Search Results (1,433)

In many developing countries, seismic vulnerability remains high due to the widespread presence of informally constructed buildings without professional design or technical supervision. In Colombia, where nearly 60% of structures are non-engineered, this issue is especially acute. The objective of this study is to design, implement, and quantitatively evaluate the Learning Experience for Earthquake Awareness Program (LEAP), an experiential educational strategy for young students that enhances seismic knowledge, promotes sustainable construction awareness, and contributes to disaster risk reduction as a component of social sustainability. To address this challenge, LEAP introduces students to basic principles of structural mechanics and seismic behavior through playful, hands-on activities combining theoretical instruction, practical experimentation, collaborative design, and the testing of model structures. An experimental design with pre- and post-surveys was implemented with 141 participants, including 80 secondary school students (grades 8th–11th) and 61 university students enrolled in engineering, architecture, and construction programs, using 3D-printed models, earthquake simulators, and interactive games. Statistical analysis using the Wilcoxon signed-rank test ($p<0.05$) revealed significant improvements in conceptual understanding and perception, including gains in distinguishing between the hypocenter and epicenter ($+45.39\%$, $p=5.10\times {10}^{-8}$, $r=0.50$), understanding seismic magnitude ($+39.01\%$, $p=1.67\times {10}^{-12}$, $r=0.71$), and visually identifying structural vulnerabilities ($+25.50\%$, $p=4.50\times {10}^{-2}$, $r=0.41$). Overall, LEAP contributes to disaster risk reduction and social sustainability by strengthening seismic awareness and responsible construction practices. The most significant results were observed among secondary school students, while university participants mainly reinforced applied and visual comprehension. Given its convenience sample, lack of control group, and immediate post-test, findings should be interpreted as exploratory and associative. Full article

Flood disasters are increasing worldwide due to climate change, posing growing risks to infrastructure and human life. Korea, where nearly 70% of annual rainfall occurs during the summer monsoon, is particularly vulnerable to extreme precipitation events intensified by El Niño and La Niña. This study investigates how terrain resolution influences flood simulation accuracy by comparing a 1 m LiDAR digital elevation model (DEM) with a DEM generated from a 1:5000 topographic map. Flood depth and velocity fields produced by the two DEMs show notable quantitative differences: for final flood depth, the 1:5000 DEM yields a mean absolute error of approximately 56.9 cm and an RMSE of 76.4 cm relative to LiDAR results, with substantial local over- and underestimations. Flow velocity and maximum velocity also show significant deviations, with RMSE values of 58.0 cm/s and 68.4 cm/s, respectively. Although the 1:5000 DEM captures the general inundation pattern, these discrepancies—particularly in narrow channels and urbanized floodplains—demonstrate that coarse-resolution terrain data cannot reliably reproduce hydrodynamic behavior. We conclude that while 1:5000 DEMs may be acceptable for reconnaissance-level hazard screening, high-resolution LiDAR DEMs are essential for accurate flood depth and velocity simulation, supporting their integration into engineering design, urban flood risk assessment, and disaster management frameworks. Full article

Drought remains a phenomenal disaster of critical concerns in West Africa, particularly within the Niger River Basin, due to its insidious, multifaceted, and long-lasting nature. Its continuous severe impacts on communities, combined with the limitations of existing univariate index-based monitoring methods, worsen the challenge. This paper introduces and evaluates a Hybrid Drought Resilience Empirical Model (DREM) that integrates meteorological, agricultural, and hydrological indicators to improve their concurrent monitoring and early warning for effective decision-making in the region. Using reanalysis hydrometeorological data (1980–2016) and community vulnerability records, results show that the DREM-based composite index detects drought earlier than the Standardized Precipitation Index (SPI), with stronger alignment to soil moisture and streamflow variations. The model identifies drought onset when thresholds range from −0.26 to −1.19 over three consecutive months, depending on location, and signals drought termination when thresholds rise between −0.08 and −0.82. The study concludes that the DREM-based composite index provides a more reliable and integrated framework for early drought detection and decision-making across the Niger River Basin, and hence, has proven to be a suitable drought monitor for stakeholders in the Niger Basin which can be relied upon and trusted with high confidence. Full article

