27001:2022. Of 11 newly introduced controls, 2 were implemented, 4 were partially implemented, 4 were not implemented, and 1 was not applicable. The most significant transition gaps were found in threat intelligence, information deletion, data masking, and data leakage prevention. The study contributes by distinguishing transitional risk exposure from residual risk and by offering a control-level basis for prioritizing ISO/IEC 27001:2022 transition activities in banking institutions. Full article

When natural disasters strike, the destruction of terrestrial communication infrastructure creates urgent demands for emergency networks. Efficient UAV deployment in capsule airport–UAV hierarchical networks has emerged as a critical challenge due to limited aerial resources and stringent quality-of-service requirements. This paper develops a QoS-aware joint optimization model for UAV deployment, integrating air-to-ground (A2G) channel modeling with resource allocation, where upper-level position optimization is coordinated with lower-level frequency allocation and power control through a hierarchical decomposition strategy. The proposed QoS-TLK-VNS-K algorithm combines graph coloring for interference mitigation with iterative power control for SINR guarantee. Empirical evaluation using multi-scenario simulations demonstrates that the proposed approach significantly outperforms the traditional distance-based coverage method. Statistical validation over 30 independent runs demonstrates significant improvements in QoS satisfaction ($+23.8\%$, $p<0.001$), average SINR ($+104.0\%$, $p<0.001$), minimum user rate ($+194.9\%$, $p<0.001$), and Jain’s fairness index ($+16.2\%$, $p<0.001$) compared to the distance-based baseline. These results demonstrate that the framework effectively addresses the trade-off between interference suppression and network connectivity in multi-UAV emergency communication systems. Full article

Accurate detection in the early stages of a fire is a crucial prerequisite for the efficient implementation of fire suppression and emergency rescue operations. Its accuracy and timeliness directly affect the control of disaster loss severity. Traditional fire detection methods mainly include three categories, which are manual inspection, sensor detection, and visual recognition. However, manual inspection is restricted by labor costs and time efficiency, making it difficult to achieve large-scale, high-frequency and real-time fire monitoring. Sensor detection is easily interfered by environmental factors such as temperature, humidity, and dust, leading to frequent false alarms and missed alarms. Visual recognition technology has shortcomings in aspects such as detailed feature perception, dynamic scene modeling, and reasoning robustness in complex environments, making it difficult to meet the requirements of high-precision detection. To address these issues, this study innovatively proposes a lightweight fire and smoke detection model based on semantic enhancement and frequency domain perception modeling, which is named the SemaFire you only look once (SemaFire-YOLO) model. The model constructs a large language and vision assistant (LLaVA) semantic guidance module, which uses a large language model to understand and guide the semantic features of images, thereby enhancing the saliency representation intensity of small and weak target regions. Then, a Haar wavelet-based downsampling module is adopted, which compresses spatial information while preserving high-frequency features such as flame edges and smoke textures, improving the accuracy of target recognition. Next, the convolution modulation mechanism is introduced to replace the traditional attention mechanism, enhancing the overall modeling efficiency and reducing computational overhead. Finally, a Dynamic Tanh normalization module is adopted to replace the batch normalization module in the traditional YOLO algorithm, strengthening the model’s representation stability and reasoning robustness under unstable input distributions. Experimental results show that the SemaFire-YOLO model achieves a mean average precision (mAP@0.5) of 64.30% on the fire image dataset, which is 0.8, 2.0, 0.6, and 3.8 percentage points higher than that of mainstream models such as YOLOv5n, YOLOv8n, YOLOv11n, and YOLOv12n, respectively. It exhibits better boundary detection capability and practical deployment potential. Through visual analysis, the results indicate that the improved SemaFire-YOLO model achieves more accurate detection and higher confidence in actual complex scenarios, further verifying the model’s robustness and accuracy in complex scenarios such as low contrast and dynamic fire conditions. Full article

