Search Results (554)

Wildfire occurrence in temperate Europe is increasingly shaped by the interaction of human activities and short-term climatic anomalies rather than by natural ignition processes alone. This study analyses national wildfire statistics from Slovakia covering the period 2010–2025 to investigate temporal trends in wildfire occurrence, ignition causes, and fire-related impacts, including economic damages and human casualties. Official fire records provided by the Fire Research Institute of the Ministry of the Interior of the Slovak Republic were analyzed using descriptive and exploratory statistical methods. The dataset includes annual information on wildfire frequency, detailed ignition cause classifications, direct economic losses, fatalities, and injuries. European-scale wildfire patterns were considered for contextual comparison using data from the European Forest Fire Information System (EFFIS). Results show that wildfire occurrence in Slovakia is overwhelmingly dominated by human-caused ignitions, with negligence-related activities forming a persistent baseline of ignition pressure throughout the study period. The extreme wildfire year 2012, during which more than 11,000 wildfire events were recorded, illustrates how routine human behaviors can be strongly amplified under climatically favorable conditions without altering the underlying cause structure. Importantly, wildfire impacts were found to be weakly correlated with fire frequency, as years with moderate numbers of fires occasionally generated disproportionately high economic damages and casualties. These findings demonstrate that wildfire risk in Slovakia is primarily driven by behavioral ignition patterns modulated by short-term climatic variability. The results support a shift towards prevention-oriented and impact-focused wildfire risk management strategies, consistent with current European policies emphasizing integrated risk assessment, early warning, and targeted prevention in temperate regions. Full article

This manuscript is a continuation of the research published in Fire 2025, 8(8), 302, i.e., it deals with the examination of the cause-and-effect relationships of fires in the Republic of Serbia from the aspect of human safety. Among others, variables related to gender, age, and severity of injuries caused by fires are introduced, on which various methods of statistical analysis and stochastic modeling are first applied. Continuous age variables are modelled using the flexible Generalized Additive Models for Location, Scale, and Shape (GAMLSS) framework, where the Generalized Normal Distribution (GND) is identified as the optimal generative model for injuries, while a Reflected Log-Normal Distribution with positive support (RefLOGND+) provides the best fit for fatalities. The quality of such modeling is formally verified, and the probabilities of injury and death of individuals in certain age categories are predicted, revealing a pronounced concentration of injuries in the working-age population and a markedly higher relative risk of fatal outcomes among elderly individuals. Thereafter, by applying certain Data Mining (DM) techniques, primarily the Apriori algorithm, the most frequently occurring association rules are found, which indicate typical patterns and demographic structure of injuries and deaths in fires in Serbia. Finally, using the CART (Classification and Regression Trees) algorithm, several decision trees are formed that describe the impact and relationship of different causes of fires on injury and death in fires. In this way, some important and frequent patterns are observed that indicate key fire risk factors that significantly affect the demographic structure of human casualties. The results thus obtained provide a basis for developing targeted strategies for fire prevention and improving emergency response planning. Full article

Forest fires are becoming increasingly prevalent and destructive in many regions of the world, posing significant threats to biodiversity, ecosystems, human settlements, climate, and the economy. The United States of America (USA), Australia, Canada, Greece and Portugal are five regions that have experienced enormous forest fires. One way to reduce the size and rage of forest fires is by decreasing the amount of flammable material in forests. This can be achieved using autonomous Unmanned Ground Vehicles (UGVs) specialized in vegetation cutting and equipped with Artificial Intelligence (AI) algorithms to identify and differentiate between vegetation that should be preserved and material that should be removed as potential fire fuel. In this paper, an innovative study of forest vegetation detection, classification and 3D localization using ground vehicles’ RGB and depth images is presented to support autonomous forest cleaning operations to prevent fires. The presented work, which is a continuation of a previous research, presents a method for 3D objects localization in the real-world using Deep Learning Object Detection (DLOD) combined with an RGB-D camera. It presents and compares results of eight recent high-performance DLOD architectures, YOLOv5, YOLOv7, YOLOv8, YOLO-NAS, YOLOv9, YOLOv10, YOLO11 and YOLOv12, to detect and classify forest vegetation in five classes: “Grass”, “Live vegetation”, “Cut vegetation”, “Dead vegetation”, and “Tree-trunk”. For the training of the DLOD models, our custom dataset acquired in dense forests in Portugal is used. A methodology that combines the best DLOD trained for vegetation detection and classification and an RGB-D camera for the 3D localization of the classified detected objects in the real-world. The presented methods are employed in an Unmanned Ground Vehicle (UGV) to localize forest vegetation that needs to be thinned for fire prevention purposes. A key challenge for autonomous forest vegetation cleaning is the reliable discrimination of objects that need to be identified to reach the goal of fire prevention using autonomous unmanned ground vehicles in dense forests. With the obtained results, forest vegetation is precisely detected, classified and localized using the DL models and the localization method presented. Also, the fastest DLOD architecture to train is YOLOv5, and the fastest to infer are YOLOv7 and YOLOv12. The innovation presented is the detection, classification, and 3D localization of the vegetation using DLOD architectures, in real-time, with a localization error of the real-world object in width, height and depth under 21.4, 20.7 and 11%, respectively, using only a depth camera and a processing unit. The 3D localized objects are defined as parallelepiped geometrical shapes. The methodology for vegetation detection, classification and localization presented in this paper is highly suitable for future autonomous forest vegetation cleansing, specialized using unmanned ground vehicles. Full article

