Search Results (126)

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

After large-scale disasters, swift and robust humanitarian logistics are crucial to provide timely assistance to injured people and displaced individuals. This study proposes a bi-objective optimization model for humanitarian logistics network design to simultaneously consider the facility location-allocation decisions, along with the transportation operation issues under uncertainty. The framework addresses the needs of both severely and mildly injured casualties and homeless populations. A hybrid robust optimization approach is accordingly developed that incorporates scenario-based, box-type, and polyhedral uncertainty representations to handle the uncertainty of factors such as casualty volume, travel times, facility failures, and demands for resources. More recently, machine learning methods have been applied to classify casualties and displaced individuals with respect to their geographic distribution and severity, further improving demand estimates and operational efficacy. This study seeks to develop a data-driven and robust optimization framework for designing humanitarian logistics networks under uncertainty, enabling decision-makers and emergency planners to gain insights into enhancing casualty evacuation, medical treatment, and shelter allocation in disaster response operations. The case of the Kermanshah earthquake in Iran is used for assessing the applicability of the model. The computational experiments and comparative analyses conducted show that the developed model exhibits high efficiency and robustness. The results are useful for guiding disaster preparedness and strategic decisions in humanitarian logistics. Besides operational performance, the model optimizes sustainability in the area of emergency response based on cost efficiency and social fairness, as underlined by SDGs 3 and 11. Full article

Urban metro systems are highly sensitive to seismic disturbances, and the ability of metro stations to manage emergencies effectively has become an increasingly important component of urban resilience. This study develops a resilience-oriented evaluation framework that conceptualizes emergency management as a sequential managerial process encompassing preparedness, response, and recovery. A multi-dimensional indicator system was constructed based on the four resilience capacities—absorptive, maintaining, recovery, and adaptive—and operationalized through a multi-stage Data Envelopment Analysis (DEA) model. The framework enables both overall efficiency assessment and stage-specific diagnosis of managerial weaknesses. Methodologically, the study demonstrates how resilience theory can be operationalized into a network efficiency structure suitable for process-level diagnosis rather than aggregate scoring. A case study of a representative metro station demonstrates the applicability of the proposed method. The results reveal that while preparedness practices are relatively mature, notable inefficiencies exist in real-time response and post-event recovery due primarily to managerial factors such as communication reliability, personnel coordination, and restoration planning. Improvement simulations confirm that targeted enhancements in these management processes can substantially increase overall emergency efficiency. The findings highlight that seismic resilience is not solely determined by physical infrastructure but is heavily dependent on managerial effectiveness across the emergency cycle. The proposed framework contributes a process-oriented, data-driven tool for evaluating and improving emergency management performance and offers practical guidance for metro operators seeking to strengthen resilience under earthquake scenarios. Full article

For the Simeulue community, trauma does not remain a source of fear or psychological burden. Instead, it becomes a guideline for their survival. This study explores how societies reconstruct themselves through memory by examining smong, the local knowledge of the Simeulue community in Indonesia, as a cultural mechanism that transforms disaster experience into social resilience. Using a qualitative ethnographic approach, the research utilizes interviews, nandong and song lyrics, field notes, and historical documentation. The findings indicate that smong operates through interconnected layers of communicative and cultural memory: it is preserved in family stories, bedtime stories, artistic expressions, commemorative practices, and symbolic markers such as monuments and grave inscriptions. Through these processes, traumatic experiences are reframed as moral instructions and actionable knowledge that guide rapid evacuation, mutual aid, and collective vigilance during earthquakes and tsunamis. This study demonstrates that the reconstruction of the Simeulue community is driven not by a formal disaster management system but by practices rooted in culture. Past disaster experiences are continuously reinterpreted and integrated into everyday life. This highlights the importance of memory-based strategies for strengthening community resilience and offers directions for future research on intergenerational knowledge transmission, cultural adaptation, and disaster preparedness in oral societies. Full article

