Search Results (721)

Natural disasters occur continuously across the globe, posing severe threats to human life and property. Remote sensing technology has provided powerful technical means for large-scale and rapid disaster monitoring. However, the deep integration of remote sensing observations with sector-specific disaster statistical data to construct a knowledge system that supports early warning decision-making remains a significant challenge. This study aims to address the bottleneck in the “data-information-knowledge-service” transformation process by constructing an integrated natural disaster early warning knowledge graph that incorporates multi-source heterogeneous data. We first designed an ontological schema layer comprising six core elements: disaster type, event, anomaly information, impact information, warning information, and decision information. Subsequently, multi-source data were integrated from various sources, including the Emergency Events Database (EM-DAT), sector-specific websites, encyclopedic pages, and remote sensing imagery such as Gaofen-2 (GF-2) and Sentinel-1. A Bidirectional Encoder Representations from Transformers with a Conditional Random Field layer (BERT-CRF) model was employed for entity and relation extraction, and the knowledge was stored and visualized using the Neo4j graph database. The core innovation of this research lies in proposing a quantitative methodology for assessing disaster intensity, impact, and trends based on remote sensing evaluation, establishing a knowledge conversion mechanism with sector-specific warning levels, and designing explicit warning issuance rules. A case study on a specific wildfire event (2017-0417-PRT, Coimbra, Portugal) demonstrates that the knowledge graph not only achieves organic integration and visual querying of multi-source disaster knowledge but also facilitates warning decision-making driven by remote sensing assessment indicators. For this event, quantitative analysis of Gaofen-2 imagery yielded intensity, impact, and trend levels of 4, 3, and 3, respectively, which, when applied to our warning rule (intensity ≥ 1 or impact ≥ 1 or trend ≥ 3), automatically triggered an early warning, thereby validating the rule’s practicality. A preliminary performance evaluation on 50 historical wildfire events demonstrated promising results, with an F1-score of 74.3% and an average query response time of 128 ms, confirming the system’s practical responsiveness and detection capability. In conclusion, this study offers a novel and operational technical pathway for the deep interdisciplinary integration of remote sensing and disaster science, effectively bridging the gap between data silos and actionable warning knowledge. Full article

Floods are among the most frequent and damaging natural hazards in the Mekong River Basin, where the interplay of monsoon-driven climate variability, complex topography, and rapid socio-economic change creates high exposure and vulnerability. This study presents a comprehensive assessment of flood disaster patterns, loss distribution, and regional disparities across five countries in the Lower Mekong Basin—Cambodia, Laos, Myanmar, Thailand, and Vietnam. Using multivariate spatiotemporal analysis based on EM-DAT, MRC, and national government datasets, the study quantifies flood frequency, casualties, and affected population to reveal cross-country differences in disaster impact and timing. Results show that while Vietnam and Thailand experience high flood frequency and storm-induced events, Laos and Cambodia face riverine flooding under constrained economic and infrastructural conditions. The findings highlight a basin-wide increase in flood frequency over recent decades, driven by climate change, land use transitions, and uneven development. The analysis identifies critical gaps in adaptive governance, particularly the need for dynamic policy frameworks that can adjust to spatial disparities in flood typologies (e.g., Vietnam’s storm floods vs. Cambodia’s riverine floods) and improve transboundary coordination of reservoir operations. Despite the region’s extensive reservoir capacity, most infrastructure prioritizes hydropower over flood mitigation. The study evaluates the role of regional cooperation frameworks such as the Lancang–Mekong Cooperation (LMC), demonstrating how strengthened institutional flexibility and knowledge-sharing mechanisms could enhance progress toward Sustainable Development Goals (SDGs) related to water governance (SDG 6), resilient infrastructure (SDG 9), and disaster risk reduction (SDG 11). By constructing the first integrated national-level flood disaster database for the basin and conducting comparative analysis across countries, this research provides empirical evidence to support differentiated yet coordinated flood risk governance strategies at both national and transboundary levels. Full article