Flash floods are among the most destructive natural hazards, often causing substantial loss of life and severe damage to infrastructure and property. Predicting flood-prone areas remains challenging because flood generation is controlled by complex interactions among topographic, hydrological, climatic, and environmental factors. In this study, six machine learning algorithms—Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree Classifier (DTC), AdaBoost, and Artificial Neural Network (ANN)—were developed to predict flash-flood inundation locations using satellite-derived flood inventories from two major rainfall events in Wadi El-Darb and Wadi El-Allaqi, Egypt. Model performance was evaluated using accuracy, precision, recall, and F1-score. During model development, Random Forest and Decision Tree Classifier achieved the highest prediction accuracy (94%), followed by AdaBoost and ANN (92%), while Logistic Regression (89%) and SVM (88%) also produced satisfactory results. To evaluate model generalization, the trained models were independently validated using a rainfall event in Wadi Hodein (Egypt) and a major flash-flood event that occurred in Oman during April 2024. The external validation showed that AdaBoost achieved the highest predictive performance in both validation basins, with accuracies of 87% for Wadi Hodein and 83% for Oman, providing encouraging initial evidence of applicability across hydrologically similar arid watersheds, While AdaBoost and Logistic Regression maintained satisfactory performance during external validation, other algorithms exhibited noticeable reductions in recall and F1-score, particularly in the Oman case study, indicating variability in model generalization across independent watersheds These findings suggest that the proposed framework may support flood susceptibility assessment in ungauged arid environments with comparable hydrological characteristics, although further validation across a wider range of climatic and geological settings is needed. Overall, the results highlight the value of integrating satellite remote sensing with machine learning to support flood hazard assessment, disaster preparedness, early warning systems, and flood risk management in data-scarce regions. Full article

Variations in urban surface runoff are often attributed to static infrastructure, neglecting the non-stationary hydrological responses induced by rapid urbanization and intricate micro-topography. This study introduces a Pressure–Trend–Pulse (PTP) framework to examine surface runoff dynamics in Kunming, China. By integrating continuous Soil and Water Assessment Tool (SWAT) simulations (2005–2024), Sen’s slope estimation, the Mann–Kendall test, robust residual analysis, and Self-Organizing Map (SOM) clustering, we quantify these multi-dimensional changes. The findings indicate: (1) runoff displays a structural north–south gradient, with the generation centroid migrating northward at a rate of 2.3 km per decade; (2) non-stationary positive trends (0.16 mm/year) are exclusively concentrated in northern sub-basins, which constitute 26.74% of the total area, thereby exacerbating long-term cumulative pressure; and (3) detrended residual analysis reveals high-frequency pulse volatility predominantly in the southern sink areas. Overlaying 139 historical waterlogging points confirms that trend-driven and pulse-driven risks account for 30.22% and 13.67% of urban disasters, respectively. Furthermore, approximately 22% of waterlogging occurrences fall within non-significant downstream zones, implying a potential upstream-downstream source–sink decoupling. The PTP framework highlights the necessity of differentiated resilience planning: upstream source-control and downstream adaptive buffering. Full article

Water inrush disasters in karst tunnels pose a significant threat to construction safety, project timelines, and the long-term sustainability of infrastructure. Effective risk assessment is crucial for mitigating these hazards and ensuring the resilient development of underground transportation networks. This study proposes a quantitative risk assessment model that integrates the entropy weight method with attribute interval recognition theory to address the uncertainties inherent in complex geological environments. First, a hierarchical evaluation index system is established based on four primary controlling factors: stratigraphy, geological structure, topography, and hydrogeology. Subsequently, the entropy weight method is employed to objectively determine the weight of each index, thereby minimizing human bias. The attribute interval recognition model is applied to calculate the comprehensive attribute measure for each tunnel segment, effectively managing the fuzziness of risk classification boundaries. The risk grade is ultimately determined using the confidence criterion. The proposed model is applied to the Qigan Mountain karst tunnel in Chongqing, China, which is divided into 56 segments for detailed analysis. Results show 41.32% (4088 m) high-risk, 40.14% (3971 m) medium-risk and 18.55% (1835 m) low-risk sections, which are highly consistent with the theoretical water inflow calculation results. The model realizes accurate and quantitative water inrush risk assessment, providing a scientific basis for disaster prevention and control in karst tunnel construction, and further promoting the sustainability and safety of underground engineering in karst areas. Full article