Many countries around the world repeatedly suffer from natural disasters such as earthquakes, tsunamis, floods, and hurricanes due to geographical factors, including plate boundaries, tropical cyclone zones, and coastal regions. Representative examples include Hurricane Katrina, which struck the United States in 2005, and the Great East Japan Earthquake in 2011. Both were large-scale disasters that occurred in developed countries and caused enormous human and economic losses regardless of disaster type or location. As the occurrence of such catastrophic events remains inevitable, establishing effective preparedness and rapid response systems for large-scale disasters has become an urgent global challenge. One of the critical issues in disaster response is the rapid estimation of the number of affected individuals required for effective rescue operations. During large-scale disasters, terrestrial communication infrastructure is often rendered unusable, which severely hampers the collection of situational information. If the population within a disaster-affected area can be estimated without relying on ground-based communication networks, rescue resources can be more appropriately allocated based on the estimated number of people in need, thereby accelerating rescue operations and potentially reducing casualties. In this study, we propose a population-estimation system that remotely senses radio signals emitted from smartphones in disaster areas using Low Earth Orbit (LEO) satellites. Through numerical analysis conducted in MATLAB R2023b, the feasibility of the proposed system is examined. The numerical results demonstrate that, under ideal conditions, the proposed system can estimate the number of smartphones within the observation area with an average error of 2.254 devices. Furthermore, an additional evaluation incorporating a 3D urban model demonstrates that the proposed system can estimate the number of smartphones with an average error of 19.03 devices. To the best of our knowledge, this is the first attempt to estimate post-disaster population using wireless LAN signals sensed by LEO satellites, offering a novel remote-sensing-based approach for rapid disaster response. Full article

Earthquakes are among the most catastrophic natural disasters, primarily due to their immediate potential to cause loss of human life. However, their impact extends beyond the initial seismic event, particularly in karst systems, where groundwater resources are highly sensitive to geodynamic disturbances. The abundance of karst springs within these terrains makes them critical water sources for many communities, yet earthquakes can significantly disrupt their discharge patterns and degrade water quality. This study examines the vulnerability of karst springs to seismic activity, focusing on two case studies that illustrate distinct earthquake-induced hydrogeological effects. The first case investigates the temporary failure of the Opačac Spring near Imotski, Croatia, following the Mw 3.7 earthquake on 7 September 2018. This spring experienced a complete cessation of discharge for four days, as recorded by continuous hydrograph monitoring, before recovering due to the release of accumulated groundwater behind a temporarily blocked conduit. The second case explores the impact of seismic activity on water quality, focusing on the sensitive freshwater lens of the karstic Island of Vis in response to the Mw 6.1 earthquake on 22 April 2022, near Stolac, Bosnia and Herzegovina. Despite the epicenter being over 150 km away, water quality monitoring revealed notable changes, emphasizing the influence of seismic disturbances on fragile groundwater systems in carbonate island environments. Using a multidisciplinary approach, integrating seismic data analysis with hydrological and hydrogeological observations, this study investigates the mechanisms through which earthquakes alter karst water systems. A proposed vulnerability assessment framework is introduced, aiming to correlate earthquake intensity, proximity, and hydrogeological response to better predict karst spring failure and water quality degradation. This model provides valuable insights for disaster preparedness, water resource management, and risk mitigation strategies in karst terrains, highlighting the necessity of incorporating karst hydrogeology into regional earthquake response planning. Full article

Cities need emergency-shelter systems that are computationally efficient, socially fair, and consistent with long-term goals for sustainable urban development. This paper proposes a GIS-integrated, two-stage location–allocation framework for urban earthquakes that jointly optimizes shelter siting and evacuee assignment under time-varying demand. The model incorporates equity constraints that cap extreme travel burdens for vulnerable groups and robust capacity safeguards against demand uncertainty, helping prevent over- or under-investment in shelter infrastructure and promoting efficient use of land and public resources. A customized Phased Nested Local Search (PNLS) heuristic enables city-scale application and is benchmarked against a mixed-integer programming baseline solved by CPLEX. In a district-level case study of Chengdu, China, the framework reduces total assignment distance by 12.3% and the 95th-percentile travel burden by 15.8% while maintaining feasibility during the peak demand window. The results show that integrating equity, robustness, and spatial efficiency in shelter planning can strengthen urban resilience and directly support SDG 11 on sustainable cities and communities. Full article

The objective of this study is to develop an artificial intelligence algorithm that can predict both the risk level and damage level of reinforced concrete structures through classification and proportioning. This algorithm identifies buildings that require preventive measures before an earthquake and buildings that require immediate repair or demolition after an earthquake. A key aspect of the approach is calculating each building’s risk level as the ratio of its risky story to the total number of stories. That calculation provides a normalized figure, enabling comparison between buildings of varying sizes and complexities in an equitable way. The dataset of this study includes 100 buildings affected by previous earthquakes in Türkiye and 782 buildings with detailed seismic analysis. Thirteen different building parameters, structural, seismic, and geometric, have been considered within the scope of this study. Rapid visual screening (RVS) methods were applied for structural integrity analysis, and machine learning models were used for improvement in accuracy and efficiency. In the comparison of the model sets, the approach achieved the highest accuracy of 77% with an ensemble of four models. The results demonstrate the value of blending AI with traditional methodologies for risk analysis. It shows a viable and scalable mechanism for prioritization of retrofit and inspections and helps engineers and policymakers enhance disaster preparedness. By identifying structures at high risk, this work contributes towards overall aims for earthquake resilience in buildings. This study introduces a Pearson-correlation-based feature analysis and a Random Oversampling strategy to enhance model balance. The ensemble model achieved 83% external accuracy and outperformed the traditional RVS method (68%), reducing computation time from minutes to seconds. Full article

