Search Results (437)

Earthquakes, as one of the most destructive natural disasters, often cause significant casualties and severe economic losses. Accurate prediction of earthquake fatalities is of great importance for pre-disaster prevention and mitigation planning, as well as post-disaster emergency response deployment. To address the challenges of small sample sizes, high dimensionality, and strong nonlinearity in earthquake fatality prediction, this paper proposes an integrated modeling approach (PCA-IWOA-XGBoost) combining Principal Component Analysis (PCA), the Improved Whale Optimization Algorithm (IWOA), and Extreme Gradient Boosting (XGBoost). The method first employs PCA to reduce the dimensionality of the influencing factor data, eliminating redundant information and improving modeling efficiency. Subsequently, the IWOA is used to intelligently optimize key hyperparameters of the XGBoost model, enhancing the prediction accuracy and stability. Using 42 major earthquake events in China from 1970 to 2025 as a case study, covering regions including the west (e.g., Tonghai in Yunnan, Wenchuan, Jiuzhaigou), central (e.g., Lushan in Sichuan, Ya’an), east (e.g., Tangshan, Yingkou), north (e.g., Baotou in Inner Mongolia, Helinger), northwest (e.g., Jiashi in Xinjiang, Wushi, Yongdeng in Gansu), and southwest (e.g., Lancang in Yunnan, Lijiang, Ludian), the empirical results showed that the PCA-IWOA-XGBoost model achieved an average test set accuracy of 97.0%, a coefficient of determination (R²) of 0.996, a root mean square error (RMSE) and mean absolute error (MAE) reduced to 4.410 and 3.430, respectively, and a residual prediction deviation (RPD) of 21.090. These results significantly outperformed the baseline XGBoost, PCA-XGBoost, and IWOA-XGBoost models, providing improved technical support for earthquake disaster risk assessment and emergency response. Full article

Floods are among the most devastating hydrometeorological natural disasters worldwide, causing massive infrastructure and economic loss in low-lying, flood-prone developing countries like Bangladesh. Effective disaster mitigation relies on organized and detailed flood damage information to facilitate emergency evacuation, coordinate relief distribution, and formulate an effective disaster management policy. Nevertheless, the nation confronts considerable obstacles due to insufficient historical flood damage data and the underdevelopment of near-real-time (NRT) flood monitoring systems. This study addresses this issue by developing a replicable methodology for flood damage assessment and NRT monitoring systems. Using the Google Earth Engine (GEE) platform, we analyzed flood events from 2019 to 2023, integrating geospatial layers such as roads, cropland, etc. Analysis of flood events over the five-year period revealed substantial impacts, with 21.60% of the total area experiencing inundation. This flooding affected 6.92% of cropland and 4.16% of the population. Furthermore, 18.10% of the road network, spanning over 21,000 km within the study area, was also affected. This system has the potential to enhance emergency response capabilities during flood events and inform more effective disaster mitigation policies. Full article

The classification of submarine topography and geomorphology is essential for marine resource exploitation and ocean engineering, with wide-ranging implications in marine geology, disaster assessment, resource exploration, and autonomous underwater navigation. Submarine landscapes are highly complex and diverse. Traditional visual interpretation methods are not only inefficient and subjective but also lack the precision required for high-accuracy classification. While many machine learning and deep learning models have achieved promising results in image classification, limited work has been performed on integrating backscatter and bathymetric data for multi-source processing. Existing approaches often suffer from high computational costs and excessive hyperparameter demands. In this study, we propose a novel approach that integrates pruning-enhanced ConDenseNet with label smoothing regularization to reduce misclassification, strengthen the cross-entropy loss function, and significantly lower model complexity. Our method improves classification accuracy by 2% to 10%, reduces the number of hyperparameters by 50% to 96%, and cuts computation time by 50% to 85.5% compared to state-of-the-art models, including AlexNet, VGG, ResNet, and Vision Transformer. These results demonstrate the effectiveness and efficiency of our model for multi-source submarine topography classification. Full article

