Search Results (53)

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

Highway infrastructure faces growing exposure to natural hazards, necessitating more proactive and data-driven risk mitigation strategies. This study explores the integration of Disaster Risk Reduction Audits (DRRAs) into the lifecycle of highway infrastructure projects as a structured method for enhancing disaster resilience and operational safety. Using case analyses and scenario-based labor estimation models across design and construction phases, this research quantifies the resource requirements and effectiveness of DRRA application. The results show a statistically significant reduction in disaster occurrence rates in projects where a DRRA was implemented, despite slightly higher labor inputs. These findings highlight the value of adopting phased DRRA implementation as a national standard, with flexibility across different project types and scales. This study concludes that institutionalizing DRRAs, particularly when supported by digital platforms and decision-support tools, can serve as a critical component in transforming traditional infrastructure management into a more resilient and adaptive system. Full article

This study investigates rural settlement patterns and land suitability in Rwanda’s Western Highlands, a mountainous region highly vulnerable to geohazards like landslides and flooding. Its primary aim is to inform sustainable, climate-resilient development planning in this fragile landscape. We employed high-resolution satellite imagery, a Digital Elevation Model (DEM), and comprehensive geospatial datasets to analyze settlement distribution, using Thiessen polygons for influence zones and Kernel Density Estimation (KDE) for spatial clustering. The Analytic Hierarchy Process (AHP) was integrated with the GeoDetector model to objectively weight criteria and analyze settlement pattern drivers, using population density as a proxy for human pressure. The analysis revealed significant spatial heterogeneity in settlement distribution, with both clustered and dispersed forms exhibiting distinct exposure levels to environmental hazards. Natural factors, particularly slope gradient and proximity to rivers, emerged as dominant determinants. Furthermore, significant synergistic interactions were observed between environmental attributes and infrastructure accessibility (roads and urban centers), collectively shaping settlement resilience. This integrative geospatial approach enhances understanding of complex rural settlement dynamics in ecologically sensitive mountainous regions. The empirically grounded insights offer a robust decision-support framework for climate adaptation and disaster risk reduction, contributing to more resilient rural planning strategies in Rwanda and similar Central African highland regions. Full article

The increasing number of natural and man-made disasters registered at the global level is causing a significant amount of damage. This represents one of the main sustainability challenges at the global level. The collapse of the Twin Towers, Hurricane Katrina, and the nuclear accident at the Fukushima power plant are some of the most representative disaster events that occurred at the beginning of the third millennium. These relevant disasters need an enhanced level of preparedness to reduce the gaps between the plan and its implementation. Among these actions, training and exercises play a relevant role because they increase the capability of planners, managers, and the people involved. By focusing on the exposure risk component, the general objective of the research is to obtain quantitative evaluations of the exercise’s contribution to risk reduction through evacuation. The paper aims to analyze serious games using a set of methods and models that simulate an urban risk reduction plan. In particular, the paper proposes a transparent framework that merges transport risk analysis (TRA) and transport system models (TSMs), developing serious game activities with the support of emerging information and communication technologies (e-ICT). Transparency is possible through the explicitation of reproducible analytical formulations and linked parameters. The core framework of serious games is constituted by a set of models that reproduce the effects of players’ choices, including planned actions of decisionmakers and travel users’ choices. The framework constitutes the prototype of a digital platform in a “non-stressful” context aimed at providing more insights about the effects of planned actions. The proposed framework is characterized by transparency, a feature that allows other analysts and planners to reproduce each risk scenario, by applying TRA and relative effects simulations in territorial contexts by means of TSMs and parameters updated by e-ICT. A basic experimentation is performed by using a game, presenting the main results of a prototype test based on a reproducible exercise. The prototype experiment demonstrates the efficacy of increasing preparedness levels and reducing exposure by designing and implementing a serious game. The paper’s methodology and results are useful for policymakers, emergency managers, and the community for increasing the preparedness level. Full article

