Search Results (368)

Storm surge dynamics in coastal zones and estuaries are complex, driven by coupled oceanic and terrestrial interactions that enhance the risk of coastal disasters. This study investigates storm surge characteristics and mechanisms in the Macao Cross Tidal Channel (MCTC), located in the Macao Sea Area (MSA). A tide-surge coupled numerical model was established using the unstructured grid Finite Volume Community Ocean Model (FVCOM). The model was rigorously validated against tide gauge data from Typhoon Hato, demonstrating strong performance, with a skill score of 0.95 and a correlation coefficient exceeding 0.94. The spatiotemporal characteristics and mechanisms of storm surge dynamics in the MCTC were elucidated. The results show that the MCTC’s complex geometry induces a geometric funneling effect, which substantially amplifies the storm surge compared with adjacent locations in the estuary and open sea. During the typhoon period, coastal geomorphology affects winds, tide levels, currents, and waves, which in turn spatially and temporally modulate the storm surge. Wind is the primary driver, but its effect is modulated by nonlinear interactions with waves, including enhanced bottom friction and wave set-down. In isolation, the wind-induced component contributed approximately 106% of the peak total surge. This overestimation quantitatively highlights the critical role of nonlinear interactions, where wave-enhanced bottom friction acts as a major energy sink, and wave set-down directly suppresses the water level at the channel entrance. The individual peak contributions from atmospheric pressure and wave were approximately 5% and 17%, respectively, but these peaks occurred out of phase with the storm surge. Energy transformation analysis based on the Bernoulli principle revealed a distinct conversion from potential to kinetic energy in the constricted transverse waterway, while the longitudinal waterway exhibited a more gradual energy change. These findings enhance the mechanistic understanding of storm surges in complex, constricted estuaries and can inform targeted strategies for coastal hazard mitigation in the Macao region. Full article

This study examines how El Sistema, a global social music education program active in over 60 countries, contributes to the United Nations Sustainable Development Goals (SDGs). The program is analyzed using Sheerens’ CIPO (Context–Input–Process–Output) framework, which enables comprehensive evaluation of education from design and implementation to outcomes beyond simple result-focused assessment. The framework also accounts for political, social, and economic contexts, making it effective for understanding country-specific cases. Using this approach, this study analyzed how El Sistema has been localized across five representative case countries—Scotland, the United States, Sweden, Republic of Korea, and Japan. Data were collected through a narrative review integrating academic studies and credible non-academic sources, including government and organizational reports, program brochures, official websites, news articles, and multimedia materials. Findings indicate alignment with SDGs 1, 3, 4, 5, 8, 10, 11, 16, and 17. Country-specific strategies include community engagement in Scotland (SDG 17), partnerships in the United States (SDG 17), immigrant integration in Sweden (SDGs 11, 16), inclusive music education and cultural revitalization in the Republic of Korea (SDGs 11, 16, 17), and quality and healing education for disaster-affected and disabled children in Japan (SDG 11). These results suggest that El Sistema can serve as a sustainable model of social music education when supported by localized strategies, stable resources, multi-level governance, systematic evaluation, and proactive engagement with environmental initiatives. Full article

In the early months of 2025, a significant seismic activity was recorded in the area between Santorini and Amorgos, raising concerns about the potential occurrence of a major earthquake and a possible tsunami. The objective of this study is to assess the earthquake-triggered tsunami hazard in the Santorini-Amorgos Tectonic Zone (SATZ) by simulating tsunami processes using the MOST (Method of Splitting Tsunami) numerical model, implemented through the ComMIT (Community Model Interface for Tsunamis). High-resolution bathymetry and topography were employed to model tsunami generation, propagation, and onshore inundation. A total of 60 simulations were conducted using a deterministic approach based on worst-case scenarios. The analysis considered six major active faults with two kinematic types, pure normal and oblique-slip, and assessed tsunami impact on five selected coastal study areas. The simulations results showed potential maximum run-up values of 4.1 m in Gialos (Ios), 2.7 m in Kamari (Santorini), 2.4 m in Perissa (Santorini), 1.5 m in Katapola (Amorgos), and 2.3 m in Chora (Astypalaea), in some cases affecting residential zones. Inundation flows also impacted the main ports of Gialos, Katapola, and Chora, highlighting the exposure of critical infrastructure. Although earthquake-triggered tsunamis represent a potential hazard in the SATZ, the results indicated that it is unlikely to cause a widespread disaster in the study areas. Full article