Wildfires are increasingly recognized as a climatological hazard, able to threaten industrial and critical infrastructure safety and operations and lead to Natech disasters. Future projections of exacerbated fire regimes increase the likelihood of Natech disasters, therefore increasing expected direct damage costs, clean-up costs, and long-term economic losses due to business interruption and environmental remediation. While large industrial complexes, such as oil, gas, and chemical facilities have sufficient resources for the implementation of effective prevention and mitigation plans, small-to-medium-sized industrial hubs are particularly vulnerable due to their scattered distribution and limited resources for investing in comprehensive fire prevention systems. This study targets the vulnerability of these communities by proposing the deployment of Wireless Sensor Networks (WSNs) as cost-effective Early Wildfire Detection Systems (EWDSs) to safeguard wildland and industrial domains. The proposed approach leverages wildland–industrial interface (WII) geospatial data, simulated wildfire dynamics data, and mathematical optimization to maximize detection efficiency at minimal cost. The WII delimits the boundary where the presence of wildland fires impacts industrial activity, thus representing a proxy for potential Natech disasters. The methodology is tested in Cocentaina, Spain, a municipality characterized by a highly flammable Mediterranean landscape and medium-scale industrial parks. Results reveal the complex trade-offs between detection characteristics and the degree of protection in the combined wildland and WII areas, enabling stakeholders to make informed decisions. This methodology is easily replicable for any municipality and industrial installation, or for generic wildland–human interface (WHI) scenarios, provided there is access to wildfire dynamics data and geospatial boundaries delimiting the areas to protect. Full article

The aim of this article is to present the social–ecological system (SES) as a unit of analysis for sustainable water management under conditions of anthropogenic pressure in Poland. In the face of accelerating climate change and growing human impacts, Polish water systems are exposed to increasing ecological stress and to material and immaterial losses affecting local communities. The SES approach provides an integrative analytical framework that links ecological and social components, enabling a holistic view of adaptive and governance processes at multiple spatial scales, from municipalities to areas that transcend administrative boundaries. Methodologically, this study triangulates three complementary approaches to strengthen explanatory inference. This conceptual SES review defines the analytical categories used in the paper, the bibliometric mapping (Scopus database with VOSviewer) identifies dominant research streams and underexplored themes, and the qualitative Polish case studies operationalize these categories to diagnose mechanisms, feedbacks, and governance vulnerabilities under anthropogenic pressure. The bibliometric analysis identifies the main research streams at the intersection of SES, water management and sustainable development, revealing thematic clusters related to climate change adaptation, environmental governance, ecosystem services and hydrological extremes. The case studies - the 2024 flood, the 2022 ecological disaster in the Odra River, and water deficits associated with lignite opencast mining in Eastern Wielkopolska - illustrate how anthropogenic pressure and climate-related hazards interact within local SES and expose governance gaps. Particular attention is paid to attitudes and social participation, understood as configurations of behaviors, knowledge and emotions that shape decision-making in local self-government, especially at the municipal level. This study argues that an SES-based perspective can contribute to building the resilience of water systems, improving the integration of ecological and social dimensions and supporting more sustainable water management in Poland. Full article

This article examines the failure of the two most recent temporary housing programs implemented in Spain following two major disasters: the 2011 Lorca earthquake and the 2021 La Palma volcanic eruption. Despite differing hazard typologies, both cases resulted in incomplete and ultimately unsuccessful housing programs, with only 13 of the 60 planned units built in Lorca and 121 of the 200 planned units delivered in La Palma. Using a qualitative comparative case study approach, the research analyzes governance decisions, housing design, and implementation processes to assess their impact on the sustainability of post-disaster temporary housing. The analysis adopts the five dimensions of sustainability—environmental, economic, social, cultural, and institutional—as an integrated analytical framework for evaluating public management performance in post-disaster temporary housing. The findings show that early decision-making, shaped by political urgency, technical misjudgments, and the absence of adaptive governance, led to severe delays, cost overruns, inadequate and energy-inefficient construction, and the formation of marginalized settlements. This study concludes that the lack of regulatory frameworks, legal instruments, and operational protocols for temporary housing in Spain was a determining factor in both failures, generating vulnerability, prolonging recovery processes, and undermining sustainability across all five dimensions. By drawing lessons from these cases, this article contributes to debates on resilient and sustainable post-disaster recovery and highlights the urgent need for integrated regulatory frameworks for temporary housing in Spain. Full article