Background and Objectives: On 6 February 2023, two catastrophic earthquakes struck southeastern Türkiye, affecting over 13 million individuals and causing widespread destruction. While the physical damage was immediate, the psychological consequences—particularly posttraumatic stress disorder (PTSD) and depression—have proven long-lasting. This study aimed to evaluate the severity and course of PTSD symptoms among survivors and to examine the effectiveness of a telepsychiatry-based mental health intervention in a post-disaster setting. Materials and Methods: This naturalistic, observational study included 153 adult participants from the affected regions who underwent at least two telepsychiatry sessions between the first and sixth month post-disaster. Initial screening was conducted using the General Health Questionnaire (GHQ-12), and individuals scoring ≥ 13 were further assessed with the PTSD Checklist—Civilian Version (PCL-C) and the Beck Depression Inventory (BDI). Follow-up evaluations and pharmacological or psychoeducational interventions were offered as clinically indicated. Results: At the one-month follow-up, 94.4% of participants met the threshold for PTSD symptoms (PCL-C > 22) and 77.6% had severe depressive symptoms (BDI > 30). By the sixth month, PTSD symptoms had significantly decreased (mean PCL-C score reduced from 42.47 ± 12.22 to 33.02 ± 12.23, p < 0.001). Greater symptom reduction was associated with higher educational attainment and perceived social support, while prior trauma predicted poorer outcomes. Depression severity emerged as the strongest predictor of chronic PTSD. Conclusions: This study highlights the psychological burden following the 2023 earthquakes in Türkiye and demonstrates the feasibility and potential effectiveness of telepsychiatry in disaster mental health care. Integrating digital mental health services into disaster response systems may help reach vulnerable populations and improve long-term psychological recovery. Full article

This study investigates co-seismic landslides triggered by the 1 January 2024 Mw 7.6 Noto Peninsula earthquake in Japan using LiDAR differentiation and a modified Savage–Hutter model. By analyzing pre- and post-earthquake high-resolution topographic data from 13 landslides in a geologically homogeneous area of the peninsula, we characterized distinct landslide morphologies and dynamic behaviours. Our approach combined static morphological analysis from LiDAR data with simulations of granular flow mechanics to evaluate landslide mobility. Results revealed two distinct landslide types: those with clear erosion-deposition zonation and complex landslides with discontinuous topographic changes. Landslide dimensions followed power-law relationships (H = 7.51L^0.50, R² = 0.765), while simulations demonstrated that internal deformation capability (represented by the μ parameter) significantly influenced runout distances for landslides terminating on low-angle surfaces but had minimal impact on slope-confined movements. These findings highlight the importance of integrating both static topographic parameters and dynamic flow mechanics when assessing co-seismic landslide hazards, particularly for predicting potential runout distances on gentle slopes where human settlements are often located. Our methodology provides a framework for improved landslide susceptibility assessment and disaster risk reduction in seismically active regions. Full article

This study presents relevant elements of seismic resilience strategy containing an innovative digital mapping tool tailored for Bucharest, one of Europe’s most seismically vulnerable areas. The framework integrates seismic resilience indicators and expert input with Bucharest’s seismic micro-zonation map to systematically identify critical relocation areas, including educational institutions, medical facilities, and open spaces for emergency use. A seven-step methodology underpins the strategy: identifying resilience indicators, gathering local data, conducting expert workshops, mapping vulnerable areas, designating emergency open spaces, incorporating educational institutions as shelters, and evaluating the framework through a SWOT (strengths, weaknesses, opportunities, and threats) analysis. The digital mapping tool developed using Google My Maps provides a practical and accessible platform for emergency management professionals and the public, enabling real-time response coordination and informed long-term planning. District 2 is identified as the most vulnerable area due to high population density and peak ground acceleration (PGA), while District 4 faces challenges stemming from limited medical and relocation resources, despite experiencing lower seismic activity. The SWOT analysis demonstrates the tool’s potential as a robust disaster management framework, while highlighting the need for continuous updates, enhanced collaboration, and integration of additional data. This study offers a scalable model for other urban contexts, bridging the gap between strategic planning and operational readiness for seismic risk reduction. Full article

In recent years, secondary schools and university departments related to geography have begun to teach various topics using Geographic Information Systems (GIS). In particular, WebGIS, available online without installing additional software, is recognized as a powerful educational tool for educators and students. However, research on how students perceive and utilize digital maps to understand geographical objects and investigate the complexity of such learning is insufficient. Therefore, we initiated a study to clarify these research questions by implementing Disaster Risk Reduction (DRR) education using a digital hazard map developed with WebGIS technology, focusing on young people in Japan, including secondary school and university students. The results indicate that DRR education using a simple digital map is helpful for a wide range of students regardless of age. Still, some perceive difficulty in learning to use a digital hazard map. Map representation strongly affects students’ interpretation of vulnerable areas. The maps’ layers and functions are more useful when added gradually, corresponding to students’ ability and familiarity with GIS in the initial stage of geography education using maps to prevent students’ negative impressions caused by complex issues and technical problems. Full article