Recently, disaster damages have become more widespread due to climate change and the interaction between disasters, and the complexity of solving this problem is increasing. Consequently, many buildings have been severely affected, with some houses being razed or flooded, losing their residential function. The damage to housing facilities not only destroys the life-cycles of individuals and households but also causes functional loss and productivity decrease in local communities. As a countermeasure, the central and local governments provide their citizens with housing facilities, such as temporary housing, to make their lives stable. This study conducted interviews with disaster victims who experienced housing damage from various natural disasters, from the victims of the earthquake in Pohang in 2017 to the landslide in Yecheon and Bonghwa in 2023, and victims who lived in temporary housing between 2017 and 2024 immediately after they suffered such disasters. It then investigated the housing facilities themselves. The study conducted in-depth interviews through one-on-one meetings with the disaster victims directly and their satisfaction levels with their temporary housing facilities were investigated. This study also explored certain issues to be improved on and inconveniences in housing through the statements and experiences of the disaster victims. Based on the interviews, the study identified and gathered the actual problems in and of the housing facilities. Furthermore, based on the results of these investigations, this study developed modular temporary housing units which reflect the various needs and demands of different households. This study contributes to the stability in the living situations of disaster victims. It increases the disaster resilience of the local communities. Full article

The increasing frequency and severity of natural disasters—such as floods, storms, droughts, and earthquakes—have created a growing demand for temporary housing. These facilities must be rapidly deployed to provide safe, functional living environments for displaced individuals. This study proposes a design methodology for temporary housing exteriors using the text-to-image capabilities of generative artificial intelligence (GenAI) to address urgent post-disaster housing needs. The approach aims to improve both the efficiency and practicality of early-stage design processes. The study reviews global trends in temporary housing and the architectural applications of GenAI, identifying five key environmental factors that influence design: type of disaster, location and climate, duration of residence, materials and structure, and housing design. Based on these factors, hypothetical disaster scenarios were developed using ChatGPT, and corresponding exterior designs were generated using Stable Diffusion. The results show that diverse, scenario-specific design alternatives can be effectively produced using GenAI, demonstrating its potential as a valuable tool in architectural planning for disaster response. Expert evaluation of the generated designs confirmed their ability to adhere to text prompts but revealed a significant gap in terms of architectural plausibility and practical feasibility, highlighting the essential role of expert oversight. This study offers a foundation for expanding GenAI applications in emergency housing systems and supports the development of faster, more adaptable design solutions for communities affected by natural disasters. Full article

This study reviews the role of Child-Friendly Green Spaces (CFGS) in supporting children’s psychological, physical, and educational recovery following natural disasters. The main research question guiding this review is the following: how do CFGS contribute to holistic child well-being and resilience in disaster-affected contexts, and what barriers and strategies influence their effective integration into recovery frameworks? Employing a rigorous literature review methodology, we synthesized interdisciplinary evidence from environmental psychology, urban planning, public health, and education, encompassing studies published between 2000 and 2024. Findings demonstrate that CFGS significantly reduce trauma-related symptoms such as anxiety, depression, and post-traumatic stress, promotes physical health through active play, and foster educational engagement by improving concentration, attendance, and informal learning opportunities. Furthermore, CFGS contribute directly to multiple Sustainable Development Goals, particularly SDG 3 (Good Health and Well-being), SDG 4 (Quality Education), and SDG 11 (Sustainable Cities and Communities). Despite these advantages, CFGS are often overlooked in formal disaster recovery planning due to prioritization of immediate relief, financial and logistical challenges, and socio-cultural factors. To address these challenges, this study proposes a participatory, culturally sensitive framework for CFGS implementation, which integrates inclusive design, multi-sector collaboration, and ongoing monitoring and evaluation. Grounded in theoretical perspectives such as the Biophilia Hypothesis, Bronfenbrenner’s Ecological Systems Theory, and restorative environments, CFGS are reframed as critical infrastructures for children’s holistic recovery and resilience. The findings underscore the urgent need to embed CFGS within disaster recovery and urban planning policies to promote child-centered, sustainable community development. Full article