Earth-rock dams are critical components of hydraulic engineering, undertaking core functions such as flood control and disaster mitigation. However, the potential occurrence of dam breach poses a severe threat to regional socioeconomic stability and ecological security. To address the limitations of traditional Bayesian network (BN) in capturing the complex nonlinear coupling and dynamic mutual interactions among risk factors, they are integrated with machine learning techniques, based on a collected dataset of earth-rock dam breach case samples, the PC structure learning algorithm was employed to preliminarily uncover risk associations. The dataset was compiled from public databases, including the U.S. Army Corps of Engineers (USACE) and Dam Safety Management Center of the Ministry of Water Resources of China, as well as engineering reports from provincial water conservancy departments in China and Europe. Expert knowledge was integrated to optimize the network topology, thereby correcting causal relationships inconsistent with engineering mechanisms. The results indicate that the established hybrid model achieved AUC, accuracy, and F1-Score values of 0.887, 0.895, and 0.899, respectively, significantly outperforming the data-driven model G1. Forward inference identified the key drivers elevating breach risk. Conversely, backward inference revealed that overtopping was the direct failure mode with the highest probability of occurrence and the greatest contribution. The integration of data-driven approaches and domain knowledge provides theoretical and technical support for the probabilistic quantification of earth-rock dam breach and risk prevention and control decision-making. Full article

Climate change has intensified issues that undermine children’s health, compromise their well-being, and hinder their ability to develop disaster resilience. Disaster education is essential for building disaster resilience among children. As a disaster-prone country, Japan has been developing new approaches to improve disaster education programs, including those in early childhood education, to equip children with the knowledge and skills needed to mitigate risks and respond effectively to disasters. Basic disaster concepts are introduced through hands-on learning, helping children understand key ideas. This paper examines innovations in disaster education, particularly at the kindergarten level, using Bousai Terakoya as a case study. The study reveals that Bousai Terakoya fosters collaboration among schools, communities, and industries to educate kindergarten students about disasters. It emphasizes that protecting oneself and one’s family is a focus of the program, which aims to strengthen future disaster education efforts. This research adds to the discussion on disaster education for children. Active involvement from schools, communities, and industries can help develop strategies to improve the retention of essential disaster concepts in children’s memories. Full article

In many disaster-prone regions, lower-income communities face disproportionate impacts due to the prevalence of informal housing. Informal housing, characterized by substandard construction and lack of adherence to building codes, exacerbates vulnerabilities during disasters, leading to widespread destruction and hampered recovery efforts. This study examines the multifaceted causes of informal housing in Puerto Rico using a qualitative content analysis of applicable literature. Seven interdisciplinary factors were derived from 42 relevant manuscripts with identifiable factors linked to informal housing in Puerto Rico: Knowledge, Perception, Government Dynamics, Institutional Support, Enforcement, Culture, and Resources. Despite post-disaster efforts advocating for building back better, systemic challenges perpetuate informal housing practices, reinforcing cycles of vulnerability. This research underscores the need for integrated decision making in pre-disaster preparation and post-disaster reconstruction efforts. This research presents a detailed understanding of the Informal Housing Cycle, demonstrates how interdisciplinary factors are barriers to safe and sustainable housing, and explores the complex relationships between these factors. This study aims to guide policy and practice to reduce future disaster impacts on Puerto Rico housing, thus breaking the cycle of vulnerability, empowering communities, and fostering sustainable resilience in post-disaster reconstruction efforts. Full article

The present study developed and evaluated an integrated disaster nursing education program combining functional training and simulator-based learning to address limitations of traditional, theory-driven approaches. Overall, 49 senior nursing students completed the program using a four-stage repeated-measures design. The findings indicated a substantial enhancement in disaster nursing knowledge over time. However, problem-solving ability, learning self-efficacy, and motivation exhibited improvement only in post hoc comparisons. This contradictory yet fundamental finding suggests that knowledge acquisition occurs more directly, whereas problem-solving and motivational competencies require cumulative practice, feedback, and contextual immersion. Educator reflections and student debriefings further underscored the significance of teamwork, communication, and scenario relevance in facilitating learning transfer. Despite its limitations, including a single-site, female-dominated sample, reliance on self-reported measures, and a brief follow-up period, this study makes a significant contribution to the field of disaster nursing education by presenting a sustainable and adaptable model. Incorporation of multi-institutional and longitudinal designs, as well as qualitative analyses of learning processes will be crucial in future studies. This will ensure the study’s generalizability and long-term impact. Full article