Building resilient and sustainable energy systems is a critical challenge for disaster-prone regions in the Global South. This study investigates the energy management of a networked microgrid (NMG) system on Lombok Island, Indonesia, a region frequently exposed to natural disasters (NDs) and characterized by vulnerable grid infrastructure. A multi-objective optimization framework is developed to jointly minimize operational costs, load-not-served, and environmental impacts under both normal and abnormal operating conditions. The proposed strategy employs the Multi-objective JAYA (MJAYA) algorithm to coordinate photovoltaic generation, diesel generators, battery energy storage systems, and inter-microgrid power exchanges within a 20 kV distribution network. Using real load, generation, and electricity price data, we evaluate the NMG’s performance under five representative fault scenarios that emulate ND-induced outages, including grid disconnection and loss of inter-microgrid links. Results show that the interconnected NMG structure significantly enhances system resilience, reducing load-not-served from 366.3 kWh in fully isolated operation to only 31.7 kWh when interconnections remain intact. These findings highlight the critical role of cooperative microgrid networks in strengthening community-level energy resilience in vulnerable regions. The proposed framework offers a practical decision-support tool for planners and governments seeking to enhance energy security and advance sustainable development in disaster-affected areas. Full article

The increasing severity of extreme weather events necessitates rapid methods for post-disaster damage assessment. Current remote sensing datasets often lack the spatial resolution required for a detailed evaluation of critical waterfront infrastructure, which is vulnerable during hurricanes. To address this limitation, we introduce the Flood and Waterfront Infrastructure Segmentation Dataset (FWISD), a new dataset constructed from high-resolution unmanned aerial vehicle imagery captured after a major hurricane, comprising 3750 annotated 1024 × 1024 pixel image patches. The dataset provides semantic labels for 11 classes, specifically designed to distinguish between intact and damaged structures. We conducted comprehensive experiments to evaluate the performance of both convolution and Transformer-based models. Our results indicate that hybrid models integrating Transformer encoders with convolutional decoders achieve a superior balance of contextual understanding and spatial precision. Regression analysis indicates that the distance to water has the maximum influence on the detection success rate, while comparative experiments emphasize the unique complexity of waterfront infrastructure compared to homogenous datasets. In summary, FWISD provides a valuable resource for developing and evaluating advanced models, establishing a foundation for automated systems that can improve the timeliness and precision of post-disaster response. Full article

Environmental Impact Assessments (EIAs) are crucial for mitigating flood risks in vulnerable ecosystems, yet their effective application remains inconsistent. This study synthesises global literature to systematically map EIA methodologies, evaluate the extent of hydrological integration, and analyse the evolution of practices against policy frameworks for flood-prone areas. A scoping review of 144 peer-reviewed articles, conference papers, and one book chapter (2005–2025) was conducted using PRISMA protocols, complemented by bibliometric analysis. Quantitative findings reveal a significant gap where 72% of studies lacked specialised hydrological impact assessments (HIAs), with only 28% incorporating them. Post-2016, advanced tools like GIS, remote sensing, and hydrological modelling were used in less than 32% of studies, revealing reliance on outdated checklist methods. In South Africa, despite wetlands covering 7.7% of its territory, merely 12% of studies applied flood modelling. Furthermore, 40% of EIAs conducted after 2016 excluded climate adaptation strategies, undermining resilience. The literature is geographically skewed, with developed nations dominating publications at a 3:1 ratio over African contributions. The study’s novelty is its systematic global mapping of global EIA practices for flood-prone areas and its proposal for mandatory HIAs, predictive modelling, and strengthened policy enforcement. Practically, these reforms can transform EIAs from reactive compliance tools into proactive instruments for disaster risk reduction and climate resilience, directly supporting Sustainable Development Goals 11 (Sustainable Cities), 13 (Climate Action), and 15 (Life on Land). This is essential for guiding future policy and improving EIA efficacy in the face of rapid urbanisation and climate change. Full article