The Doce River basin is the largest river system in southeastern Brazil. Over the last century, the Doce River has been undergoing a serious process of degradation, culminating in a huge environmental disaster due to Fundão tailing dam bursting in Mariana (Minas Gerais) and causing severe damage to biodiversity and local human communities. Near its mouth, the Doce River harbors an extensive lake area, with over ninety lakes on coastal lowlands. These lakes are of fluvial origin and connected to each other and to the main Doce River by small tributary streams. In this area, one of the main sources of impact on the fish fauna is the presence of non-native fish species. We compared richness, taxonomic diversity, beta diversity, species composition and proportion of non-native species in lakes and streams, and related these variables to each other and to environmental variables. We used the indicator species index (IndVal) to identify species associated with each type of environment. We used multivariate analyses to test the influence of stream habitat on the fish fauna in streams and Generalized Linear Models (GLMs) to test the influence of distance to lakes on the proportion of non-native species in streams, and the influence of this proportion on total and native fish richness and diversity. The results showed that some non-native species originating from lentic environments have adapted to the lakes and are spread throughout the internal lake system. In streams, there are proportionally fewer non-native fish and their distribution is more fragmented, as some stretches do not provide the conditions for the establishment of some of these species, making them potential refuges for native ichthyofauna. As the streams move away from the lakes, the proportion of non-native species tends to decrease. In streams, the richness and diversity of native species are affected by the proportion of non-native species, but not in lakes. The native vegetation in the landscape showed no potential for reducing the invasion of non-native species. The depth and width of the streams are directly related to the proportion of non-native species within the streams and are structural characteristics that should be considered in strategies for the conservation of the fish fauna. Full article

Landslides pose a significant threat to Taiwan’s mountainous regions, particularly after extreme weather events such as typhoons. This study introduces a machine learning framework for post-disaster land use-land cover (LULC) classification and landslide detection in Nanfeng Village, central Taiwan, following Typhoon Khanun in August 2023. Using high-resolution Pléiades imagery and 22 environmental and spectral factors, a Random Forest classifier was developed. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was systematically evaluated across multiple variants. The Distance_SMOTE method yielded the best results, increasing overall accuracy from 74% to 85% and the Kappa coefficient from 0.69 to 0.82. F1-scores for landslides, roads, and grassland improved markedly, reaching 0.97, 0.85, and 0.78, respectively. The optimized model produced accurate pre- and post-typhoon LULC maps, revealing significant expansion of landslide zones after the event. This study demonstrates the practical value of combining SMOTE-based resampling with Random Forest for rapid, reliable post-disaster assessment, offering actionable insights for disaster response and land management in data-imbalanced conditions. By enabling timely mapping of hazard-affected areas and informing targeted recovery actions, the approach supports disaster risk reduction, sustainable land use planning, and ecosystem restoration. These outcomes contribute to the Sustainable Development Goals, particularly SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 15 (Life on Land), by strengthening community resilience, promoting climate adaptation, and protecting terrestrial ecosystems in hazard-prone regions. Full article

Mount Etna has a well-documented history of frequent eruptions and seismic activity, periodically causing significant damage to urban areas. On 26 December 2018, a Mw 4.9 shallow earthquake struck the volcano’s eastern flank, severely damaging approximately 3000 buildings. The post-earthquake recovery strategy aimed to enhance community resilience by addressing the hazardous nature of the affected territory. This objective was achieved through measures such as relocation and public use transformation. In areas impacted by active faults, the relocation of damaged buildings was encouraged, while cleared zones were repurposed for public use, transformed into gardens and open-air parking spaces. Despite these efforts, some relocated individuals experienced psychological distress. To address this challenge, government planners played a pivotal role in disseminating scientifically accurate information, raising public awareness, and facilitating adaptation. The approach implemented on Etna was later adopted in other post-earthquake recovery programs in Italy, evolving into a replicable strategy for risk mitigation in disaster-prone areas. Full article