Earthquake-induced accidents involving buried oil and gas pipelines in water-network regions are governed by coupled seismic, hydrological, geotechnical, and emergency-response factors, while complete accident records are scarce. To support scenario-based consequence analysis under sparse-data conditions, this study develops an accident scenario analysis framework that integrates numerical simulation with Bayesian probabilistic inference. Scenario elements are organized according to four categories: disaster-causing factors, elements at risk, hazard-inducing environment, and emergency management. Finite element analysis and computational fluid dynamics are used to quantify pipeline mechanical response and hydraulic-scour effects, and the resulting physical responses are embedded in a dynamic Bayesian network as state evidence and transition constraints. Triangular fuzzy numbers are used to process expert evaluations and determine node probabilities. The resulting multi-mechanism simulation-Bayesian inference framework quantifies the accident chain from earthquake loading to pipeline deformation, leakage, fire or explosion, and emergency control. Forward reasoning estimates the probability of each scenario state, sensitivity analysis identifies key drivers, including strong earthquakes triggering landslides and rainfall during flood seasons, and disaster-chain analysis clarifies the dominant causative pathways. The framework provides a reproducible basis for scenario analysis, consequence assessment, monitoring and early warning, and emergency response planning for buried oil and gas pipelines exposed to seismic hazards in water-network regions. Full article

Despite the rapid expansion of unmanned aerial vehicle (UAV) applications across precision agriculture, logistics, infrastructure inspection, disaster response, and aerial surveying, objective and quantitative hardware evaluation criteria for UAV components remain insufficiently developed. This paper proposes quantitative key performance indicators (KPIs) for thirteen core hardware subsystems, including airframe and propulsion, battery and power supply, flight control, wireless communication, imaging (camera), Global Positioning System (GPS)/Global Navigation Satellite System (GNSS) positioning, thermal management, acoustic and vibration characteristics, AI-based autonomous flight, electromagnetic compatibility (EMC), cybersecurity, and reliability and environmental qualification, together with LiDAR payload evaluation criteria. International standardization activities by 3GPP (Release 15/17), IEEE (1936–1958 series), American society for photogrammetry and remote sensing (ASPRS), and national regulatory frameworks are synthesized to define measurable performance metrics and recommended test methods for each subsystem. An integrated KPI matrix maps application-domain-specific performance targets—encompassing surveying (real-time kinematic (RTK) horizontal accuracy ≤ 2 cm root-mean-square error (RMSE), ground sample distance (GSD) ≤ 2 cm/px), infrastructure inspection (LiDAR payload up to 8 kg, beyond visual line-of-sight (BVLOS) latency ≤ 140 ms), and logistics delivery (payload ≥ 2 kg, precision landing ≤ 50 cm)—demonstrating that no universal platform can simultaneously satisfy all domain requirements. A fuzzy-AHP weighting procedure and inter-subsystem coupling analysis are introduced to address size, weight, and power (SWaP) trade-off relationships that purely additive scoring models cannot capture. The proposed evaluation framework is intended to contribute practically to UAV standardization, certification, and quality management across the full design–procurement–operation lifecycle. Full article

Recurrent flooding along the Wabi Shabelle River has repeatedly displaced communities in Beledweyne, Somalia, prompting a 2020 government-led relocation policy intended to reduce long-term flood risk exposure. Whether such resettlement constitutes a durable change-detection method disaster risk reduction strategy in semi-arid East Africa remains empirically untested. We integrate ten years of Sentinel-1 SAR (259 scenes, 2015–2025), three global DEMs (Copernicus GLO-30, FABDEM, SRTM), CHIRPS precipitation, and BFAST changepoint analysis to map flood frequency at 10 m resolution. The Z-score showed the strongest coupling with 12-day cumulative precipitation (Pearson r = +0.338; block-bootstrap 95% CI [+0.13, +0.49], excluding zero) and strong agreement with the log-ratio method (r = +0.676), whereas the conventional fixed −17 dB threshold produced a physically implausible negative correlation (r = −0.248). These conclusions were stable across alternative thresholds. HAND from all three DEMs was positively associated with flood frequency (Spearman ρ ≈ +0.30); GLO-30 and FABDEM were near-equivalent in this low-relief setting (median pairwise difference, 0.13 m). BFAST detected 476,955 changepoints (49.9% post-2020 vs. 35.6% pre-2020), concentrated in high-flood-frequency pixels (Kolmogorov–Smirnov D = 0.854, p < 0.001). The mean flooded area fraction rose from 4.68% to 5.61%, a relative increase of +19.8% (95% CI 9.1–32.0); this remained significant after controlling for precipitation (+0.96 pp, p < 0.001) and excluding the extreme 2023 events (+0.81 pp). Because standard optical and multi-year surface water products are unsuitable for pixel-level validation in this turbid seasonal river, we demonstrate that SAR flood frequency is significantly higher within independently mapped JRC water corridors (median, 0.070 vs. 0.042; p < 0.001). These convergent lines of evidence are consistent with re-encroachment into hazardous floodplains, suggesting that structural relocation alone is unlikely to deliver durable flood risk reduction without parallel investment in tenure security, livelihoods, and inclusive governance (SDGs 11.5, 13.1). The reproducible, open-source SAR framework provides a transferable monitoring template for data-sparse Horn of Africa floodplains. Full article

