Search Results (688)

Street-view imagery supports fine-grained urban monitoring, but most street-scene change detection methods are pixel-centric or object-centric and cannot explicitly capture the evolution of inter-entity spatial relations needed for interpretable tasks (e.g., compliance inspection and post-disaster assessment). To address this, we propose SSPRCD, a scene graph-based framework that extracts entity-relation triplets with pixel locations, builds spatial knowledge graphs, and achieves stable node alignment via intra-/inter-temporal consistency. Graph differencing then identifies added, removed, and unchanged entities/relations, while nGED and graph2vec jointly quantify structural discrepancies between temporal scenes. Experiments on the TSUNAMI dataset, with comparisons across two object detectors and seven scene graph generation backbones, show that SSPRCD achieves a macro-F1 of 0.65 for the object-level task, F1 of 0.72 for binary change detection, and F1 of 0.89 for relation-level detection, consistently outperforming baseline methods. Overall, SSPRCD delivers relation-aware and topology-informed change explanations that improve the interpretability of street-block level change analysis for geospatial in-formation updating and urban applications. Full article

During the operation of nuclear power plants, nuclear fuel undergoes significant compositional changes. After several cycles of use, the fuel must be removed and stored. Currently, spent fuel is stored mainly in pools or casks, and it is necessary to demonstrate the subcriticality of these systems. Spent nuclear fuel has a complex composition, and because computational codes are typically validated using fresh-fuel experiments, subcriticality assessments are usually performed conservatively with fresh-fuel compositions. These approaches demonstrate subcriticality but are very conservative and can lead to storage system designs that are more expensive or have reduced capacity. This paper focuses on the validation of computational codes using nuclear power plant critical start-up tests (referred to as reactor criticals). These tests include spent fuel and are well documented, allowing them to serve as validation experiments. Codes validated using reactor criticals can be applied to systems containing spent fuel calculation if sufficient similarity is demonstrated. Similarity is evaluated using the SCALE TSUNAMI-IP module, which is widely used for this purpose. Based on a database containing dozens of reactor criticals and similarity analyses, we developed a methodology for demonstrating the subcriticality of spent-fuel storage systems. Full article

This study presents a comprehensive investigation of exact fractional wave solutions and bifurcation analysis for the (3 + 1)-dimensional extended shallow water wave (3D-eSWW) equation with $\beta$-derivative, which models nonlinear wave phenomena in fluid dynamics and coastal engineering. Leveraging the flexibility of the fractional derivative, the model provides a more generalized and adaptable framework for describing shallow water wave propagation. The Modified Extended Direct Algebraic Method (MEDAM) is systematically employed to derive a broad spectrum of novel exact analytical solutions. These include the following: dark solitary waves, singular solitons, singular periodic waves, periodic solutions expressed via trigonometric and Jacobi elliptic functions, polynomial solutions, hyperbolic wave patterns, combined dark–singular structures, combined hyperbolic–linear waves, and exponential-type wave profiles. Each solution family is presented with explicit parameter constraints that ensure both mathematical consistency and physical relevance, thereby offering a robust classification of wave regimes under diverse conditions. A thorough bifurcation analysis is conducted on the reduced dynamical system to examine parametric dependence and stability transitions. Critical bifurcation thresholds are identified, and distinct solution branches are mapped in the parameter space spanned by wave numbers, nonlinear coefficients, external forcing, and the fractional order $\beta$. The analysis reveals how solution dynamics undergo qualitative transitions—such as the emergence of solitary waves from periodic patterns or the appearance of singular structures—driven by the interplay of nonlinearity, dispersion, and fractional-order effects. These insights are crucial for understanding wave stability, predictability, and the onset of extreme events in shallow water contexts. Graphical representations of selected solutions validate the analytical results and illustrate the influence of $\beta$ on wave morphology, propagation, and stability. The simulations demonstrate that varying the fractional order can significantly alter wave profiles, highlighting the role of fractional calculus in capturing complex real-world behaviors. This work demonstrates the efficacy of the MEDAM technique in handling high-dimensional fractional nonlinear PDEs and provides a systematic framework for predicting and classifying wave regimes in real-world shallow water environments. The findings not only enrich the solution inventory of the 3D-eSWW equation but also advance the analytical toolkit for studying complex spatio-temporal dynamics in fractional mathematical physics and fluid mechanics. Ultimately, this research contributes to the development of more accurate models for coastal protection, tsunami forecasting, and marine engineering applications. Full article