Multi-energy systems (MES), or energy hubs, offer a technologically viable solution for maintaining resilient energy infrastructure in the face of increasingly frequent disasters, which disproportionately affect low-income and disadvantaged communities; however, their adoption for these purposes remains poorly understood. Following PRISMA 2020, this paper systematically reviews the MES literature from both resiliency and equity perspectives to identify synergies, disparities, and gaps in the context of climate change and long-term decarbonization goals. From 2420 records identified from Scopus (1997–2023), we included 211 original MES research publications for detailed review, with studies excluded based on their scale, scope, or technology. Risk of bias was minimized through dual-stage screening and statistical analysis across 18 physical system and research approach categories. The results found that papers including equity are statically more likely to involve fully renewable energy systems, while middle income countries tend to adopt renewable systems with biofuels more than high income countries. Sector coupling with two energy types improved the resiliency index the most (73% difference between baseline and proposed MES), suggesting two-type systems are optimal. Statistically significant differences in modeling formulations also emerged, such as equity-focused MES studies adopting deterministic design models, while resilience-focused studies favored stochastic control formulations and load-shedding objectives. While preliminary studies indicate low operational costs and high resilience can synergistically be achieved, further MES case studies are needed with low-income communities and extreme climates. Broadly, this review novelly applies structured statistical analysis for the MES domain, revealing key trends in technology adoption, modeling approaches, and equity-resilience integration. Full article

Frequent grassland fires have severely affected regional ecosystems as well as the production and living conditions of local residents. Grassland fire prevention capabilities constitute an integral part of the disaster prevention and mitigation system and play an important role in improving grassroots governance. To gain a deeper understanding of the practical foundation and influencing mechanisms of grassland fire prevention capabilities, establish an evaluation index system for prevention capabilities covering the four dimensions of disaster prevention, disaster resistance, disaster relief, and recovery. Combining micro-level survey data, a quantile regression model is used to analyze the influencing factors. The research findings indicate that (1) disaster resistance (0.49) plays a prominent role in grassland fire prevention capabilities, with economic foundations and individual disaster relief capabilities being particularly critical for overall improvement. Although residents have strong fire prevention awareness, their organizational collaboration capabilities are relatively weak, and there are significant differences in prevention capabilities across regions, necessitating tailored, precise enhancements. (2) There are significant differences in prevention capabilities among residents of different agricultural and pastoral production types, with semi-agricultural and semi-pastoral areas having the strongest comprehensive capabilities and pastoral areas relatively weaker. (3) A significant analysis of factors influencing grassland fire prevention capabilities: effective and diverse risk communication is a prerequisite for enhancing residents’ prevention capabilities; the level of panic regarding grassland fires and road infrastructure are important influencing factors, but residents’ understanding of climate change and grassroots organizations’ capacity for mechanism construction have insignificant impacts. Therefore, in future grassland fire disaster prevention and mitigation efforts, it is essential to strengthen risk communication, improve infrastructure, monitor environmental changes and the spatiotemporal patterns of grassland fires, enhance residents’ understanding of climate change, reinforce the emergency response capabilities of grassroots organizations, and stimulate public participation awareness to collectively build a multi-tiered grassland fire prevention system. Full article

Mission-critical services (MCX) comprise a standardized suite of capabilities including Mission-Critical Push-to-Talk (MCPTT), MCVideo, and MCData, designed to meet stringent requirements for availability, reliability, latency, security, and Quality of Service (QoS). These services are essential for public safety, emergency response, and other critical infrastructure domains, where communication performance directly affects operational effectiveness. Integration into 4G and 5G mobile networks, supported by targeted standardization efforts, has extended broadband capabilities to mission-critical environments. 5G networks provide the technical foundations for MCX through ultra-low latency (below 1 ms), high availability (99.999%), broadband throughput over 100 Mbps per user, deterministic QoS via network slicing, massive device connectivity (over one million devices per square kilometer), and seamless Non-Terrestrial Network (NTN) integration. Technical enablers such as Proximity Services (ProSe), network slicing, and Ultra-Reliable Low-Latency Communications (URLLC) are fundamental to delivering these capabilities. This paper reviews MCX architectures, service frameworks, and protocols, relating MCPTT, MCData, and MCVideo to the key performance requirements defined in ITU-T M.2377-2. It also examines the frozen features of 3GPP Release 19, including enhancements to MC services, NTN integration, Reduced Capability device support, sub-meter positioning, extended network slicing for Public Protection and Disaster Relief (PPDR), and strengthened security mechanisms. Finally, the study addresses challenges such as standard maturity, interoperability, and deterministic QoS, identifying research priorities toward 6G readiness. By consolidating advances from standards bodies, research initiatives, and deployments, this work serves as a technical reference for scalable, secure, and standards-compliant MCX solutions in current and future networks. Full article

