Search Results (841)

Wildfires, which encompass all fires that occur outside urban areas, represent one of the most frequent forms of natural disaster worldwide. This study presents the wildfire occurrence across the territory of Southeastern Europe, covering an area of 800,000 km² (Greece, Romania, Serbia, Slovenia, Croatia, Bosnia and Herzegovina, Montenegro, Albania, North Macedonia, Bulgaria, and Moldova). The research applies geospatial artificial intelligence techniques, based on the integration of machine learning (Random Forest (RF), XGBoost), deep learning (Deep Neural Network (DNN), Kolmogorov–Arnold Networks (KAN)), remote sensing (Sentinel-2, VIIRS), and Geographic Information Systems (GIS). From the geospatial database, 11 natural and anthropogenic criteria were analyzed, along with a wildfire inventory comprising 28,952 historical fire events. The results revealed that areas of very high susceptibility were most prevalent in Greece (10.5%), while the smallest susceptibility percentage was recorded in Slovenia (0.2%). Among the applied models, RF demonstrated the highest predictive performance (AUC = 90.7%), whereas XGBoost, DNN, and KAN achieved AUC values ranging from 86.7% to 90.5%. Through a SHAP analysis, it was determined that the most influential factors were global horizontal irradiation, elevation, and distance from settlements. The obtained results hold international significance for the implementation of preventive wildfire protection measures. Full article

The dead fine fuel moisture content (DFFMC) directly affects forest fire occurrence and spread. Accurate DFFMC prediction is key to estimating forest fire risk and behavior. The well-fitting machine learning (ML)-based meteorological factor regression models are a focus of DFFMC prediction modeling. Nevertheless, this method’s reliance on a considerable amount of training data and limited extrapolation hinders its potential for extensive implementation in practice. To improve the prediction accuracy of the model in the context of limited training data volumes and interspecies and spatial extrapolated predictions, this study proposed a novel DFFMC prediction method based on a knowledge-embedded neural network (KENN). By integrating the partial differential equation (PDE) of the meteorological response of forest fuel moisture content into a multilayer perceptron (MLP), the KENN utilizes prior physical knowledge and posterior observational data to determine the relationship between meteorology and moisture content. Data from Mongolian oak, white birch, and larch were collected to evaluate model performance. Compared with three representative ML algorithms for DFFMC prediction—random forest (RF), long short-term memory networks (LSTM), and MLP—the KENN can efficiently reduce training data volume requirements and improve extrapolation prediction accuracy within the investigated fire season, thereby enhancing the usability of ML-based DFFMC prediction methods. Full article

Forest fires pose escalating threats to ecological security and public safety in Guangdong Province. This study presents a novel machine learning framework for fire occurrence prediction by synergistically integrating multi-source geospatial data. Utilizing Moderate-resolution Imaging Spectroradiometer (MODIS) active fire detections from 2014 to 2023, we quantified historical fire patterns and incorporated four categories of predisposing factors: meteorological variables, topographic attributes, vegetation characteristics, and anthropogenic activities. Spatiotemporal clustering dynamics were characterized via kernel density estimation and spatial autocorrelation analysis. An XGBoost classifier, hyperparameter-optimized through the Sparrow Search Algorithm (SSA), achieved a predictive accuracy of 90.4%, with performance evaluated through precision, recall, and $F_1$-score. Risk zoning maps generated from predicted probabilities were validated against independent fire records from 2019 to 2024. Results reveal pronounced spatial heterogeneity, with high-risk zones concentrated in northern and western mountainous areas, constituting 29% of the provincial territory. Critical driving factors include slope gradient, proximity to roads and rivers, temperature, population density, and elevation. This robust predictive framework furnishes a scientific foundation for spatially-explicit fire prevention strategies and optimized resource allocation in key high-risk jurisdictions, notably Qingyuan, Shaoguan, Zhanjiang, and Zhaoqing. Full article