Disaster headlines often underscore devastation and loss while overlooking success stories where proactive disaster risk reduction (DRRM) measures have averted catastrophe, saved lives, and reduced economic damage. This study addresses the gap in documentation and analysis of DRRM success stories in Africa, particularly within the Southern African Development Community (SADC), arguing that the absence of such narratives hampers a shift from reactive to proactive disaster risk governance. The research aims to extract critical lessons from success stories for enhancing future preparedness and response frameworks. A qualitative research design was employed, integrating document analysis, expert interviews, field observations, and practitioner workshops. Data was triangulated from diverse sources, including national disaster management agency reports (e.g., South Africa’s NDMC, Botswana’s NDMO, Mozambique’s INGC), peer-reviewed literature, UNDRR reports, SADC policy documents, and first-hand experiences from the authors’ consultancy work in the African Union’s biennial DRRM reporting processes. Case studies examined include Mozambique’s response to Cyclone Idai in 2019, South Africa’s drought and flood risk governance (e.g., the 2023 floods in Eastern and Western Cape), and Malawi’s flood resilience programs. Findings reveal that successful DRRM outcomes are driven by a combination of anticipatory governance, community-based preparedness, integration of Indigenous Knowledge Systems (IKSs), and investment in infrastructure and ecosystem-based adaptation. These cases demonstrate that locally embedded, yet scientifically informed, interventions enhance resilience and reduce disaster impacts. The study underscores the relevance of theoretical frameworks such as resilience theory, narrative theory, and social learning in interpreting how success stories contribute to institutional memory and adaptive capacity. Policy recommendations emphasize the need for institutionalizing success-story documentation in national DRRM frameworks, scaling up community engagement in risk governance, and fostering regional knowledge-sharing platforms within the SADC. Furthermore, the paper advocates for making DRRM success stories more visible and actionable to transition toward more anticipatory, inclusive, and effective disaster risk management systems. Full article

This review seeks to understand what urban planning and management can do to reduce disaster risk and help cities adapt to the impacts of climate change. To achieve this, it examines various streams of the literature, as the topic sits at the intersection of several distinct but relevant disciplinary fields. These include urban planning in hazardous areas, recovery planning, disaster risk reduction (an umbrella term encompassing disciplines from engineering to geography and sociology), and, more recently, climate change adaptation. To navigate this vast body of knowledge, a conceptual framework is proposed to guide the selection of the relevant literature, and the strategy for this selection is detailed in the methodological section. This review adopts elements of both critical and theoretical approaches: it does not aim to be comprehensive or to systematically search each disciplinary domain addressed. While acknowledging the limitations and potential biases in the selection of articles and books, the review reflects an evolution in the discourse on urban planning for resilience. The discussion explores how the concept of resilience has emerged as a valuable bridge between disaster risk reduction, sustainability, and climate change adaptation—especially as cities face increasing exposure and vulnerability to stresses that are now more frequently compounded, multi-hazard, and cascading. The conclusion outlines the gaps and challenges that researchers, practitioners, and policy makers need to address moving forward. Full article

Background/Objectives: Inclusive disaster management is increasingly recognized as essential for mitigating the heightened vulnerabilities faced by individuals with special needs during crises. This study aimed to examine the knowledge, perceptions, and practices of Search and Rescue Non-Governmental Organization (SAR-NGO) volunteers regarding the inclusion of individuals with special needs in disaster and emergency response scenarios. Methods: This qualitative case study was conducted in Eskişehir, Türkiye, involving 20 accredited SAR-NGO volunteers selected through criterion sampling. Data were collected through semi-structured interviews, document analysis, and reflective diaries by the researcher. The study explored the conceptualizations and operational practices of volunteers concerning inclusivity in disaster settings. Results: Findings indicate that SAR-NGO volunteers primarily associate special needs with visible physical and sensory impairments, demonstrating limited awareness of cognitive and invisible disabilities. Volunteers reported difficulties in identifying individuals with special needs during emergencies and encountered substantial communication barriers due to the lack of alternative communication tools and insufficient training. Despite receiving extensive technical training in search and rescue operations, participants revealed a significant lack of formal education on inclusive practices. Conclusions: Effective inclusive disaster response necessitates not only technical proficiency but also structured training in disability awareness, accessible communication, and inclusive intervention strategies. The study recommends revising volunteer training curricula to integrate participatory models involving individuals with disabilities and expanding inclusive disaster research across various regions. These findings highlight critical gaps in current disaster response systems and underscore the need for systemic reforms to achieve inclusive resilience. Full article