Over the past decades, China has experienced severe compound natural disasters, such as extreme rainfalls, which have led to significant losses. In response to the challenges posed by the lack of a clear investigation process and inadequate comprehensiveness in evaluating the natural disaster chains, this study proposes a comprehensive retrospective simulation strategy for emergency investigation and simulation of heavy rainstorm–flash flood–debris flow chain disasters at the county–town level. The primary aim is to avert potential new chain disasters and alleviate subsequent disasters. This study combines emergency investigation efforts with hydrodynamic models to digitally simulate and analyze compound chain disasters triggered by an extreme rainfall event in the Haihe River regional area, specifically Gaoyakou Valley, Liucun Town, Changping District, Beijing, in July 2023, along with potential new disasters in adjacent regions. The findings indicate that the heavy rainstorm chain disaster on “7.29” resulted from a complex interplay of interrelated natural phenomena, including flash floods, debris flows, urban floodings, and river overflows. Hantai Village has experienced flash flood and debris flow events at different times in this area. Should the rainfall volume experienced in Liucun Town be replicated in the Ming Tombs Town area, approximately 6.2 km² of land would be inundated, leading to damages to 458 residences and impacting around 240 ha of agricultural land. The anticipated release of floodwater from the reservoir would lead to significant impacts on downstream residents and roads. Our research can improve the efficacy of emergency investigations and assessments, which in turn can help with the management and reduction of disaster risks at the grassroots level. Full article

This study examines the integration of disaster risk reduction (DRR) into security and defense studies curricula at Serbian universities, focusing on public and private institutions. As climate change accelerates and natural disasters become more frequent, addressing these risks is critical for national security and sustainable development. This research evaluates the extent of DRR incorporation in curricula and the use of emerging technologies in DRR education. A qualitative analysis of programs at institutions such as the Faculty of Security Studies at the University of Belgrade, the Military Academy, the University of Criminal Investigation and Police Studies, and private universities like Singidunum and Educons University reveals that public institutions have made significant progress. However, private universities still need comprehensive DRR-focused courses and technological integration. This study recommends fostering collaboration between public and private universities, expanding access to the National Simulation Center, and incorporating modern technologies and active learning strategies across curricula to bridge existing gaps. These steps equip future security professionals with the practical skills and interdisciplinary knowledge necessary for effective disaster management in an increasingly complex risk environment. Full article

This study focuses on the development and evaluation of an AI-based digital therapeutic prototype for adolescent mental health management and disaster response. The system integrates real-time monitoring, AI-driven conversation analysis, personalized psychological treatment programs, and multimodal data analysis. An algorithm was developed to detect gaslighting and verbal abuse using a BERT-based classification model, achieving 85% accuracy in gaslighting detection and 87% accuracy in verbal abuse detection. Additionally, a psychological disaster-recovery support module was included, which demonstrated a 30% improvement in users’ stress reduction rates during simulated disaster scenarios. This study demonstrates that digital therapeutic approaches can significantly contribute to early intervention in adolescent mental health issues. Additionally, these approaches provide effective support during disasters. The developed prototype demonstrates the potential of AI and digital technology to innovate mental health management and disaster response strategies. Full article

This study investigates the transformative impact of smart intelligence, leveraging the Internet of Things and edge-cloud platforms in smart urban development. Smart urban development, by integrating diverse digital technologies, generates substantial data crucial for informed decision-making in disaster management and effective urban well-being. The edge-cloud platform, with its dynamic resource allocation, plays a crucial role in prioritizing tasks, reducing service delivery latency, and ensuring critical operations receive timely computational power, thereby improving urban services. However, the current method has struggled to meet the strict quality of service (QoS) requirements of complex workflow applications. In this study, these shortcomings in edge-cloud are addressed by introducing a multi-objective resource optimization (MORO) scheduler for diverse urban setups. This scheduler, with its emphasis on granular task prioritization and consideration of diverse makespans, costs, and energy constraints, underscores the complexity of the task and the need for a sophisticated solution. The multi-objective makespan–energy optimization is achieved by employing a deep reinforcement learning (DRL) model. The simulation results indicate consistent improvements with average makespan enhancements of 31.6% and 70.09%, average cost reductions of 62.64% and 73.24%, and average energy consumption reductions of 25.02% and 17.77%, respectively, by MORO over-reliability enhancement strategies for workflow scheduling (RESWS) and multi-objective priority workflow scheduling (MOPWS) for SIPHT workflow. Similarly, consistent improvements with average makespan enhancements of 37.98% and 74.44%, average cost reductions of 65.53% and 74.89%, and average energy consumption reductions of 29.52% and 24.73%, respectively, by MORO over RESWS and MOPWS for CyberShake workflow, highlighting the proposed model’s efficiency gains. These findings substantiate the model’s potential to enhance computational efficiency, reduce costs, and improve energy conservation in real-world smart urban scenarios. Full article