Peatlands provide essential ecological services but are highly vulnerable to degradation from drainage, leading to greenhouse gas emissions, land subsidence, and increased fire susceptibility. This study investigates peat hydrology and its relationship to fire risk in a fire-prone area in South Sumatra, Indonesia. Groundwater levels and soil moisture were continuously monitored using automated loggers, and recession analysis quantified their rates of decline. Multispectral drone imagery (NDVI, NDWI) over a 44.1-ha area assessed vegetation and surface wetness, while fire occurrences (2019–2024) were analyzed using the Fire Information for Resource Management System (FIRMS). During a 58-day dry period, groundwater depth reached 78.5 cm with a recession rate of 9.68 mm day⁻¹, while soil moisture decreased by 0.00291 m³ m⁻³ per day over 27 consecutive dry days. Drone imagery revealed that unhealthy and dead grass covered nearly 90% of the site, although wetness remained moderate (NDWI = 0.02–0.58). FIRMS data indicated that rainfall below 2000 mm year⁻¹ and prolonged dry spells (>30 days) strongly trigger peat fires. These findings correspond with early-warning model outputs based on soil moisture recession and ignition thresholds. Maintaining a high groundwater level is, therefore, crucial for reducing peat fire vulnerability under extended dry conditions. Full article

Climate change is widely recognized as a significant contributor to both wildfire initiation and spread, conditions such as high temperatures and prolonged droughts facilitating the rapid ignition and propagation of fires. As a result, extreme weather events can trigger fires through lightning strikes with increases in frequency and severity. Despite this, we argue that it is important to distinguish and clarify the concepts of fire occurrence and fire spread, as these phenomena are not directly synonymous in the field of fire ecology. This review examined the published literature to determine if climate factors contribute to fire occurrence and/or spread, and evaluated how well the concepts are used when drawing connections between fire occurrence and fire spread related to climate variables. Using the PRISMA bibliographic analysis methodology, 70 scientific articles were analyzed, including reviews and research papers in the last 5 years. According to the analysis, most publications dealing with fire occurrence, fire spread, and climate change come from the northern hemisphere, specifically from the United States, China, Europe, and Oceania with South America appearing to be significantly underrepresented (less than 10% of published articles). Additionally, despite climatic variables being the most prevalent factors in predictive models, only 38% of the studies analyzed simultaneously integrated climatic, topographic, vegetational, and anthropogenic factors when assessing wildfires. Furthermore, of the 47 studies that explicitly addressed occurrence and spread, 66 percent used the term “occurrence” in line with its definition cited by the authors, that is, referring specifically to ignition. In contrast, 27 percent employed the term in a broader sense that did not explicitly denote the moment a fire starts, often incorporating aspects such as the predisposition of fuels to burn. The remaining 73 percent focused exclusively on “spread.” Hence, caution is advised when making generalizations as climate impact on wildfires can be overestimated in predictive models when conceptual ambiguity is present. Our results showed that, although climate change can amplify conditions for fire spread and contribute to the occurrence of fire, anthropogenic factors remain the most significant factor related to the onset of fires on a global scale, above climatic factors. Full article

Elderly welfare institutions in Taiwan have experienced multiple severe fire incidents, with smoke inhalation accounting for the majority of fatalities. Hot smoke can rapidly propagate through interconnected ceiling spaces, complicating evacuation for residents with limited mobility who depend heavily on caregiving staff and external responders. Field inspections conducted in this study indicate that 82% of residents require assisted evacuation, underscoring the critical role of early detection, staff-mediated response, and effective smoke control. Drawing on disaster management theory, this study examines key determinants of fire safety performance in elderly welfare institutions, where caregiving staff are primarily trained in medical care rather than fire safety. A total of 64 licensed institutions in Tainan City were investigated through on-site inspections, structured checklist-based surveys, and statistical analyses of fire protection systems. In addition, a comparative review of building and fire safety regulations in Taiwan, the United States, Japan, and China was conducted to contextualize the findings. Using the defense-in-depth framework, this study proposes a three-layer fire safety strategy comprising (1) prevention of fire occurrence, (2) rapid fire detection and early suppression, and (3) containment of fire and smoke spread. From a sustainability perspective, this study conceptualizes fire safety in elderly welfare institutions as a problem of risk governance, illustrating how defense-in-depth can be operationalized as a governance-oriented framework for managing fire and smoke risks, safeguarding vulnerable older adults, and sustaining the resilience and continuity of long-term care systems in an aging society. Full article