Satellite remote sensing plays an essential role in wildfire monitoring due to its large-scale observation capability. However, fire targets in satellite imagery are typically extremely small, sparsely distributed, and embedded in complex backgrounds, making accurate segmentation highly challenging for existing methods. To address these challenges, this paper proposes a multi-scale Mamba-based network for tiny fire segmentation, named FireMambaNet. The network adopts a nested U-shaped encoder-decoder architecture, primarily consisting of three modules: the Cross-layer Gated Residual U-shaped module (CG-RSU), the Fire-aware Directional Context Modulation module (FDCM), and the Multi-scale Mamba Attention Module (M2AM). The CG-RSU, as the core building block, adaptively suppresses background redundancy and enhances weak fire responses by extracting multi-scale features through cross-layer gating. The FDCM explicitly enhances the network’s ability to perceive anisotropic expansion features of fire points, such as those along the wind direction and terrain orientation, by modeling multi-directional context. The M2AM model employs a Mamba state-space model to suppress background interference through global context modeling during cross-scale feature fusion, while enhancing consistency among sparsely distributed tiny fire targets. In addition, experimental validation is conducted using two subsets from the Active Fire dataset, which have significant pixel-level sparse features: Oceania and Asia4. The results show that the proposed method significantly outperforms various mainstream CNN, Transformer, and Mamba baseline models on both datasets. It achieves an IoU of 88.51% and F1 score of 93.76% on the Oceania dataset, and an IoU of 85.65% and F1 score of 92.26% on the Asia4 dataset. Compared to the best-performing CNN baseline model, the IoU is improved by 1.81% and 2.07%, respectively. Overall, the FireMambaNet demonstrates significant advantages in detecting tiny fire points in complex backgrounds. Full article

Accurately assessing the accessibility of fire services is critical for enhancing urban safety and the resilience of the built environment. However, existing studies often lack a systematic analysis of spatiotemporal dynamics across an entire municipality. To address this gap, this study develops a citywide dynamic assessment framework for Shanghai, integrating GIS with real-time traffic data across 240 consecutive intervals to assess the service accessibility of 195 fire stations in relation to 7973 key units of fire safety. The principal findings are threefold. First, the results reveal significant urban–suburban heterogeneity in emergency response times. Notably, the proximity advantage of fire stations in central urban areas is offset by traffic congestion, and the marginal benefit of traffic speed improvement exhibits a sharp decline once the average speed exceeds a critical threshold of 13.7–21.0 km/h. Second, the accessibility ratio demonstrates a clear temporal pattern, being highest on holidays and lowest during weekday peak hours, and follows a nonlinear spatial decline from the urban centre to the periphery. This pattern is influenced more critically by the matching of supply and demand than by fire station density alone. Third, the analysis identifies dynamic vulnerability hotspots, which display a ‘bimodal (M-shaped)’ pattern on weekdays and a ‘unimodal (A-shaped)’ pattern on weekends and holidays. This spatiotemporal mismatch shows that central urban areas, despite higher station density, can suffer from both high fire risk and low accessibility, revealing structural patterns consistent with the ‘Inverse Care Law’ in emergency service provision. This study concludes that merely improving traffic conditions is insufficient; optimising the spatial matching of resources is paramount for effective urban disaster prevention. By developing a refined dynamic assessment framework, this study advances current knowledge by focusing on demand locations consistent with actual fire regulatory priorities and examining spatiotemporal patterns across both urban and suburban areas, thereby providing quantitative, evidence-based support for the strategic planning of fire stations and the enhancement of infrastructure resilience. Full article