Landslides pose persistent threats to mountainous regions in Taiwan, particularly in areas such as Nanfeng Village, Nantou County, where steep terrain and concentrated rainfall contribute to chronic slope instability. This study investigates spatiotemporal patterns of vegetation change as a proxy for identifying potential landslide-prone zones, with a focus on the Tung-An tribal settlement in the eastern part of the village. Using high-resolution satellite imagery from SPOT 6/7 (2013–2023) and Pléiades (2019–2023), we derived annual NDVI layers to monitor vegetation dynamics across the landscape. Long-term vegetation trends were evaluated using the Mann–Kendall test, while spatiotemporal clustering was assessed through Emerging Hot Spot Analysis (EHSA) based on the Getis-Ord Gi* statistic within a space-time cube framework. The results revealed statistically significant NDVI increases in many valley-bottom and mid-slope regions, particularly where natural regeneration or reduced disturbance occurred. However, other valley-bottom zones—especially those affected by recurring debris flows—still exhibited declining or persistently low vegetation. In contrast, persistent low or declining NDVI values were observed along steep slopes and debris-flow-prone channels, such as the Nanshan and Mei Creeks. These zones consistently overlapped with known landslide paths and cold spot clusters, confirming their ecological vulnerability and geomorphic risk. This study demonstrates that integrating NDVI trend analysis with spatiotemporal hot spot classification provides a robust, scalable approach for identifying slope hazard areas in data-scarce mountainous regions. The methodology offers practical insights for ecological monitoring, early warning systems, and disaster risk management in Taiwan and other typhoon-affected environments. By highlighting specific locations where vegetation decline aligns with landslide risk, the findings can guide local authorities in prioritizing slope stabilization, habitat conservation, and land-use planning. Such targeted actions support the Sustainable Development Goals, particularly SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 15 (Life on Land), by reducing disaster risk, enhancing community resilience, and promoting the long-term sustainability of mountain ecosystems. Full article

Natural disasters like wildfires pose significant threats to communities, which necessitates timely and effective disaster response strategies. While Aspect-based Sentiment Analysis (ABSA) has been widely used to extract sentiment-related information at the sub-sentence level, the corresponding field of Aspect-based Emotion Analysis (ABEA) remains underexplored due to dataset limitations and the increased complexity of emotion classification. In this study, we used EmoGRACE, a fine-tuned BERT-based model for ABEA, which we applied to georeferenced tweets of the 2020 California wildfires. The results for this case study reveal distinct spatio-temporal emotion patterns for wildfire-related aspect terms, with fear and sadness increasing near wildfire perimeters. This study demonstrates the feasibility of tracking emotion dynamics across disaster-affected regions and highlights the potential of ABEA in real-time disaster monitoring. The results suggest that ABEA can provide a nuanced understanding of public sentiment during crises for policymakers. Full article

Landslides are among the most hazardous natural phenomena affecting Greater Abidjan, causing significant economic and social damage. Strategic planning supported by geographic information systems (GIS) can help mitigate potential losses and enhance disaster resilience. This study evaluates landslide susceptibility using logistic regression and frequency ratio models. The analysis is based on a dataset comprising 54 mapped landslide scarps collected from June 2015 to July 2023, along with 16 thematic predictor variables, including altitude, slope, aspect, profile curvature, plan curvature, drainage area, distance to the drainage network, normalized difference vegetation index (NDVI), and an urban-related layer. A high-resolution (5-m) digital elevation model (DEM), derived from multiple data sources, supports the spatial analysis. The landslide inventory was randomly divided into two subsets: 80% for model calibration and 20% for validation. After optimization and statistical testing, the selected thematic layers were integrated to produce a susceptibility map. The results indicate that 6.3% (0.7 km²) of the study area is classified as very highly susceptible. The proportion of the sample (61.2%) in this class had a frequency ratio estimated to be 20.2. Among the predictive indicators, altitude, slope, SE, S, NW, and NDVI were found to have a positive impact on landslide occurrence. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), demonstrating strong predictive capability. These findings can support informed land-use planning and risk reduction strategies in urban areas. Furthermore, the prediction model should be communicated to and understood by local authorities to facilitate disaster management. The cost function was adopted as a novel approach to delineate hazardous zones. Considering the landslide inventory period, the increasing hazard due to climate change, and the intensification of human activities, a reasoned choice of sample size was made. This informed decision enabled the production of an updated prediction map. Optimal thresholds were then derived to classify areas into high- and low-susceptibility categories. The prediction map will be useful to planners in helping them make decisions and implement protective measures. Full article