Global Navigation Satellite Systems (GNSS), benefiting from global coverage, all-weather operation, high precision, and high temporal resolution, have progressively become a key technology in natural hazard monitoring and early warning systems. This paper adopts a hybrid review strategy that integrates scientometric analysis with a systematic review to examine the development trajectory, research hotspots, and technological evolution of GNSS applications in natural hazard studies based on the existing literature. From a technological perspective, three core capabilities of GNSS in hazard monitoring are identified: high-precision, multi-scale deformation sensing; multi-sphere environmental sensing based on signals of opportunity; and real-time monitoring supporting rapid early warning and emergency response. The paper further reviews the development of GNSS in conjunction with multi-sensor collaborative observation and its integration with data-driven methods such as machine learning. Representative applications of GNSS and its integrated techniques are summarized across major hazard types, including earthquakes, tsunamis, landslides, land subsidence, hydrometeorological hazards, and volcanic activity, and further discussions are provided on methodological considerations, the commonalities and differences in GNSS applications across different hazards, and future development directions. The review demonstrates that GNSS applications in natural hazard research are evolving from single-source deformation monitoring toward multi-source integration, intelligent sensing, and operational early warning support systems. This work provides a reference for the further development of GNSS technologies in natural hazard monitoring and risk mitigation. Full article

Effective interpretation of coherent and usable information from aerial images (e.g., satellite imagery or high-altitude drone photography) can greatly reduce human effort in many situations, both natural (e.g., earthquakes, forest fires, tsunamis) and man-made (e.g., highway pile-ups, traffic congestion), particularly in disaster management. This research proposes a novel encoder–decoder framework for captioning of remote sensing images that integrates self-supervised DINOv3 visual features with a hybrid Transformer–LSTM decoder. Unlike existing approaches that rely on supervised CNN-based encoders (e.g., ResNet, VGG), the proposed method leverages DINOv3’s self-supervised learning capabilities to extract dense, semantically rich features from aerial images without requiring domain-specific labeled pretraining. The proposed hybrid decoder combines Transformer layers for global context modeling with LSTM layers for sequential caption generation, producing coherent and context-aware descriptions. Feature extraction is performed using the DINOv3 model, which employs the gram-anchoring technique to stabilize dense feature maps. Captions are generated through a hybrid of Transformer with Long Short-Term Memory (LSTM) layers, which adds contextual meaning to captions through sequential hidden layer modeling with gated memory. The model is first evaluated on two traditional remote sensing image captioning datasets: RSICD and UCM-Captions. Multiple evaluation metrics like Bilingual Evaluation Understudy (BLEU), Consensus-based Image Description Evaluation (CIDEr), Recall-Oriented Understudy for Gisting Evaluation (ROUGE-L), and Metric for Evaluation of Translation with Explicit Ordering (METEOR), are used to quantify the performance and robustness of the proposed DINOv3 hybrid model. The proposed model outperforms conventional Convolutional Neural Network (CNN) and Vision Transformers (ViT)-based models by approximately 9–12% across most evaluation metrics. Attention heatmaps are also employed to qualitatively validate the proposed model when identifying and describing key spatial elements. In addition, the proposed model is evaluated on advanced remote sensing datasets, including RSITMD, DisasterM3, and GeoChat. The results demonstrate that self-supervised vision transformers are robust encoders for multi-modal understanding in remote sensing image analysis and captioning. Full article