Amid the increasing frequency and severity of major disasters, the rapid spread of online misinformation poses substantial risks to public safety, effective crisis management, and long-term societal sustainability. Current methods for managing disaster-related rumors rely on static, rule-based approaches that lack scalability, fail to capture nuanced misinformation, and are limited to reactive responses, hindering effective disaster management. To address this gap, this study proposes a novel framework that leverages large language models (LLMs) and event knowledge graphs (EKGs) to facilitate the sustainable agile identification and adaptive control of disaster-related online rumors. The framework follows a multi-stage process, which includes the collection and preprocessing of disaster-related online data, the application of Gaussian Mixture Wasserstein Autoencoders (GMWAEs) for sentiment and rumor analysis, and the development of EKGs to enrich the understanding and reasoning of disaster events. Additionally, an enhanced model for rumor identification and risk control is introduced, utilizing Graph Attention Networks (GATs) to extract node features for accurate rumor detection and prediction of rumor propagation paths. Extensive experimental validation confirms the efficacy of the proposed methodology in improving disaster response. This study contributes novel theoretical insights and presents practical, scalable solutions for rumor control and risk management during crises. Full article

Nepal is highly susceptible to natural hazards, including earthquakes, flooding, and landslides, all of which may occur independently or in combination. Climate change is projected to increase the frequency and intensity of these natural hazards, posing growing risks to Nepal’s infrastructure and development. To the authors’ knowledge, the majority of existing geohazard research in Nepal is typically limited to single hazards or localised areas. To address this gap, MiMapper was developed as a cloud-based, open-access multi-hazard mapping tool covering the full national extent. Built on Google Earth Engine and using only open-source spatial datasets, MiMapper applies an Analytical Hierarchy Process (AHP) to generate hazard indices for earthquakes, floods, and landslides. These indices are combined into an aggregated hazard layer and presented in an interactive, user-friendly web map that requires no prior GIS expertise. MiMapper uses a standardised hazard categorisation system for all layers, providing pixel-based scores for each layer between 0 (Very Low) and 1 (Very High). The modal and mean hazard categories for aggregated hazard in Nepal were Low (47.66% of pixels) and Medium (45.61% of pixels), respectively, but there was high spatial variability in hazard categories depending on hazard type. The validation of MiMapper’s flooding and landslide layers showed an accuracy of 0.412 and 0.668, sensitivity of 0.637 and 0.898, and precision of 0.116 and 0.627, respectively. These validation results show strong overall performance for landslide prediction, whilst broad-scale exposure patterns are predicted for flooding but may lack the resolution or sensitivity to fully represent real-world flood events. Consequently, MiMapper is a useful tool to support initial hazard screening by professionals in urban planning, infrastructure development, disaster management, and research. It can contribute to a Level 1 Integrated Geohazard Assessment as part of the evaluation for improving the resilience of hydropower schemes to the impacts of climate change. MiMapper also offers potential as a teaching tool for exploring hazard processes in data-limited, high-relief environments such as Nepal. Full article

The severity of natural disasters is increasing every year, having an impact on many people’s lives. During the response phase of disasters, airports are important hubs where relief aid arrives while people need to be evacuated to safety. However, the airport often forms a bottleneck in these relief operations because of the sudden need for increased capacity. Limited research is carried out on the operational side of airport disaster management. Experts identify the main problems as first the asymmetry of information between the airport and the incoming flights, and second the lack of resources. The goal of this research is to gain understanding of the effects of incomplete knowledge of incoming flights with different resource allocation strategies on the performance of the cargo handling operations in an airport after a natural disaster event. An agent-based model is created, where realistic offloading strategies with different degrees of information uncertainty are implemented. Model calibration and verification are performed with experts in the field. The model performance is measured by the average turnaround time, which can be split into offloading time, boarding time and the cumulative waiting times. The results show that the effects of one unplanned aircraft are negligible. However, the waiting times and other inefficiencies rapidly increase with the more unplanned aircraft arriving. Full article