The frequent occurrence of fires has prompted China to accelerate the development of community fire prevention and emergency management systems. Language, serving both communicative and affective functions by facilitating the flow of information and fostering mutual understanding, runs through the entire process of community fire emergency management. In response to the early-stage nature of this field and the lack of a systematic framework, this study constructs a dynamic capability evaluation system for urban community fire-related emergency language services (FELS) by integrating multi-source and heterogeneous data. First, by adopting a hybrid approach combining dynamic capability theory and text mining, a three-level indicator system is established. Second, based on domain knowledge, quantitative methods and scoring rules are designed for the third-level qualitative indicators to provide standardized input for the model. Third, a weighting and integration framework is developed that simultaneously considers the internal mechanism characteristics and statistical properties of indicators. Specifically, a knowledge-driven weighting approach combining FAHP and fuzzy DEMATEL is employed to characterize indicator importance and interrelationships, while the CRITIC method is used to extract Data-Driven weights based on data dispersion and information content. These knowledge-driven and Data-Driven weights are then integrated through a multi-feature fusion weighting approach. Finally, a linear weighting model is applied to combine the normalized indicator values with the integrated weights, enabling a systematic evaluation of the dynamic capabilities of community FELS. To validate the proposed framework, application tests were conducted in four representative types of urban communities, including internationally developed, aging and vulnerable, newly developed, and economically diverse communities, using fire emergency scenarios as the entry point. The external validity and internal robustness of the proposed model were verified through these tests. The results indicate that the evaluation system provides accurate, objective, and adaptive assessments of dynamic capabilities in FELS across different community contexts, offering a governance-oriented quantitative tool to support grassroots fire prevention and to enhance community resilience. Full article

Wildfire occurrence in arid and semiarid landscapes is increasingly driven by shifts in climatic and biophysical conditions, yet its dynamics remain poorly understood in the mountainous environments of western Saudi Arabia. This study modeled wildfire probabilities across the Aseer, Al Baha, Makkah Al-Mukarramah, and Jazan regions via multisource Earth observation datasets from 2012–2025. Active fire detections from VIIRS were integrated with ERA5-Land reanalysis variables, vegetation indices, and Copernicus DEM GLO30 topography. A random forest classifier was trained and validated via stratified sampling and cross-validation to predict monthly burn probabilities. Calibration, reliability assessment, and independent temporal validation confirmed strong model performance (AUC-ROC = 0.96; Brier = 0.03). Climatic dryness (dew-point deficit), vegetation structure (LAI_lv), and surface soil moisture emerged as dominant predictors, underscoring the coupling between energy balance and fuel desiccation. Temporal trend analyses (Kendall’s τ and Sen’s slope) revealed the gradual intensification of fire probability during the dry-to-transition seasons (February–April and September–November), with Aseer showing the most persistent risk. These findings establish a scalable framework for wildfire early warning and landscape management in arid ecosystems under accelerating climatic stress. Full article

Lightning is the primary natural cause of wildfires in mid- to high-latitude forests, and it is increasing in frequency under climate change. Traditional fire danger forecasts, reliant on standard meteorological data, often fail to capture extreme events and future risk. To address this issue, we integrate extreme climate indices with meteorological, vegetation, soil, and topographic data, and apply four machine learning methods to build probabilistic models for lightning fire occurrence. The results show that incorporating extreme climate indices significantly improves model performance. Among the models, XGBoost achieved the highest accuracy (87.4%) and AUC (0.903), clearly outperforming traditional fire weather indices (accuracy 60%–71%). Model interpretation with SHapley Additive exPlanations (SHAP) further revealed the driving mechanisms and interaction effects of extreme factors. Extreme temperature and precipitation indices contributed nearly 60% to fire occurrence, with growing season length (GSL), minimum of daily maximum temperature (TXn), diurnal temperature range (DTR), and warm spell duration index (WSDI) identified as key drivers. In contrast, heavy precipitation indices exerted a suppressing effect. Compound hot and dry conditions amplified fuel aridity and markedly increased ignition probability. This interpretable framework improves short-term lightning fire prediction and offers quantitative support for risk warning and resource allocation in a warming climate. Full article

