Search Results (289)

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

The manuscript examines the cause-and-effect relationships of fires in the Republic of Serbia over a fifteen-year period, primarily from the aspect of human safety. For this purpose, numerical variables describing the number of injuries and deaths in fires were introduced, on which various analysis and modeling techniques were implemented, which can be viewed in the context of data mining (DM). First, for both observed variables, stochastic modeling of their temporal dynamics was analyzed, and subsequently, cluster analysis of the values of these variables was performed using two different methods. Finally, by interpreting these variables as outputs (objectives) for the classification problem, several decision trees were formed that describe the influence and relationship of different fire causes on situations in which injuries or human casualties occur or not. In that way, several different types of fires have been identified, including rare but deadly incidents that require urgent preventive measures. Key risk factors such as fire cause, location, season, etc., have been found to significantly influence human casualties. These findings provide practical insights for improving fire protection policies and emergency response. Through such a comprehensive analysis, it is believed that some important results have been obtained that precisely describe the specific relationships between the causes and consequences of fires occurring in the Republic of Serbia. Full article

Wildlife–vehicle collisions (WVCs) remain a significant cause of animal mortality worldwide, particularly in regions experiencing rapid road network expansion. During the COVID-19 pandemic, a number of studies reported decreased WVC rates, attributing this trend to reduced traffic volumes. However, the validity of the simplified assumption that “fewer vehicles means fewer collisions” remains underexplored from a mechanistic perspective. This study aims to reevaluate that assumption using two simulation-based models that incorporate both the physics of vehicle movement and behavioral parameters of road-crossing animals. Employing an inverse modeling approach with quasi-realistic traffic scenarios, we quantify how vehicle speed, spacing, and animal hesitation affect collision likelihood. The results indicate that approximately 10% of modeled cases contradict the prevailing assumption, with collision risk peaking at intermediate traffic densities. These findings challenge common interpretations of WVC dynamics and underscore the need for more refined, behaviorally informed mitigation strategies. We suggest that integrating such approaches into road planning and conservation policy—particularly under the European Union’s ‘Vision Zero’ framework—could help reduce wildlife mortality more effectively in future scenarios, including potential pandemics or mobility disruptions. Full article

Rockfalls, among the most common natural disasters, pose risks such as traffic congestion, casualties, and substantial property damage. Guizhou Province, with China’s fourth-longest highway network, features mountainous terrain prone to frequent rockfall incidents annually. Consequently, assessing highway rockfall risks in Guizhou Province is crucial for safeguarding the lives and travel of residents. This study evaluates highway rockfall risk through three key components: susceptibility, hazard, and vulnerability. Susceptibility was assessed using information content and logistic regression methods, considering factors such as elevation, slope, normalized difference vegetation index (NDVI), aspect, distance from fault, relief amplitude, lithology, and rock weathering index (RWI). Hazard assessment utilized a fuzzy analytic hierarchy process (AHP), focusing on average annual rainfall and daily maximum rainfall. Socioeconomic factors, including GDP, population density, and land use type, were incorporated to gauge vulnerability. Integration of these assessments via a risk matrix yielded comprehensive highway rockfall risk profiles. Results indicate a predominantly high risk across Guizhou Province, with high-risk zones covering 41.19% of the area. Spatially, the western regions exhibit higher risk levels compared to eastern areas. Notably, the Bijie region features over 70% of its highway mileage categorized as high risk or above. Logistic regression identified distance from fault lines as the most negatively correlated factor affecting highway rockfall susceptibility, whereas elevation gradient demonstrated a minimal influence. This research provides valuable insights for decision-makers in formulating highway rockfall prevention and control strategies. Full article

Chemical, biological, radiological, and nuclear (CBRN) incidents pose significant challenges requiring swift, coordinated responses to safeguard public health. This is especially the case in densely populated urban areas, where the public is not only at risk but can also be of assistance. Public cooperation is critical to the success of mass decontamination efforts, yet prior research has primarily focused on technical and procedural aspects, neglecting the psychological and social factors driving casualty behaviour. This paper addresses this gap through a narrative literature review, chosen for its flexibility in synthesising fragmented and interdisciplinary research across psychology, sociology, and emergency management. The review identified two primary pathways influencing casualty decision making: rational and affective. Rational pathways rely on deliberate decisions supported by clear communication and trust in responders’ competence, while affective pathways are shaped by emotional responses like fear and anxiety, exacerbated by uncertainty. Trust emerged as a critical factor, with effective —i.e., transparent, empathetic, and culturally sensitive— communication being proven to enhance public cooperation. Cultural and societal norms further shape individual and group responses during emergencies. This paper demonstrates the value of narrative reviews in addressing a complex, multifaceted topic such as casualty behaviour, enabling the integration of diverse insights. By emphasising behavioural, psychological, and social dimensions, the results of this paper offer actionable strategies for emergency responders to enhance public cooperation and improve outcomes during CBRN incidents. 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