Landslides occurring on reservoir banks in steep, high-gradient canyon areas pose a significant risk of surge disasters when they slide into the water. This can endanger the lives and property of downstream residents and damage coastal infrastructure. Therefore, researching the formation mechanisms, disaster evolution, and risk assessment of the landslide-surge disaster chain in such areas is essential. This paper takes the Laowuchang landslide in the Shuibuya Reservoir area of the Qingjiang River, China, as its research object. Using GeoStudio 2018 software, it evaluates the landslide’s stability under varying reservoir water levels and rainfall conditions. For potential unstable scenarios identified, a full-chain numerical simulation of the landslide–tsunami disaster was conducted based on the Tsunami Squares method, with a focus on analyzing the wave characteristics during generation, propagation, and run-up processes. Furthermore, the paper assesses the risk of landslide–tsunami disasters in the Laowuchang landslide area. The research findings indicate that: (1) Under the long-term continuous river incision, limestone of the Triassic Daye Formation slides along weak interlayers, inducing large-scale collapses. Subsequently, part of the landslide mass is transported by water, while most accumulates in the near-shore area of the Qingjiang River, ultimately shaping the present morphology of the landslide. (2) The Laowuchang landslide is stable under static water levels of 375 m and 400 m, with corresponding safety factors of 1.137 and 1.167, respectively. Under combined static water level and heavy rainfall conditions, the slope stability decreases significantly, with safety factors of 1.034 and 1.064, respectively. Under reservoir drawdown conditions, the slope tends to be unstable, with a safety factor of 1.047. (3) Numerical simulation results indicate that if the Laowuchang landslide fails into water by the speed of 12 m/s and with a volume of 2 million m³, the maximum initial wave height can reach 15.9 m. The tsunami’s affected range spans 10 km upstream and downstream from the landslide mass, with four houses and one substation within a 2 km up and downstream falling into high-risk areas. If abnormal increases in landslide displacement occur, relocation and risk avoidance measures should be implemented. The findings of this study provide a scientific basis for the prevention and response to landslide–tsunami disasters in similar high and steep canyon terrains. Full article

Living in the shadow of ‘Iddu’, the Stromboli volcano, requires a unique cultural adaptation. This study explores the risk perception of the permanent residents of Stromboli Island (Italy), a complex multi-hazard environment where persistent volcanic activity coexists with tsunami threats. Adopting a qualitative design based on 17 semi-structured interviews and focus groups (May 2024), we analysed residents’ narratives through the Cultural Theory of Risk. The findings reveal a hybrid risk culture: a dominant individualistic orientation (37%), driven by self-reliance, is balanced by a strong egalitarian ethos (33%) rooted in community solidarity. The analysis highlights three critical dynamics: (1) the normalization of volcanic risk versus the fear of rare tsunami events; (2) a ‘Trust Gap’ between the community’s horizontal preparedness strategies and the institutions’ vertical communication protocols; and (3) an ‘Economic Filter’ imposed by tourism, which creates a cognitive dissonance where risk is privately acknowledged but publicly downplayed. The study concludes that effective Disaster Risk Reduction (DRR) cannot rely solely on top-down technology but must integrate local knowledge and participatory approaches to bridge the distance between scientific monitoring and community experience. Full article

The Licantén coastal area in central Chile was severely impacted by the 2010 Mw 8.8 Cobquecura earthquake and subsequent tsunami, exposing the high vulnerability of coastal communities. Over the past decade, urban expansion has advanced toward the shoreline, increasing exposure to coastal hazards. This study aims to quantify shoreline dynamics and urban growth in Licantén between 2010 and 2025. We integrated satellite-derived shorelines (SDSs) from Landsat and Sentinel-2 imagery, ERA5 ocean reanalysis to characterize extreme wave events, and an open-source building footprint dataset with high-resolution imagery for urban mapping. Results indicate a post-earthquake acceleration in shoreline erosion up to 5 m per year and a rise in extreme wave events linked to climate variability. Urbanized areas expanded by an average of 46.3%, intensifying risk in hazard-prone zones. These findings highlight the urgent need for evidence-based coastal planning, including zoning and land-use restrictions, to reduce exposure and enhance resilience. This research contributes to climate adaptation strategies and sustainable coastal management in Chile. Full article