Escalating extreme wildfires, fueled by the confluence of climate change, land use patterns alterations, ignitions by humans, and flammable fuels accumulation, pose significant and increasingly destructive risks to critical infrastructure (CI). This study presents a comprehensive comparative analysis of wildfire impacts and the corresponding CI resilience strategies employed across the EU and the US. It examines the vulnerability of CIs to the devastating effects of wildfires and their inadvertent contribution to wildfire ignition and spread. The study evaluates the EU’s CER Directive and the US National Infrastructure Protection Plan and assesses European Commission wildfire resilience-related initiatives, including FIRELOGUE, FIRE-RES, SILVANUS, and TREEADS flagship projects. It synthesizes empirical evidence and extracts key lessons learned from major wildfire events in the EU (2017 Portuguese fires; 2018 Mati wildfire) and the US (2023 Lahaina disaster; 2025 Los Angeles fires), drawing insights regarding the effectiveness of various resilience measures and identifying areas for improvement. Persistent challenges impeding effective wildfire resilience are identified, including governance fragmentation, lack of standardization in risk assessment and mitigation protocols, and insufficient integration of scientific knowledge and data into policy formulation and implementation. It concludes with actionable recommendations aimed at fostering science-based, multi-stakeholder approaches to strengthen wildfire resilience at both policy and operational levels. Full article

Affordable public housing has been identified as a key programme initiative towards social infrastructural development. Scholarship has shown that sustainable affordable public housing (SAPH) aids in achieving the UN Sustainable Development Goals (SDGs), especially SDG 1 (No Poverty), SDG 9 (Industry, Innovation and Infrastructure) and SDG 11 (Sustainable Cities and Communities). There exists a dire need for sustainable and affordable housing initiatives in Small Island Developing States (SIDS). The purpose of this research paper is to investigate the relationship between sustainability and affordable public housing development by extracting and ranking critical success factors (CSFs) associated with SAPH in Caribbean SIDS. By adopting a quantitative methodology, a closed-ended questionnaire was administered to relevant personnel associated with the construction industry to rank the risk impact of various success factors relating to SAPH and produce a Structural Equation Model (SEM). The findings revealed that CSFs for SAPH in SIDS are context specific and deviated from the general literature. CSFs were broadly grouped into economic, energy efficiency and transparency, and construction execution categories. The most pertinent CSF was the implementation of risk reduction inputs into the planning stages of construction projects. These risk reduction inputs are important preventive measures for disasters and contribute to achieving social, economic and environmental sustainability within Caribbean SIDS. This research aims to bridge the gap between academic research and praxis to advance a sustainable construction industry within SIDS while contributing to the critically limited body of knowledge on SAPH within developing nations. It also aims to form the foundation of sustainable housing within SIDS based on the Triple Bottom Line (TBL) theory. Full article

Background: Disasters, although predictable, often occur unexpectedly, and efforts must be directed towards reducing their impact. Emergency healthcare workers, key players in disaster response, should maintain a high level of preparedness to act in catastrophic situations. Data on knowledge, attitude, and disaster preparedness among emergency healthcare workers is scarce, particularly for developed countries in Europe. This study aimed to measure the perceived preparedness of various health practitioners in emergency care in Iași county (Romania) and identify factors that influence it. Materials and methods: A self-assessment web-based questionnaire was developed to measure knowledge (K), attitude (A), and preparedness (P). Nonparametric tests compared measurements between demographic groups. Spearman correlation, linear univariate, and multivariate regression models were used to test the effect of perceived knowledge, attitude, and other work-related factors (such as experience, training, and leadership) on disaster preparedness. Results: 211 valid entries were recorded (114 female and 97 male), of which 33.6% were doctors, 25.1% were nurses, and 23.7% were paramedics. There were differences in exposure to training across health professions for disasters and trauma management (p = 0.03 and p = 0.009). The sample’s overall scores for the three primary domains assessed were moderate. Univariate analyses identified a significant effect of knowledge and attitude on preparedness (B = 0.9, 95% CI: 0.79–1.01, p < 0.001, and B = 0.81, 95% CI: 0.66–0.97, p < 0.001, respectively), which was maintained in multivariate regression. Workplace factors (disaster plans and institutional collaboration), along with experience in disaster management and emergency care, were determinants of preparedness, while the effect of training was insignificant. Conclusions: Most healthcare workers displayed moderate preparedness for disasters, while exposure to training and practice was found to be inadequate. Focus should be placed on identifying barriers and enhancing training delivery, strengthening institutional involvement in staff preparedness, and improving inter-professional collaborations. Adequate training methods must be developed and validated in further studies. Full article