Ground subsidence is a significant geotechnical hazard in urban areas, leading to property damage, casualties, and broader societal issues. This study investigates the mechanisms of cavity formation and ground subsidence through laboratory model tests using Korean standard sand and marine clay under controlled conditions. A transparent soil box apparatus was fabricated to simulate sewer pipe damage, with model grounds prepared at various relative densities, groundwater levels, and fines contents. The progression of cavity formation and surface collapse was observed and quantitatively analyzed by measuring the time to cavity formation and ground subsidence, as well as the mass of discharged soil. Results indicate that lower relative density accelerates ground subsidence, whereas higher density increases cavity volume due to greater frictional resistance. Notably, as the fines content increased, a tendency was observed for ground subsidence to be increasingly suppressed, suggesting that cohesive clay particles can limit soil loss under seepage conditions. These findings provide valuable insights for selecting backfill materials and managing subsurface conditions to mitigate ground subsidence risks in urban infrastructure. Full article

With the increasing digitalization of maritime transportation, the demand for structured and interoperable data has grown. While the S-100 framework developed by the International Hydrographic Organization (IHO) provides a foundation for standardizing maritime information, a data model for representing marine casualties has not yet been developed. As a result, past incident data—such as collisions or groundings—remain fragmented in unstructured formats and are excluded from electronic navigational systems, limiting their use in safety analysis and route planning. To address this gap, this paper proposes a data model for structuring and visualizing marine casualty information within the S-100 standard. The model was designed by defining an application schema, constructing a machine-readable feature catalogue, and developing a portrayal catalogue and custom symbology for integration into Electronic Navigational Charts (ENCs). A case study using actual casualty records was conducted to examine whether the model satisfies the structural and portrayal requirements of the S-100 framework. The proposed model enables previously unstructured casualty data to be standardized and spatially integrated into digital chart systems. This approach allows accident information to be used alongside other S-100-based data models, contributing to risk-aware route planning and future applications in smart ship operations and maritime safety services. Full article

Decreases in animal mortality due to wildlife–vehicle collisions have been consistently documented as an environmental effect of human mobility restrictions aimed at containing the spread of the COVID-19 pandemic. In this study, we investigate this phenomenon on the mid-sized Mediterranean island of Lesvos, considering a multi-species group of mammals over a five-year systematic recording of animal casualties. We developed a method to analyze the relationship between actual casualties and risk, drawing inspiration from Markowitz’s theory on multi-asset optimization in economics. Additionally, we treated this phenomenon as a Poisson probabilistic process. Our main finding indicates that the lockdown year diverged markedly in modeled return–risk space, exhibiting a displacement on the order of 10² compared to the multi-year baseline—an outcome that reflects structural changes in risk dynamics, not a literal 100-fold decrease in observed counts. This modeled shift is significantly larger compared to published evidence regarding individual species. The results concerning the vulnerability of specific mammals, analyzed as a Poisson process, underscore the importance of singular events that can overshadow the overall systemic nature of the issue. We conclude that a promising strategy for addressing this problem is for conservationists to integrate animal-friendly measures into general human road safety policies. Full article

The main causes of damage include poor site selection, such as building on fault lines or on fill soil, as well as deficiencies in design, materials, and workmanship. Damage levels are also linked to the economic conditions of the region. In the 1939 earthquake, there were high casualties due to the magnitude of the earthquake, lack of engineering design in traditional structures and unsuitable soil conditions. Similarly, in the 1992 earthquake, unexpected damage occurred due to faulty designs created by inexperienced engineers who lacked sufficient knowledge of the seismic behavior of structures, errors in craftsmanship and workmanship, and unsuitable residential area selection for construction. These problems continue today and put most of the building stock at risk in case of a major earthquake. Seismic steel isolators are used in two new buildings in the city; if they are effective, they should be made mandatory in new construction. Otherwise, consideration should be given to relocating the city to the more stable southern rocky areas, which were unaffected in both 1939 and 1992. Full article

Landslides triggered by extreme rainfall often cause severe casualties and property losses. Therefore, it is essential to accurately assess and predict building vulnerability under landslide scenarios for effective risk mitigation. This study proposed a quantitative framework for vulnerability assessments of structures. It integrated extreme rainfall analysis, landslide kinematic assessment, and the dynamic response of structures. The study area is located in the northern mountainous region of Tianjin, China. It lies within the Yanshan Mountains, serving as a key transportation corridor linking North and Northeast China. The Sentinel-1A satellite imagery consisting of 77 SLC scenes (from October 2014 to November 2023) identified a slow-moving landslide in the region by using the SBAS-InSAR technique. High-resolution topographic data of the slope were first acquired through UAV-based remote sensing. Next, historical rainfall data from 1980 to 2017 were analyzed. The Gumbel distribution was used to determine the return periods of extreme rainfall events. The potential slope failure range and kinematic processes of the landslide were then simulated by using numerical simulations. The dynamic responses of buildings impacted by the landslide were modeled by using ABAQUS. These simulations allowed for the estimation of building vulnerability and the generation of vulnerability maps. Results showed that increased rainfall intensity significantly enlarged the plastic zone within the slope. This raised the likelihood of landslide occurrence and led to more severe building damage. When the rainfall return period increased from 50 to 100 years, the number of damaged buildings rose by about 10%. The vulnerability of individual buildings increased by 10% to 15%. The maximum vulnerability value increased from 0.87 to 1.0. This model offers a valuable addition to current quantitative landslide risk assessment frameworks. It is especially suitable for areas where landslides have not yet occurred. Full article