Coastal societies face increasing health risks from climate change, such as weather-related extreme conditions, environmental destruction, and the occurrence of epidemics, posing significant challenges to sustainable development. There is a need to accurately measure the risks in place through integrating the climate variability with socio-economic exposure and health components to support long-term resilience and sustainable adaptation. This study conceptualized and validated a composite index-based method to assess climate–health risks across three Indonesian coastal cities: Banda Aceh, Mataram, and Ambon. This validation process was conducted by checking for face validity and consistency between sub-indices, as well as conformity to existing frameworks in the literature. Using satellite-derived climate data, national socio-economic statistics, and public health records, we identified the key parameters (hazard, sensitivity, exposure, and adaptive capacity) and quantified the risk levels for 190 villages. The results show that over 92% of villages fall into the high or very high risk categories, with universal high sensitivity and low adaptive capacity (78.95%). This points towards structural inequalities that hinder sustainable development. Spatial and quadrant analyses revealed region-specific vulnerabilities where Ambon showed higher hazard exposure (56% high and 42% very high). The findings provide policymakers and stakeholders with priority areas for targeted interventions and actionable suggestions to support public health planning, equitable resource allocation, and long-term sustainable coastal development. Full article

In this work, we examine the prospects of matching the Kadomtsev–Petviashvili (pKP) equation with the B-type Kadomtsev–Petviashvili (BKP) equation, which we will call the pKP-BKP equation. The resulting model gives a rigorous mathematical framework for describing long wave phenomena in oceans, impoundments and estuaries and for forecasting tsunamis; river, tide and irrigation flows; and wave patterns in the atmosphere. Using a consolidated method of analysis based on symmetry reductions and rational function transformations, we obtain several classes of exact solutions composed of rational, periodic, breather and kink-wave structures. These methods shed light on the interplay between symmetries that control the formation of soliton solutions, hence allowing the construction of new families of analytical soliton solutions. The solutions obtained are linked together through spectral degeneracies and reductions in symmetry. These methodologies are presented in a systematic way, emphasizing their applicability to a general class of nonlinear evolution equations. The results of the analysis are substantiated through direct substitution, and the structural characteristics of the solutions are discussed in detail. As a result, these results expand the solution space of the pKP–BKP equation and provide better analytical insights into Kadomtsev–Petviashvili-type nonlinear evolution equations. Full article

Tsunami-induced scour around coastal embankments and nearshore structures is a primary cause of structural instability and failure. However, the hydrodynamic mechanisms by which coastal vegetation mitigates this scour remain insufficiently understood. This study employs three-dimensional numerical simulations to investigate the influence of rigid and flexible vegetation on overflow-induced scour downstream of embankments and local scour around structures under tsunami-like inundation. The simulations were conducted using Ansys Fluent 2021R2, utilizing the Volume of Fluid (VOF) method to capture the free surface and the RNG k − ε turbulence model within the Reynolds-averaged Navier–Stokes (RANS) framework. Computational geometries were reconstructed from laboratory experiments, and the model’s reliability was validated against measured water surface profiles. The results demonstrated that vegetation significantly alters flow dynamics, velocity distributions, vortex structures, and both the magnitude and patterns of bed shear stress within scour holes. Specifically, in overflow-induced scour, vegetation suppresses scour intensity by inducing backwater effects, enhancing momentum diffusion, attenuating flow impingement on the bed, and reducing peak bed shear stress. Conversely, for local scour around structures, vegetation increases upstream water depth while intensifying downstream wake vortices, leading to scour hole elongation—particularly under dense and tall vegetation. These findings offer novel insights into the hydrodynamics of vegetation-induced scour mitigation and provide guidelines for optimizing vegetation configurations to enhance the tsunami resilience of coastal infrastructure. Full article