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

Using LULC change detection analysis, it is possible to identify changes due to urbanization, deforestation, or a natural disaster in an area. As population growth and urbanization increase, real-time solutions for the effects of urbanization on land use are required to assess its implications for food security and livelihood. This study seeks to identify and quantify recent LULC changes in Limbe, Cameroon, and to measure rates of conversion between agricultural, forest, and urban lands between 1986 and 2020 using remote sensing and GIS. Also, there is a deficiency of research employing these data to evaluate the efficiency of LULC satellite data and a lack of awareness by local stakeholders regarding the impact on LULC change. The changes were identified in four classes utilizing maximum supervised classification in ENVI and ArcGIS environments. The classification result reveals that the 2020 image has the highest overall accuracy of 94.6 while the 2002 image has an overall accuracy of 89.2%. The overall gain for agriculture was approximately 4.6 km², urban had an overall gain of nearly 12.7 km², while the overall loss for forest was −16.9 km² during this period. Much of the land area previously occupied by forest is declining as pressures for urban areas and new settlements increase. This study’s findings have significant policy implications for sustainable land use and food security. It also provides a spatial method for monitoring LULC variations that can be used as a framework by stakeholders who are interested in environmentally conscious development and sustainable land use practices. Full article

In October 2024, a DANA (Isolated Depression at High Levels) triggered torrential rains across the Valencian Community, causing 227 deaths, severe infrastructure damage, and economic losses estimated at €17.8 billion. In this context of crisis, startups, despite having fewer resources and less experience than large corporations, played a significant role in crisis communication, shaping public perception and operational continuity. This study explores the communication strategies adopted by startups during and after the disaster, focusing on their activity on Instagram, TikTok, and Facebook between October 2024 and January 2025. Using a mixed-methods approach, we conducted a quantitative analysis of digital discourse through the Fanpage Karma tool, assessing metrics such as engagement, reach, and posting frequency. Sentiment analysis was performed using GPT-4, an advanced natural language processing model, and in-depth interviews with startup representatives provided qualitative insights into reputational impacts. The findings reveal that startups which aligned their discourse with the social context, prioritizing transparency and emotional proximity, enhanced their visibility and credibility. These results underscore how effective crisis communication not only mitigates reputational risk but also strengthens the local entrepreneurial ecosystem through trust-building and social responsibility. Full article

Losses from floods and the wide range of impacts have been at the forefront of hazard-triggered disasters in China. Affected by large-scale human activities and the environmental evolution, China’s defense flood situation is undergoing significant changes. This paper constructs a comprehensive flood disaster risk assessment model through systematic analysis of four key factors—hazard (H), exposure (E), susceptibility/sensitivity (S), and disaster prevention capabilities (C)—and establishes an evaluation index system. Using the Analytic Hierarchy Process (AHP), we determined indicator weights and quantified flood risk via the following formula R = H × E × V × C. After we applied this model to 16 towns in coastal Zhejiang Province, the results reveal three distinct risk tiers: low (R < 0.04), medium (0.04 ≤ R ≤ 0.1), and high (R > 0.1). High-risk areas (e.g., Longxi and Shitang towns) are primarily constrained by natural hazards and socioeconomic vulnerability, while low-risk towns benefit from a robust disaster mitigation capacity. Risk typology analysis further classifies towns into natural, social–structural, capacity-driven, or mixed profiles, providing granular insights for targeted flood management. The spatial risk distribution offers a scientific basis for optimizing flood control planning and resource allocation in the district. Full article

Under the background of global climate change, shallow landslide clusters induced by extreme rainfall are occurring with increasing frequency, causing severe casualties and economic losses. To address this challenge, this study proposes an integrated approach to support both emergency response and long-term mitigation for rainfall-induced shallow landslides. The workflow includes (1) rapid landslide detection based on time-series image fusion and threshold segmentation on the Google Earth Engine (GEE) platform; (2) numerical simulation of landslide runout using the R.avaflow model; (3) landslide susceptibility assessment based on event-driven inventories and machine learning; and (4) delineation of high-risk slopes by integrating simulation outputs, susceptibility results, and exposed elements. Applied to Qugaona Township in Zhouqu County, Bailong River Basin, the framework identified 747 landslides. The R.avaflow simulations captured the spatial extent and depositional features of landslides, assisting post-disaster operations. The Gradient Boosting-based susceptibility model achieved an accuracy of 0.870, with 8.0% of the area classified as highly susceptible. In Cangan Village, high-risk slopes were delineated, with 31.08%, 17.85%, and 22.42% of slopes potentially affecting buildings, farmland, and roads, respectively. The study recommends engineering interventions for these areas. Compared with traditional methods, this approach demonstrates greater applicability and provides a more comprehensive basis for managing rainfall-induced landslide hazards. Full article