Orthophoto change detection is used to find real land cover changes in urban monitoring, disaster assessment, and environmental management. In practice, however, multi-temporal orthophotos are rarely identical in geometry and radiometry even after standard preprocessing. Small residual misregistration, local building displacement, shadow movement, and illumination differences can produce edge-like responses that look like change but do not correspond to any land cover transition. These false alarms increase manual checking costs and reduce the reliability of change maps. This study addresses that practical problem by proposing an attention-guided conditional adversarial framework, named Attention-GAN, for false alarm-resistant orthophoto change detection. The aim is not to detect small perturbations as changes but to detect real land cover changes while suppressing responses to nuisance variations that should be treated as unchanged. The framework integrates a multi-scale spatial attention module, a channel attention module, and a PatchGAN discriminator. It also introduces perturbation-negative training pairs, where controlled geometric and radiometric perturbations are applied to unchanged image pairs and assigned all-zero change masks. Experiments on LEVIR-CD, WHU-CD, and DSIFN-CD show competitive or moderately higher accuracy than the selected representative baselines, with F1 scores of 91.2%, 92.45%, and 93.18%, respectively. In the ablation experiment, the false change rate on perturbation-negative validation pairs is reduced to 4.9%. Repeated-run statistics and ablation results indicate that the proposed training strategy mainly improves robustness by reducing false alarms under the evaluated perturbation range. The results support the use of controlled nuisance perturbations as a reproducible way to train and evaluate false alarm resistance, while broader validation under real multi-view, seasonal, and cross-sensor distortions remains necessary. Full article

Recently, deep learning has enabled unmanned aerial vehicles (UAVs) to detect human bodies in aerial imagery, which is of particular importance in post-disaster situations such as floods and storms. Yet progress in this domain remains constrained by a familiar obstacle: the shortage of annotated training data. Neural networks, while powerful, are highly sensitive to data volume and diversity. Existing augmentation strategies help reduce this gap but typically introduce only incremental novelty, especially with respect to viewpoint variation, thereby limiting dataset richness. In this work, we propose a complementary strategy that leverages three-dimensional human models reconstructed via photogrammetric techniques. By situating these models within a controlled rendering environment, we generate synthetic imagery across a broad range of elevations and camera angles—perspectives that are rarely captured in conventional UAV datasets. These additions are designed to increase both the variability and the resilience of the training corpus. To evaluate the contribution of this approach, a custom CNN deep convolutional neural classifier was trained and benchmarked on a UAV human vs. non-human patch dataset of 4000 baseline images (128 × 128 px; 2800 train, 600 validation, 600 test), expanded with 3000 photogrammetry-derived synthetic patches (balanced by class) to 7000 total images for the 3DG setting. The primary metric was classification accuracy on the held-out test set, consistent with patch-level evaluation practice; detection-style metrics such as AP/IoU were not applicable to this binary classification protocol. Averaged over five independent training runs, the proposed augmentation improved classification accuracy by 3.02 percentage points over the baseline (88.06 ± 0.97% → 91.08 ± 1.03%), with consistent gains in precision, recall, and F1-score. When combined with standard augmentations (rotation, translation, scaling, flipping), accuracy reached 95.21 ± 0.61%, a gain of 7.15 percentage points over the baseline. These results suggest that photogrammetry-based augmentation offers a practical and effective enhancement for UAV-based human detection pipelines where timely, reliable identification is critical. Full article