Wildfires pose significant threats to the resilience of distribution systems. Furthermore, the phenomenon of global warming is further intensifying their contribution to power outages. Thus, enhancing distribution system resilience against wildfires remains an area of active research. This work presents a probabilistic approach to evaluate the spatio-temporal probability of wildfire occurrence using historical Forest Fire Danger Index (FFDI) data, and its impact on distribution lines and distributed energy resources (DERs) in active distribution networks (ADNs). To enhance system resilience, the deployment of hybrid energy storage systems (HESSs) is assessed, and their effectiveness in mitigating wildfire-induced disruptions is quantified. Furthermore, the proposed probabilistic methodology is compared with two deterministic approaches to demonstrate its superior capability in assessing wildfire risk and resilience improvement. The approach is suitable for large-scale geographical applications, providing a practical framework for resilience assessment and HESS-based mitigation planning in ADNs. Full article

Lightning-ignited wildfires represent a dominant natural disturbance agent in the Greater Khingan Mountains of northeastern China; however, the relationship between their occurrence and lightning characteristics remains insufficiently quantified. This study analyzed cloud-to-ground (CG) lightning data (2019–2024) and 417 lightning-ignited wildfires (2019–2024) using a full-waveform lightning detection network and spatial matching based on the Minimum Distance Method. Lightning activity shows pronounced spatiotemporal clustering, with more than 93% of flashes occurring in summer and a diurnal peak at 15:00. About 74.6% of wildfires ignited within 1 km of a lightning strike, and the holdover time exhibited clear seasonality, peaking in August (≈317 h). Negative CG (−CG) flashes dominated ignition events (56.5% multiple-stroke, average multiplicity = 2.60), and igniting flashes were concentrated within the −10 to −30 kA peak-current range, suggesting a key threshold for ignition. Vegetation type strongly influenced ignition efficiency: cold temperate and temperate coniferous forests recorded the highest lightning and fire counts, while alpine grasslands and sedge meadows showed the highest lightning ignition efficiency (LIE). These findings clarify how lightning electrical properties and vegetation conditions jointly determine ignition probability and provide a scientific basis for improving lightning-ignited wildfire risk monitoring and early-warning systems in boreal forest regions. Full article

Subway stations are enclosed spaces with high passenger density and complex evacuation conditions. Fires in such environments can escalate rapidly and cause severe consequences. This study proposes a dynamic risk assessment model grounded in dual symmetries. The first symmetry is a balanced “Human–Machine–Environment–Management” analytical structure. The second is a coherent model transformation from a Fault Tree (FT) to a Bayesian Network (BN). Shuanggang Station on Nanchang Metro Line 1 serves as a case study. This work establishes a comprehensive evaluation system based on 4 first-level indicators of man–machine–environment–management, 9 secondary indicators, and 27 tertiary indicators. FT analysis identified 117 minimal cuts and 14 minimal paths, pinpointing core risk nodes such as flammable materials and oxidizers, electrical equipment overheating, and fire management deficiencies. The model was then symmetrically converted into a BN using GeNle Academic 4.1 software to support dynamic probability inference. The results show that prevention measures at Shuanggang Station reduce the fire occurrence probability from 0.000249 to 0.00007 (a 71.9% reduction). The probability importance of rescue escape routes is 0.00223. This indicates that the accessibility of rescue routes constitutes a highly sensitive hazard. The symmetric framework and modeling approach offer a scientific basis for targeted fire prevention, control, and evacuation management in the Nanchang Metro and similar stations. The findings support improvements in the safety and resilience of metro operations. Full article