This study examines how Bulgarian municipalities plan for disasters through the analysis of their municipal disaster protection plans’ public availability. These documents are legally mandated and form the cornerstone of local prevention, preparedness, response, and recovery. The research combined a systematic search for publicly accessible plans across all 265 municipalities with a detailed review of the plans from the 27 regional centers. A GIS dataset was constructed linking municipalities with plan availability, population data, and direct links to documents. The analysis revealed that while most municipalities publish disaster-related documentation, accessibility remains uneven and many documents are hidden in poorly organized websites or uploaded as scanned image-only PDFs, limiting usability. Structural analysis of regional center plans showed that all cover the legally required hazards of earthquake, flood, and nuclear or radiological accidents, but the depth, clarity, and inclusion of additional risks vary widely. Only a few municipalities integrate climate change and emerging hazards, while most remain focused on traditional risks. The findings point to a gap between formal compliance with the Disaster Protection Act and effective public-oriented disaster planning. Full article

Disaster management requires the development of effective technologies for managing both pre-and post-disaster processes. Therefore, utilizing effective tools and techniques to mitigate the disaster risks or lower the adversarial impacts is essential. Over the last decade, digital twin (DT) applications have found a wider implementation area for varying purposes, but most importantly, they are utilized for simulating disaster impacts. This study aims to develop an open-source digital twin (DT) framework for earthquake disaster management in the Cayirova district of Kocaeli, Türkiye, one of the country’s most seismically active regions. The primary objective is to enhance local resilience by integrating multi-source data into a unified digital environment that supports risk assessment, response planning, and recovery coordination. The digital model developed using QGIS (3.40.9 Bratislava), Autodesk InfraWorks 2025 software for DT modeling integrates various data types, including geospatial, environmental, transportation, utility, and demographic data. As a result, the developed model is expected to be used as a digital database for disaster management, storing both geospatial and building data in a unified structure. The developed model also aims to contribute to sustainable practices in cities, where disaster risks are particularly critical. In this respect, the developed model is expected to create sustainable logistics chains and sustainable targets aiming to reduce the number of people affected by disasters, reducing the direct economic losses caused by disasters. In this framework, the developed model is expected to further assess seismic risk and mitigate risks with DTs. These capabilities enable the project to establish an open-source district-level DT system implemented for the first time in Cayirova, provide an alternative disaster model focused on region-specific earthquakes, and integrate 2D/3D assets into an operational, ready-to-respond digital database. In terms of practical importance, the model provides a digital database (digital backup) that can be used in emergencies, helping decision-makers make faster, data-driven decisions. The significance of this study lies in bridging the gap between urban digitalization and disaster resilience by providing a scalable and transparent tool for local governments. Ultimately, the developed DT contributes to sustainable urban management, enhancing preparedness, adaptive capacity, and post-disaster recovery efficiency. Full article

Background and Objectives: To assess the impact of the February 2023 earthquake in Turkey on asthma patients’ clinical outcomes and healthcare use. Materials and Methods: This retrospective, single-center study included 280 asthma patients followed at an outpatient clinic between January 2022 and December 2023. Clinical assessments included physical examinations, pulmonary function tests (PFTs), chest X-rays, and, when indicated, skin prick tests (SPTs) for aeroallergen sensitivity. Results: Following the earthquake, outpatient visits for asthma significantly increased from 82 to 198 patients (p < 0.001), and hospitalizations due to asthma attacks rose markedly (p < 0.001). While respiratory function parameters did not differ significantly between periods, there was a significant increase in the number of patients requiring advanced treatment (p = 0.037). Concurrently, air quality deteriorated, with substantial increases in particulate matter (PM₁₀) and sulfur dioxide (SO₂) levels recorded post-earthquake. Conclusions: The earthquake was associated with a significant rise in asthma exacerbations and healthcare utilization, likely driven by environmental pollution, poor living conditions, and disruptions in healthcare services. Disaster preparedness is key to protecting respiratory health after major earthquakes. Full article