Anthropogenic wildfires account for the majority of wildfire ignitions in human-dominated landscapes, yet their spatial drivers remain insufficiently understood at national scales. This study aims to identify key factors influencing anthropogenic wildfire occurrence and to develop a robust and interpretable prediction framework using nationwide data from South Korea. Wildfire occurrence records from 2011–2021 were integrated with daily meteorological, environmental, and socio-economic variables at a 1 km grid resolution. A stacking ensemble model combining Random Forest, XGBoost, LightGBM, Extra Trees, and logistic regression was implemented to improve predictive robustness under rare-event conditions. Model performance was evaluated using ROC–AUC, PR–AUC, and threshold-optimized F1-scores, and variable contributions were interpreted using feature importance and SHAP analyses. The ensemble model achieved a PR–AUC of 0.934 and an ROC–AUC of 0.941. Relative humidity and maximum temperature were identified as influential meteorological variables, while human-accessibility-related variables, particularly distance to roads and agricultural land, showed consistently high contributions to spatial ignition probability. These findings indicate that anthropogenic wildfire occurrence is shaped by interactions between fire-weather conditions and spatial patterns of human accessibility. The proposed framework provides a scalable approach for understanding anthropogenic wildfire mechanisms and supporting prevention strategies in forested landscapes. Full article

The aim of this research is to investigate and reduce the torque ripple in Switched Reluctance Motor (SRM) drives, which is one of the major barriers to their acceptance for electric vehicle propulsion applications despite the advantages of robustness, efficiency, and wide operating range. High torque ripple not only deteriorates drive smoothness but also contributes to noise and vibration, demanding an advanced control strategy beyond traditional current-shaping and switching-based approaches. In this context, this work proposes a DDPG (Deep Deterministic Policy Gradient) Actor–Critic Neural Network-based reinforcement learning control framework that learns the optimal firing angle offsets dynamically to ensure less ripple electromagnetic torque under varying speeds and load conditions. The developed strategy has been designed and trained in MATLAB Simulink R2024b and then deployed in real time using an FPGA-based digital controller for validation on hardware. Comparative analysis with TSF (Torque Sharing Function) and DITC (Direct Instantaneous Torque Control) demonstrates that the reinforcement learning approach gives a much smoother torque response with better dynamic behavior over the operating range analyzed. Full article

In this study, the comparative residual performance of fire-exposed reinforced concrete (RC) beams with rectangular and T-shaped cross-sections is investigated. Two concrete beams, one with a T-section and the other with a rectangular section, were tested under the combined effects of fire exposure and structural loading. Data generated in the tests during and following fire exposure is utilized to compare the thermal and structural response of the beams. The results indicate a notable difference in the temperature evolution, mid-span deflection, and the residual capacity of the beams. The T-beam experienced greater deflection and stiffness degradation due to its larger exposed surface area (approximately 17% higher than the rectangular beam) and flange geometry, despite comparable peak rebar temperatures. A simplified approach, based on the maximum concrete and rebar temperatures and corresponding strength reductions, is proposed to evaluate the residual capacity of fire-exposed RC beams. For equal cover depth to reinforcement, peak rebar temperature is unaffected by cross-section shape as long as the web of the T-beam is not slender. T-shaped beams with similar overall depth exhibit greater post-fire strength retention than rectangular beams when the neutral axis lies within the flange. A 20% reduction in the web thickness and a combined reduction of 20% in web and 37% in flange thickness result in a comparable decrease in the flexural capacity to that of the rectangular beams of similar depth, indicating that the flange plays a key role in maintaining post-fire performance. Full article

U-shaped steel−concrete composite beams (USCCBs) have been widely used in civil engineering due to their numerous advantages, including high load-bearing capacity, high rigidity, good ductility, short construction periods, and compatibility with the development of prefabricated buildings. In particular, USCCBs have been increasingly applied to super high-rise buildings and extra-large span bridges. Over the past decade or so, many new types of shear connectors and structural forms for USCCBs have been developed. Meanwhile, significant progress has been achieved in research on the flexural, shear, torsional, and fire-resistance performance of USCCBs, the seismic behavior of beam−column joints, and the strengthening of concrete beams with U-shaped steel casings. Nevertheless, challenges and limitations remain in both experimental research and practical applications. This paper presents a systematic review of recent research advances in USCCBs. Existing problems, development prospects, and future research priorities are comprehensively summarized and discussed, with the aim of further promoting the development and engineering application of USCCBs. Full article