The increasing availability of digital documentation in cultural heritage has amplified the need for interoperable systems capable of integrating heterogeneous data and supporting risk-informed conservation strategies. In the field of Disaster Risk Management (DRM), the application of structured methodologies—such as the ICCROM-CCI ABC Method—is often hindered by fragmented data sources, inconsistent terminology, and limited interoperability across institutions. This study presents a semantic workflow for the harmonization, enrichment, and integration of cultural heritage risk assessment data within a CIDOC Conceptual Reference Model (CIDOC-CRM)-compliant environment. The proposed system is structured as an Extract–Transform–Load (ETL) pipeline that converts heterogeneous assessment records into interoperable semantic knowledge graphs. The workflow combines controlled vocabularies, project-specific thesauri for risk agents and heritage typologies, and formal ontology mapping implemented through the Mapping Memory Manager (3M) and executed with the X3ML engine. The resulting data are deployed within a ResearchSpace environment, enabling semantic querying, cross-dataset exploration, and integration with external knowledge infrastructures. The workflow was applied to a dataset comprising 295 cultural heritage sites in the municipality of Ravenna (Italy). The transformation process generated a CIDOC-CRM-compliant knowledge graph containing 134,611 RDF triples and 18,954 entities, integrating information on cultural assets, risk scenarios, actors, documentary resources, and quantitative risk assessments. Through the adoption of persistent identifiers and semantic mappings, the workflow also supports interoperability with external cultural heritage resources, including ArCo and GeoNames, facilitating the contextualization and enrichment of local risk assessment data. By transforming fragmented assessment records into structured and interoperable knowledge, the proposed workflow contributes to bridging semantic and information gaps in cultural heritage risk management. The study demonstrates the feasibility of integrating risk assessment data within an ontology-based semantic infrastructure and highlights its potential to support data integration, semantic interoperability, knowledge reuse, and future decision-support applications for preventive conservation and territorial risk management. Full article

Coal and gas outburst is a major mine disaster affected by complex coupled factors, bringing obstacles to disaster prevention. To address low accuracy and poor generalization of traditional single-algorithm prediction models, this paper constructs a two-layer Stacking ensemble learning identification model for outburst risk. RF, SVM and AdaBoost serve as base models, and WOA-LightGBM acts as the meta-model. Based on measured data of a Shanxi coal mine, Spearman correlation analysis and RF dimensionality reduction remove redundant features; Borderline-SMOTE balances imbalanced samples with few severe-risk data. Accuracy, macro-precision, recall and F1-score evaluate model performance after parameter optimization. Results show that the proposed Stacking model reaches 0.9770 accuracy, outperforming single machine learning models and other intelligent algorithms. It presents minor index fluctuations with strong stability and correctly identifies all eight practical engineering cases. Combining feature engineering and Stacking learning effectively captures nonlinear relations between influencing factors and risk levels. The model owns high precision and robustness, offering reliable technical support for coal and gas outburst prediction and control. Full article

Landslide susceptibility assessment acts as a core technical tool for geological disaster governance, ecological protection and long-term risk mitigation strategies. This modeling approach quantifies the possibility of slope-collapse events and delivers objective decision-making support for regional geologic environment supervision. To overcome the low computational efficiency and weak capacity of conventional evaluation frameworks to extract multi-level spatial grid rules, this paper takes Nanning City, the capital and largest city of the Guangxi Zhuang Autonomous Region in southern China, as the research object. Ten types of terrain and geological control factors combined with historical landslide inventory records are adopted to build a two-stage coupled evaluation framework integrating the information value method (IVM), a Bidirectional Temporal Convolutional Network (BiTCN) and Transformer, named IVM-BiTCN–Transformer. The hierarchical framework first adopts IVM to finish preliminary hazard grading and calculate factor contribution weights, then inputs classified grid samples into the BiTCN-Transformer module to realize local terrain feature and global factor fusion, which significantly lifts the overall identification precision. Ten widely adopted landslide evaluation algorithms are selected for contrast simulation, with multiple quantitative metrics adopted to judge model reliability. Experimental outcomes prove that the presented IVM-BiTCN–Transformer framework obtains superior hazard discrimination capacity, which can raise the precision and stability of landslide zoning and offer reliable technical support for targeted regional geological disaster prevention. Full article