Under the frequent occurrence of urban waterlogging disasters globally, underground spaces, due to their unique environmental conditions and structural vulnerabilities, are facing growing flood pressure, resulting in substantial economic losses that hinder sustainable urban development. This study focused on a high-density urban area in China, investigating surface waterlogging conditions under rainfall characteristics as the primary driver of flooding. Focusing on the main nodes—entrances and exits—within the waterlogging disaster chain of underground garages, a risk assessment framework was constructed that encompasses three key dimensions: the attributes of extreme rainfall, the structural characteristics of entrances/exits, and emergency response capacities. Subsequently, a waterlogging risk assessment was conducted for selected underground garages in the study area under a 100-year return period extreme rainfall scenario. The results revealed that the flood depth at entrances/exits and the structural height of entrances/exits are the primary factors influencing flood risk in urban underground garages. Under this simulation scenario, 37.5% of the entrances and exits exhibited varying degrees of flood risk. The assessment framework and indicator system developed in this study provide valuable insights for flood risk evaluation in underground garage systems and offer decision-makers a more scientific and robust foundation for formulating improvement measures. Full article

With the rapid development of the global economy, urban flood events are occurring more frequently. Scientific risk assessment methods are of great significance in reducing the loss of life and property. This study is devoted to developing an integrated urban flood risk assessment approach based on improved structural equation modeling and the extensible matter-element analysis method. Firstly, a flood risk assessment index system containing four dimensions (hazard, exposure, vulnerability, and regional shelter capability) is established according to a hydrological–hydrodynamic model and a literature survey. Subsequently, improved structural equation modeling (ISEM) coupled with Pearson’s correlation coefficient is introduced to determine indicator weights while eliminating correlations among indicator variables, thereby enhancing the accuracy of the weight calculation. Finally, the extensible matter-element evaluation analysis method (EMAM) is employed to conduct the urban flood risk assessment, providing a more scientific evaluation of urban flood risks through the calculation results of the correlation degree between index factors and risk levels. The integrated flood risk assessment approach was applied in the Liwan District in Guangzhou City, China, and the results demonstrated that the novel approach effectively enhances the accuracy of urban flood risk assessment by 23.69%. In conclusion, this research offers a novel and high-precision methodology for risk assessment, contributing to decision-making in disaster prevention and control. Full article

Under the context of global climate change, sea-level rise and frequent storm surge events pose significant challenges to coastal areas. Protecting coastlines from erosion, mitigating socio-economic losses, and maintaining ecosystem balance are critical for the sustainable development of coastal zones. The concept of “living shorelines” based on Nature-based Solutions (NbS) employs near-natural ecological restoration and protection measures. In low-energy coastal segments, natural materials are prioritized, while high-energy segments are supplemented with artificial structures. This approach not only enhances disaster resilience but also preserves coastal ecosystem stability and ecological functionality. This study constructs a coastal vitality evaluation system for Fujian Province, China, using the entropy weight method, integrating three dimensions: protective safety, ecological resilience, and economic vitality. Data from 2010 and 2020 were analyzed to assess the spatiotemporal evolution of coastal vitality. Results indicate that coastal vitality initially exhibited a spatial pattern of “low in the north, high in the center, and low in the south,” with vitality values ranging from 0.20 to 0.67 (higher values indicate stronger vitality). Over the past decade, ecological restoration projects have significantly improved coastal vitality, particularly in central and southern regions, where high-vitality segments increased markedly. Key factors influencing coastal vitality include water quality, cyclone intensity, biological shoreline length, and wetland area. NbS-aligned coastal management strategies and soft revetment practices have generated substantial ecological and economic benefits. To further enhance coastal vitality, region-specific approaches are recommended, emphasizing rational resource utilization, optimization of ecological and economic values, and the establishment of a sustainable evaluation framework. This study provides scientific insights for improving coastal protection capacity, ecological resilience, and economic potential. Full article

Quang Tri, located in the central region of Vietnam, regularly experiences prolonged and extreme rainfall that causes severe and widespread flooding. This has resulted in significant losses in terms of both lives and property. Therefore, an integrated flood risk map is an essential tool for supporting disaster response and prevention efforts, utilizing a multi-criteria analysis approach to assess flood risks. This study proposes a method for constructing flood risk maps for the downstream areas of river basins in Quang Tri Province, based on the combination of the unweighted method by Iyengar and Sudarshan with multi-criteria and spatial analysis. The results indicate that during the historic flood in October 2020, the level of flood risk varied significantly among communes in 10 districts in the downstream areas. Specifically, the Hai Phong, Dien Sanh, Hai Hung, and Hai Quy communes in Hai Lang district had the largest proportion of the highest risk area (level 5), accounting for 3.76% of the total area. The area classified as medium risk (level 3) represented approximately 16.54%. The resulting flood risk map enables Quang Tri authorities to focus disaster prevention and response efforts more effectively on the most vulnerable areas identified, particularly the high-risk communes in Hai Lang district. Full article