Tree seedling and understory vegetation re-establishment following wildfires is fundamental to landscape recovery but highly variable, depending strongly on biophysical context at small spatial scales. Onsite regeneration surveys and monitoring have been traditionally viewed as a crucial part of sustainable forest management but can be extremely difficult and time-consuming. The objectives of this study were to use a combination of ground measurements and nonparametric hypothesis tests to quantify the ecological relationship between seedling abundance and microclimate by identifying optimal ranges of vapor pressure deficit (VPD) and sun for seedling abundance in postfire environments in the McKenzie River watershed in Oregon. We followed this effort by evaluating how wildfire severity alters these optimal conditions, informing concepts of conifer regeneration under shifting fire regimes in the Pacific Northwest. LOESS modeling, nonparametric statistics, and geospatial analysis quantified the top–down relationship between wildfire severity, site factors, and seedling abundance in our case study. Using LOESS models, optimal VPD ranges were found at 1.1–1.7 kPa and optimal sun ranges were found at 31.2%–47.8% (PAR). Kruskal–Wallis tests were used to compare differences in seedling abundance and optimal VPD and sun ranges (p = 0.027; p = 0.045). Their combined effect on seedling abundance was also evaluated using a Wilcoxon rank-sum test (p = 0.012). Fire severity was not significant to seedling abundance occurrence, but high-severity areas had a higher occurrence of optimal environments. However, given seed source availability, moderate-fire-severity events are still favored for predictable postfire regeneration. These results give insight into the resilience of ecosystems postfire and can be used to assess reforestation needs and monitor forest recovery. Measurements and resulting applications will benefit land managers serving as prefire data for when, inevitably, the next wildfire burns. These concepts can help repair the relationship between humans and wildland fire. Full article

Public surface transport (which includes rail) is considered relatively safe. However, in the event of a fire, conditions for people inside the vehicles deteriorate rapidly, particularly due to the emission of smoke and its toxic components, which make evacuation very difficult and cause panic. The increased fire risk in rail vehicles has increased with the expansion of plastics used in their construction and equipment, which occurred in the second half of the 20th century, along with increased travel speeds and increased passenger interest in this mode of transport. The measures taken in the 1980s to ensure fire safety in the railway industry are undergoing systematic changes resulting from advances in vehicle design and production technologies, the introduction of new power sources, and safety systems. The aim of this article was to summarize the current knowledge regarding fire hazards in rail vehicles and methods for preventing them. The causes of fires occurring in trains are discussed, taking into account also the potential threats resulting from the introduction of alternative power sources. We also present efforts to develop tools for assessing the severity of the threat and then preventing it, including the contribution of the Railway Research Institute to the development of research methods and Polish and European standardization. Passive and active safety measures required by applicable regulations (TSI, EN standards) aimed at limiting the occurrence of fire and minimizing its consequences were described. As part of passive protection measures, the results of tests carried out at the Railway Institute regarding the fire properties of materials according to current European requirements are presented and discussed. The RAMS approach to risk assessment is also described. Furthermore, the impact of these measures on improving fire safety in rolling stock is analyzed, and new challenges that the development of new technologies poses to the railway industry and related fire protection engineering are presented. Full article

As the demand for maritime transportation of power battery shipping containers grows rapidly, the incidence of fire accidents has increased in tandem. However, most studies focus on analyzing fire causes through the thermal runaway mechanism; few analyze fire risk across the full maritime transportation process from a safety science perspective. To fill this gap, based on the thermal runaway mechanism of lithium-ion batteries, this study couples the loading characteristics of shipping containers with maritime operating conditions and employs the Fault Tree (FT) model, Bayesian Network (BN) model, and Attack–Defense Game Theory for investigation. The results are as follows: Starting from three core factors—battery thermal runaway mechanism, scenario characteristics of shipping container maritime transportation, and failure of initial emergency response—and combining the FT model, it qualitatively identified and systematically sorted accident-causing factors. Via the FT-BN conversion criteria and expert assessment results, the fire probability of po’wer battery shipping containers on the target route was calculated to be 35%. According to Attack–Defense Game Theory, two key risk evolution pathways were identified with occurrence probabilities of 3.77% and 4.35%, respectively. Meanwhile, their action mechanisms were elaborated on, and the targeted preventive measures were proposed. This study provides theoretical support and methodological reference for the systematic assessment of fire risks associated with power battery shipping containers in maritime scenarios. Full article