Lateral cephalometric radiographs are crucial in dentistry and orthodontics for diagnosis and treatment planning. However, their use in forensic identification, especially with burned bodies or in mass disasters, is challenging. AM (antemortem) and PM (postmortem) radiographs can be compared for identification. This study introduces and evaluates a novel algorithm for extracting cranial patterns from digital lateral cephalometric radiographs for identification purposes. Due to the unavailability of AM cephalograms from deceased individuals, the algorithm was tested using pre- and post-treatment cephalograms of living individuals from an orthodontic archive, considered as AM and PM data. The proposed algorithm encodes cranial patterns into a database for future identification. It matches PM cephalograms with AM records, accurately identifying individuals by comparing cranial features. The algorithm achieved an accuracy of 97.5%, a sensitivity of 97.7%, and a specificity of 95.2%, correctly identifying 350 out of 358 cases. The mean similarity score improved from 91.02% to 98.10% after applying the Automatic Error Reduction (AER) function. Intra-observer error analysis showed an average Euclidean distance of 3.07 pixels (SD = 0.73) for repeated landmark selections. The proposed algorithm shows promise for identity recognition based on cranial patterns and could be enhanced with artificial intelligence (AI) algorithms in future studies. Full article

Inclusive green growth is an effective strategy for achieving sustainable development in rural areas. In the digital economy era, it is crucial to examine whether rural digital development and inclusive green growth can be harmoniously integrated. This study investigates the spatial and temporal evolution of the coupled coordination between digital village construction and rural inclusive green growth in China. Utilizing panel data from 30 provinces from 2011 to 2022, we assess development levels using the entropy weighting method and analyze interdependencies with a coupling coordination model. The results indicate an upward trend in coupling coordination, with significant regional disparities, and it is slowly taking on the characteristics of spatial clustering. Economically advanced regions exhibit higher coordination levels, attributed to stronger economic foundations and better fiscal resources, enabling effective investments in digital infrastructure and green growth initiatives. Additionally, factors such as urbanization rate, innovation levels, reduction in natural disasters, increased financial support for agriculture, and improved large-scale operations positively contribute to this coordination. These findings offer insights for targeted regional development strategies, enhancing the synergy between digital transformation and sustainable rural development. Full article

This study aims to analyze the determinants that influence the consumers’ disposition to invest in microgrid technology in the United Arab Emirates (UAE). This research offers valuable insights for policymakers on investors’ motivations to develop strategies to foster microgrid technology adoption through end-user investments leading to a reduction in microgrid high capital cost. The study employed descriptive statistics, correlation, and regression analyses to analyze the responses of a sample of property owners to a quantitative survey. The study examines such variables as strategic alignment, profitability, digitization, renewable energy utilization, CO₂ emission reduction, and disaster recovery readiness. The data collected reveal a moderate level of understanding and cost-awareness of microgrid technology among the respondents, with a mean of 2.46 out of 5. Notably, the data highlight the significant influence of strategic alignment with the UAE’s national energy goals on the respondents’ inclination to invest in microgrids, with a strong positive correlation of 0.942 at the 0.01 level (two-tailed). By comparison, profitability and disaster recovery have a comparatively weaker correlation. Furthermore, based on the data collected during this study, it has been determined that there is a strong value added by the microgrid initiatives considering the UAE’s strategic direction and the positive influence of reduced CO₂. The regression models used were highly significant at F = 85.690. There is an acceptable level of multicollinearity with VIF values ranging from 1.087 to 2.155. UAE Strategy has low collinearity. UAE Strategy emerges as the only significant predictor of willingness to invest (p < 0.001) in the stepwise regression analysis. The analysis shows that villa and townhouse owners are willing to invest in community microgrid given that it is aligned with UAE strategy and leads to CO₂ emissions reduction. Full article