The Seulawah Agam volcano, located in Aceh, hosts one of Indonesia’s largest unexploited geothermal resources that is included in the Indonesian Green Energy Program. Previous studies of the Seulawah geothermal system (SGS) have used partial data and methods without developing a comprehensive conceptual model of the reservoir and its fluid flow and heat transfer patterns. This study aims to characterize the groundwater flow and heat transfer patterns of the SGS through numerical modeling based on integrated geological, geophysical, and geochemical data. Numerical modeling was conducted along two representative transects: Ie Seum, Ie Jue, and Kawah van Heutsz manifestations. MODFLOW 6 was used to model groundwater flow and heat transfer using a new conceptual model derived from magnetotelluric data, chemical composition and physical properties of the fluid, isotopic data, and mineragraphic data. The low resistivity anomalies are closely related to fluid discharges beneath the Ie Seum and Ie Jue areas. The depth of the Ie Seum reservoir is around 1.0–2.5 km, with estimated temperatures of 120–242 °C, while the depth of the Ie Jue and Kawah van Heutsz reservoirs is between 0.8 and 2.5 km, with estimated temperatures of 150–316 °C. The modeling suggests that the Ie Seum and the Ie Jue–Kawah van Heutsz systems represent regional groundwater and intermediate-local flow regimes, respectively. It is suggested that drilling be conducted around the local Ie Jue hydrothermal system, which is more economical given the shallower reservoir and higher temperature. Full article

Tsunami hazards pose persistent threats to low-lying coastal settlements in Indonesia, where physical exposure and social vulnerability often intersect. This study integrates tsunami inundation modelling using the Cornell Multi-grid Coupled Tsunami (COMCOT) model with a community preparedness assessment to develop a comprehensive understanding of tsunami risk in Tanjung Benoa, Bali, Indonesia. The COMCOT simulation, based on a potential Mw 8.5 earthquake scenario south of Bali, indicates a maximum inundation depth of up to 14 m, where the tsunami waves are projected to traverse the Tanjung Benoa peninsula, with the first tsunami arrival being expected within 24 min after rupture. A social survey involving 327 household heads across six neighborhoods was conducted using the Tsunami Ready Community framework (UNESCO–IOC) to evaluate awareness, preparedness, and response capacities. The overall Preparedness Index (PI) reached 78, categorized as “Ready”, indicating moderate readiness but uneven distribution across neighborhoods. This integrated approach highlights that physical modelling alone is insufficient to capture real tsunami risk without incorporating social preparedness dimensions. The study provides actionable insights for local disaster management authorities and supports the strengthening of the UNESCO–IOC Tsunami Ready Community indicators in Tanjung Benoa. The framework demonstrated here can serve as a replicable model for other coastal communities pursuing sustainable and data-driven tsunami resilience strategies. Full article

Maritime surveillance is critical for ensuring the safety and continuity of sea logistics, port operations, and coastal activities in the presence of anomalies such as unlawful maritime activities, security-related incidents, and anomalous events (e.g., tsunamis or aggressive marine wildlife). Recent advances in unmanned aerial vehicles (UAVs)/drones and computer vision enable automated, wide-area monitoring that can reduce dependence on continuous human observation and mitigate the limitations of traditional methods in complex maritime environments (e.g., waves, ship clutter, and marine animal movement). This study proposes a hybrid anomaly detection and tracking pipeline that integrates YOLOv12, as the primary object detector, with two auxiliary modules: (i) motion assistance for tracking moving anomalies and (ii) stillness (appearance) assistance for tracking slow-moving or stationary anomalies. The system is trained and evaluated on a custom maritime dataset captured using a DJI Mini 2 drone operating around a port area near Bayshore MRT Station (TE29), Singapore. Windsurfers are used as proxy (dummy) anomalies because real anomaly footage is restricted for security reasons. On the held-out test set, the trained model achieves over 90% on Precision, Recall, and mAP50 across all classes. When deployed on real maritime video sequences, the pipeline attains a mean Precision of 92.89% (SD 13.31), a mean Recall of 90.44% (SD 15.24), and a mean Accuracy of 98.50% (SD 2.00%), indicating strong potential for real-world maritime anomaly detection. This proof of concept provides a basis for future deployment and retraining on genuine anomaly footage obtained from relevant authorities to further enhance operational readiness for maritime and coastal security. Full article