Fire events can impact physical and mental health through smoke exposure, evacuation, property loss, and/or other environmental stressors. In this study, we developed community-driven, cross-sectional online surveys to assess public attitudes, health impacts, and protective actions of residents affected by the Tustin hangar fire that burned for 24 days in southern California. Results showed the most frequently reported fire-related exposure concerns (93%) to be asbestos and general air pollution and the most commonly reported mental health impacts to be anxiety (41%), physical fatigue (37%), headaches (33%), and stress (26%). Nose/sinus irritation was the most commonly reported (26.0%) respiratory symptom, while skin- and eye-related conditions were reported by 63.0% and 72.2% of the survey population, respectively. The most commonly reported health-protective actions taken by residents included staying indoors and/or closing doors and windows (67%), followed by wearing face masks (37%) and the indoor use of air purifiers (35%). A higher proportion of low-income residents had to spend money on remediation or other health-protective actions compared to high-income residents. Participants overwhelmingly reported disapproval of their city’s and/or government’s response to the fire disaster. Findings from this study underscore the potential impacts of major pollution events on neighboring communities and offer critical insights to better position government agencies to respond during future disasters while effectively communicating with the public and addressing community needs. Full article

When a disaster occurs, a large number of social media posts on platforms like Weibo attract public attention with their combination of text and images. However, the consistency between textual descriptions and visual representations varies significantly. Consistent multi-modal data are crucial for helping the public understand the disaster situation and support rescue efforts. This study aims to develop a systematic framework for assessing the consistency of multi-modal disaster-related data on social media. This study explored how the congruence between text and image content affects public engagement and informs strategies for efficient emergency responses. Firstly, the Clip (Contrastive Language-Image Pre-Training) model was used to mine the disaster correlation, loss category, and severity of the images and text. Then, the consistency of image–text pairs was qualitatively analyzed and quantitatively calculated. Finally, the influence of graphic consistency on social concern was discussed. The experimental findings reveal that the consistency of text and image data significantly influences the degree of public concern. When the consistency increases by 1%, the social attention index will increase by about 0.8%. This shows that consistency is a key factor for attracting public attention and promoting the dissemination of information related to important disasters. The proposed framework offers a robust, systematic approach to analyzing disaster loss information consistency. It allows for the efficient extraction of high-consistency data from vast social media data sets, providing governments and emergency response agencies with timely, accurate insights into disaster situations. Full article

The northern foothills of the Qinling Mountains, a critical ecological barrier and urban–rural transition zone in China, face intensifying rainstorm–flood disasters under climate extremes and rapid urbanization. This study pioneers a remote sensing-driven, dynamically coupled framework by integrating multi-source satellite data, system resilience theory, and spatial modeling to develop a novel “risk identification–resilience assessment–scenario simulation” chain. This framework quantitatively evaluates the nonlinear response mechanisms of town–village systems to flood disasters, emphasizing the synergistic effects of spatial scale, morphology, and functional organization. The proposed framework uniquely integrates three innovative modules: (1) a hybrid risk identification engine combining normalized difference vegetation index (NDVI) temporal anomaly detection and spatiotemporal hotspot analysis; (2) a morpho-functional resilience quantification model featuring a newly developed spatial morphological resilience index (SMRI) that synergizes landscape compactness, land-use diversity, and ecological connectivity through the entropy-weighted analytic hierarchy process (AHP); and (3) a dynamic scenario simulator embedding rainfall projections into a coupled hydrodynamic model. Key advancements over existing methods include the multi-temporal SMRI and the introduction of a nonlinear threshold response function to quantify “safe-fail” adaptation capacities. Scenario simulations reveal a reduction in flood losses under ecological priority strategies, outperforming conventional engineering-based solutions by resilience gain. The proposed zoning strategy prioritizing ecological restoration, infrastructure hardening, and community-based resilience units provides a scalable framework for disaster-adaptive spatial planning, underpinned by remote sensing-driven dynamic risk mapping. This work advances the application of satellite-aided geospatial analytics in balancing ecological security and socioeconomic resilience across complex terrains. Full article