The unique natural environment and climate of tropical island regions present significant challenges to construction. Under these variable natural conditions and complex construction processes, identifying and analyzing potential risks that could lead to vulnerabilities in construction safety systems and clarifying their transmission pathways remains a pressing issue. To fill this research gap, a GV-IB model for vulnerability analysis of construction safety systems in tropical island building projects (CSSTIBPs) was established. This model constructs a vulnerability analysis index system for tropical island construction safety systems based on the Grey Relational Analysis (GRA) and Vulnerability Scoping Diagram (VSD), considering exposure, sensitivity, and adaptability. By combining the artificial fish swarm algorithm with the K2 algorithm and the EM algorithm, an Improved Bayesian Network (IBN) is constructed to analyze and infer the influencing factors and disaster chains of vulnerability in tropical island construction safety systems. The IBN can effectively overcome the dependence on node order and data gaps in traditional Bayesian Network construction methods. The effectiveness of the model is verified by analyzing Hainan Island, China. The research results show that (a) The IBN stability verification showed an Area Under ROC Curve (AUC) of 0.783 > 0.7, indicating high effectiveness in identifying vulnerability factors. (b) Within the vulnerability measurement nodes of the CSSTIBPs, the influence on the system decreases in the following order is exposure (0.41), sensitivity (0.31), and adaptability (0.03). (c) Emergency response time, safety training, hazard identification time, accident response time, and duration of severe weather are key factors affecting the vulnerability of CSSTIBPs. Full article

Catastrophic mass flows originating from the high mountain cryosphere often cause cascading hazards. With increasing human activities in the alpine region and the sensitivity of the cryosphere to climate warming, cryospheric hazards are becoming more frequent in the mountain regions. Monitoring the evolution and impact of the glaciers and ice-rock avalanches and hazard consequences in the mountain regions is crucial to understand nature and drivers of mass flow process in order to prevent and mitigate potential hazard risks. In this study, the glacier and ice-rock avalanches that occurred in the Tibetan Plateau (TP) were investigated based on the Sentinel-2 satellite data and in situ observations, and the main driving forces and impacts on the regional environment, landscape, and geomorphological conditions were also analyzed. The results showed that the avalanche deposit of Arutso glacier No. 53 completely melted away in 2 years, while the deposit of Arutso glacier No. 50 melted in 7 years. Four large-scale ice-rock avalanches in the Sedongpu basin not only had significant impacts on the river flow, landscape, and geomorphologic shape in the basin, but also caused serious disasters in the region and beyond. These glacier and ice-rock avalanches were caused by temperature anomaly, heavy precipitation, climate warming, and seismic activity, etc., which act on the specific glacier properties in the high mountain regions. The study highlights scientific advances should support and benefit the remote and vulnerable mountain communities to make mountain regions safer. Full article

Against the backdrop of intensifying global climate change and expanding human encroachment into mountainous regions, landslides have increased markedly in both frequency and destructiveness, emerging as a key risk to socio-ecological security and development in mountain areas. Rigorous assessment and forward-looking prediction of landslide disaster vulnerability (LDV) are essential for targeted disaster risk reduction and regional sustainability. However, existing studies largely center on landslide susceptibility or risk, often overlooking the dynamic evolution of adaptive capacity within affected systems and its nonlinear responses across temporal and spatial scales, thereby obscuring the complex mechanisms underpinning LDV. To address this gap, we examine Yunnan Province, a landslide-prone region of China where intensified extreme rainfall and the expansion of human activities in recent years have exacerbated landslide risk. Drawing on the vulnerability scoping diagram (VSD), we construct an exposure–sensitivity–adaptive capacity assessment framework to characterize the spatiotemporal distribution of LDV during 2000–2020. We further develop a multi-model, multi-scale integrated prediction framework, benchmarking the predictive performance of four machine learning algorithms—backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF), and XGBoost—across sample sizes ranging from 2500 to 360,000 to identify the optimal model–scale combination. From 2000 to 2020, LDV in Yunnan declined overall, exhibiting a spatial pattern of “higher in the northwest and lower in the southeast.” High-LDV areas decreased markedly, and sustained enhancement of adaptive capacity was the primary driver of the decline. At approximately the 90,000-cell grid scale, XGBoost performed best, robustly reproducing the observed spatiotemporal evolution and projecting continued declines in LDV during 2030–2050, albeit with decelerating improvement; low-LDV zones show phased fluctuations of “expansion followed by contraction”, whereas high-LDV zones continue to contract northwestward. The proposed multi-model, multi-scale fusion framework enhances the accuracy and robustness of LDV prediction, provides a scientific basis for precise disaster risk reduction strategies and resource optimization in Yunnan, and offers a quantitative reference for resilience building and policy design in analogous regions worldwide. Full article