Landslides represent a recurrent hazard in tropical mountain environments, where rapid urbanization and extreme rainfall amplify disaster risk. The Sentani region of Papua, Indonesia, is highly vulnerable, as demonstrated by the catastrophic debris flows of March 2019 that caused fatalities and widespread losses. This study developed high-resolution landslide susceptibility maps for Sentani using an ensemble machine learning framework. Three base learners—Random Forest, eXtreme Gradient Boosting (XGBoost), and CatBoost—were combined through a logistic regression meta-learner. Predictor redundancy was controlled using Pearson correlation and Variance Inflation Factor/Tolerance (VIF/TOL). The landslide inventory was constructed from multitemporal satellite imagery, integrating geological, topographic, hydrological, environmental, and seismic factors. Results showed that lithology, Slope Length and Steepness Factor (LS Factor), and earthquake density consistently dominated model predictions. The ensemble achieved the most balanced predictive performance, Area Under the Curve (AUC) > 0.96, and generated susceptibility maps that aligned closely with observed landslide occurrences. SHapley Additive Explanations (SHAP) analyses provided transparent, case-specific insights into the directional influence of key factors. Collectively, the findings highlight both the robustness and interpretability of ensemble learning for landslide susceptibility mapping, offering actionable evidence to support disaster preparedness, land-use planning, and sustainable development in Papua. Full article

This phenomenological study investigates how individuals from diverse marginalized backgrounds in Portland, Oregon, perceive barriers and facilitators related to disaster management, response, and recovery, specifically in the case of an earthquake. Drawing on semi-structured interviews with 45 participants, the study explores how social, cultural, and structural factors shape perceived barriers and facilitators related to disaster preparedness among historically marginalized communities. Reflexive thematic analysis and independent coding were used to identify key patterns in the data. Four key themes emerged from the analysis: (1) Disaster events could exacerbate existing service gaps for specific groups. (2) The privilege of mobility creates unequal access to emergency services. (3) Delays in recovery efforts disproportionately impact vulnerable populations. (4) Persistent concerns exist around the inclusiveness and trust in disaster management. These findings suggest that disaster planning must account for systemic social barriers in addition to infrastructure needs, to ensure equitable access to resources for all. The study highlights the value of participatory, community-informed strategies that can reduce vulnerabilities and foster trust. By illustrating the intersection of disaster preparedness with systemic inequality, the research contributes to broader discussions of urban resilience and offers insights to inform more inclusive emergency planning in high-risk urban environments. Full article

Seismic hazards in Thailand are frequently overlooked in disaster management planning, leading to insufficient research and significant economic losses during earthquake events. The 2014 Chiang Rai earthquake exposed critical vulnerabilities in Thailand’s building practices due to widespread non-compliance with building codes and limited preparedness. This exposure prompted the development of empirical vulnerability functions using loss data from 15,031 damaged residences. The study analyzed government compensation records, which were standardized using replacement cost metrics. Three distinct models were developed through probabilistic and possibilistic modeling approaches. Residual analysis demonstrated the superior performance of the possibilistic approach, with the Possibilistic-based Vulnerability Function achieving a 49.84% reduction in residuals for small loss predictions compared to probability-based models. The research findings indicate that possibility theory—capable of addressing multiple uncertainties—provided a more accurate representation of the observed losses. These results offer valuable guidance for enhancing seismic risk assessment and disaster preparedness strategies in local applications. Full article

Greece faces increasing exposure to natural hazards—particularly wildfires, floods, and earthquakes—driven by climatic, environmental, and spatial factors. This study systematically reviews 108 peer-reviewed publications and official reports, applying PRISMA methodology to evaluate the effectiveness of the national civil protection system. The analysis reveals localized progress, notably in earthquake preparedness due to strict building codes and centralized oversight, but also persistent systemic weaknesses. These include fragmented governance, coordination gaps across agencies, insufficient integration of spatial planning, limited local preparedness, and reactive approaches to disaster management. Case studies of major events, such as the 2018 Mati wildfires and 2023 Thessaly floods, underscore how communication breakdowns and delayed evacuations contribute to substantial human and economic losses. Promising developments—such as SMS-based early warning systems, joint training exercises, and pilot GIS risk-mapping tools—illustrate potential pathways for improvement, though their application remains uneven. Future priorities include strengthening unified command structures, enhancing prevention-oriented planning, investing in interoperable communication systems, and fostering community engagement. The findings position Greece’s civil protection as structurally capable of progress but in need of sustained, systemic reforms to build a resilient, prevention-focused framework for increasing disaster risks. Full article