Fishery bycatch is a significant threat to biodiversity, with birds being frequent casualties. Current research mainly focuses on seabird bycatch in large-scale marine fisheries, while bird bycatch in inland freshwater areas remains poorly understood. Crayfish traps are extensively used in China’s freshwater environments, but their ecological impacts on birds are overlooked due to monitoring difficulties. Through iEcology approaches, we collected and analyzed 146 bird bycatch incidents in crayfish traps from Chinese social media platforms between September 2010 and December 2023. The results revealed 420 identified birds from 62 species (11 orders, 24 families), predominantly omnivorous and carnivorous, while 106 individuals could not be identified. Cases were concentrated in the middle and lower reaches of the Yangtze River, showing significant positive correlations with water area ratio and aquaculture production (p < 0.001). During fishing seasons, the number of cases, species, and individuals were significantly higher (p < 0.001), though mortality rates increased in off seasons. The middle and lower reaches of the Yangtze River are main production areas of red swamp crayfish (Procambarus clarkii) and Chinese mitten crab (Eriocheir sinensis), where intensive use of crayfish traps may increase bird bycatch risk. Despite existing regulations, systematic supervision is needed to minimize ecosystem impacts. Full article

Fire, whether wild or urban, depends on the triad of oxygen, fuel, and heat. Urban fires, although smaller in scale, have devastating impacts, as evidenced by the 2018 wildfire in Mati, Attica (Greece), which claimed 104 lives. The elderly and children are the most vulnerable due to mobility and cognitive limitations. This study applies Grammatical Evolution (GE), a machine learning method that generates interpretable classification rules to predict the consequences of urban fires. Using historical data (casualties, containment time, and meteorological/demographic parameters), GE produces classification rules in human-readable form. The rules achieve over 85% accuracy, revealing critical correlations. For example, high temperatures (>35 °C) combined with irregular building layouts exponentially increase fatality risks, while firefighter response time proves more critical than fire intensity itself. Applications include dynamic evacuation strategies (real-time adaptation), preventive urban planning (fire-resistant materials and green buffer zones), and targeted awareness campaigns for at-risk groups. Unlike “black-box” machine learning techniques, GE offers transparent human-readable rules, enabling firefighters and authorities to make rapid informed decisions. Future advancements could integrate real-time data (IoT sensors and satellites) and extend the methodology to other natural disasters. Protecting urban centers from fires is not only a technological challenge but also a moral imperative to safeguard human lives and societal cohesion. Full article

The enclosed nature of indoor building spaces during fires creates complex fire environments and restricted evacuation routes, substantially elevating the risk of mass casualties. Traditional static evacuation routes not only overlook the complexity of fire scenarios but also fail to satisfy safety requirements for evacuation. To address this issue, this study proposes an enhanced A* algorithm to determine evacuation paths based on dynamic fire risk assessment. A dynamic fire risk assessment model is established using key fire environment parameters (e.g., temperature, visibility, and toxic gas concentration) and their corresponding personnel harm thresholds. This model quantifies fire risks within a discrete space. The A* algorithm is improved by integrating fire risk values and initial direction constraints into its heuristic function and path update strategy, thereby increasing the algorithm’s accuracy and efficiency. Using a subway station fire as a case study, the simulation results indicate that the improved algorithm can update evacuation paths in line with the dynamic evolution of fire risks. It also identifies evacuation routes by balancing fire risk, distance, and initial direction. This approach maintains the original path direction while substantially reducing path risk, achieving an approximate 70% reduction in individual evacuation path risk. This method can guide building fire safety design and the formulation of emergency evacuation plans. It also serves as a reference for path guidance during emergencies. Full article

During the development of coal resources in China, mine bed-separation water damage has become a new type of disaster in recent years, bringing severe casualties and economic losses to mining areas. This study aims to solve the limitations of the existing bed-separation calculation methods. It proposes a new method of bed-separation discrimination based on the bending deflection of rock strata and a spatial volumetric calculation model that considers the development stage of bed separation. The improved stepwise comparison combination method (ISCCM) was combined with the theory of thin elastic plates to determine the developmental stage of the bed separation, which was able to predict the location of the bed separation and its volume more accurately. An example analysis of the 21301 working face in Cui mu Coal Mine, Shaanxi Province, shows that the proposed method exhibits higher accuracy and reliability in predicting the location of bed-separation development and the water inrush risk. The study shows that changes in the morphology of bed-separation development significantly affect the amount of water accumulation, and the traditional calculation method may produce a significant error after long-distance coal mining. This research result helps to improve the early warning ability and management effect of water damage in the mine bed separation. It provides technical support for the safe and efficient production of the mine. Full article