In the initial phase of large-scale disasters, delayed external relief supplies make scientific local emergency supply allocation crucial—not only for reducing casualties, but also for advancing sustainable disaster response, a key link in enhancing post-disaster resilience. Existing research mostly focuses on cross-regional material allocation while overlooking local challenges like low resource efficiency and unbalanced supply–demand dynamics. To tackle these limitations in the existing research, this study develops a multi-objective collaborative local emergency supply allocation model centered on sustainability. It uses an improved TOPSIS method to quantify the urgency of needs in disaster-stricken areas, prioritizing material distribution to vulnerable regions in line with the principle of “no vulnerable area left neglected in relief efforts”. The study also integrates the entropy weight method and analytic hierarchy process (AHP) to ensure rational indicator weighting, and designs a double-layer encoded genetic algorithm to obtain optimal allocation schemes that balance efficiency, fairness, and sustainability. Validated using the 2013 Ya’an Earthquake case study, the model outperforms traditional local allocation approaches: it boosts resource utilization efficiency by reducing material shortage rates, accelerates post-disaster recovery by shortening response times, and improves allocation fairness. Findings provide empirical support for the establishment of “local–external” collaborative rescue systems and sustainable disaster risk reduction frameworks. Empirical calculations using case-specific data and real-world estimates verify the model’s practical applicability: it meets the requirements for fair and rapid allocation needs, aligns with the goals of sustainable disaster management, and lowers the carbon footprint of relief operations by lessening reliance on long-distance external materials. Full article

High-rise buildings present substantial challenges for firefighting and rescue operations owing to their considerable height. The stack effect, which becomes more pronounced with increasing building height, accelerates smoke propagation and significantly increases the likelihood of casualties. This study identifies and analyzes the risks associated with fire incidents in high-rise residential buildings. A 49-story building was selected as the reference model, and population density was applied to estimate occupant numbers for the risk assessment. For the damage scenario, one disaster-vulnerable individual per household was assumed. The simulation results revealed that firefighters and vulnerable occupants were exposed to smoke within 541 s. The findings of this study indicate that the stack effect, amplified by building height, exacerbates fire and smoke spread, thereby increasing firefighting risks and potential casualties. These results highlight fire incidents in high-rise structures as a critical category of urban disaster. Furthermore, the study underscores the limitations of existing firefighting facilities in addressing such scenarios and emphasizes the urgent need for new paradigms in firefighting strategies and smoke control technologies to mitigate the risks associated with the stack effect. Full article

The large-scale integration of distributed photovoltaics (DPV) and their inherent uncertainties have significantly increased the operational risks of distribution networks. Moreover, frequent outages caused by extreme events further impose substantial losses on these networks, highlighting the urgent need to enhance their disaster resilience and load-supply capabilities. To address these challenges, this paper proposes an energy storage allocation method that simultaneously considers economic performance and comprehensive vulnerability. First, a vulnerability assessment framework for distribution networks is established from both pre-disaster and post-disaster perspectives. In the pre-disaster stage, an improved electrical betweenness index, voltage deviation index, and network-balance index are employed to identify weak lines and nodes. In the post-disaster stage, based on the identified weak components, two types of scenarios, namely random line failures and worst-case failures, are constructed to emulate extreme events, and an enhanced network supply efficiency index is developed to quantitatively evaluate the network’s recovery capability. Subsequently, a multi-objective optimal allocation model for energy storage is formulated with economic cost and comprehensive vulnerability as objective functions, and an Enhanced Beluga Whale Optimization algorithm is adopted to obtain the optimal siting and sizing of energy storage systems. Case studies on an improved IEEE 33-bus distribution system show that, compared with the no-ESS scheme, the proposed plan yields about a 66.4% reduction in network loss cost, around 22% improvement in average voltage deviation, and a roughly 10% reduction in the comprehensive vulnerability index under normal operation. Under random and targeted line outage scenarios, the proposed scheme also achieves the highest area under curve and average network effectiveness indices and the lowest performance volatility among the benchmark strategies. These results demonstrate that, for the tested IEEE 33-bus system, the vulnerability-driven ESS planning framework can markedly enhance both economic efficiency and resilience to extreme events. Full article