Background/Objectives: Fire is a dominant ecological force in Mediterranean ecosystems, shaping the adaptive traits of forest species such as Pinus brutia. Prescribed burning (also called controlled burning) is the intentional, carefully planned use of fire under specific environmental conditions to manage vegetation and reduce wildfire risk. While morphological and physiological fire adaptations are well-documented, emerging evidence highlights the role of epigenetic mechanisms—such as DNA methylation and histone modifications—in mediating stress responses. Methods: This study investigates genome-wide epigenetic changes in P. brutia following a prescribed burning experiment on Chios Island, Greece. Using methylation-sensitive amplified polymorphism (MSAP) analysis, we compared temporal shifts on epigenetic profiles before and after fire exposure extracting DNA from the same trees. Results: A significant increase in polymorphic epiloci, epigenetic diversity indices, and private epigenetic bands after prescribed burning was revealed, suggesting a stress-induced reprogramming of the epigenome. Concurrent measurements of midday needle water potential indicated an exploratory association between water stress and epigenetic shifts. Furthermore, Fireline Intensity (FI) correlated with epigenetic diversity index signaling an immediate response of the tree. Conclusions: These findings support the hypothesis that fire stress induces epigenetic responses in P. brutia, potentially enhancing resilience to future environmental challenges. Further research is required to address the level of heritability of these epigenetic changes in next generation and connect these indexes with adaptation and sustainability of forest ecosystems. Full article

This study constructs a resilience assessment framework for China’s coal power industry chain from three dimensions—resistance, recovery, and greenness—using provincial panel data from 30 provinces over the period 2015–2022 (240 observations). It empirically examines the nonlinear impact of green electricity consumption on coal power industry chain resilience and explores the underlying mechanisms. The results show that: (1) the resilience of China’s coal power industry chain exhibits a fluctuating upward trend with significant regional disparities, with the central region showing the highest average resilience level; (2) green electricity consumption has a statistically significant inverted U-shaped effect on coal power industry chain resilience, with an estimated turning point at approximately 0.633, indicating that green expansion enhances resilience below this threshold but weakens it beyond this level; (3) mediation analysis reveals that in the early stage, green electricity consumption improves resilience by increasing power source diversity, while excessive expansion reduces resilience by lowering coal-fired power utilization hours; and (4) heterogeneity analysis indicates that the inverted U-shaped relationship is significant in the central and western regions but not in the eastern and northeastern regions. These findings suggest that green electricity consumption should be coordinated with coal power adjustment capacity to ensure a resilient energy transition. Full article

Military–civilian interface zones (MCIZs) adjacent to the Korean Demilitarized Zone (DMZ) represent complex wildfire environments shaped by restricted access, intensive military activities, and adjacent civilian land use. This study develops a spatially explicit wildfire ignition risk assessment framework for the DMZ and Civilian Control Zone (CCZ) in Paju, South Korea, employing Random Forest (RF), Generalized Additive Models (GAM), and Geographically Weighted Regression (GWR) in a complementary analytical design. A dataset of 318 wildfire ignition events (2001–2024), including 78 associated with military activities, was analyzed. The RF model achieved high predictive accuracy (AUC = 0.81), identifying proximity to military training zones, relative humidity, wind speed, and proximity to built infrastructure as dominant ignition drivers. GAM revealed narrow nonlinear thresholds—relative humidity at 13.8%–14.0% and wind speed at 13.5–14.0 m/s—corresponding to peak ignition probabilities. GWR demonstrated pronounced spatial heterogeneity, with military proximity exerting a stronger influence in the eastern and northern sectors, while the meteorological effects varied geographically. Based on these outputs, a unified analytical framework was established in which RF-derived ignition probabilities were interpreted alongside GAM- and GWR-based explanatory layers to provide spatially explicit ignition susceptibility assessments without numerical map fusion. The proposed approach provides a scientifically rigorous and operationally applicable method for quantifying ignition risk in politically sensitive, access-restricted landscapes, offering valuable insights for adaptive wildfire prevention and spatially informed governance of transboundary fire risk. Full article

Coniferous forests account for 78% of the western US forests and store a substantial amount of carbon. Wildfires significantly alter vegetation structure and associated forest carbon stocks. This study evaluates postfire biomass recovery trajectories (1984–2017) and total biomass accumulation in conifer forests that historically experienced low-severity, high-frequency fire regimes in the western US using recently launched Global Ecosystem Dynamic Investigations (GEDI) mission lidar data. All three ecoregions studied, including the Pacific Northwest (PNW), Southern Rockies (SR), and Northern Rockies (NR), show site-specific biomass recovery trajectories shaped by fire severity. The recovery trajectories were characterized by an initial decline and a variable gain with time since fire across the three ecoregions. Regions with low burn severity recovered to the unburned background state within the first three decades, while regions with higher burn severity only recovered in the Northern Rockies after five decades without fire. Moderate- and high-severity burned areas in both SR and PNW exhibited slow declines or sustained low biomass periods following fires, implying potential ecosystem transformation or an arrested state of lower biomass. Time since fire and fire severity were identified as the most significant drivers of postfire biomass recovery, likely because they reflect both reduced seed availability and the process of seedling establishment and regeneration. In addition, distance to unburned area, drought (measured using the Standardized Precipitation Evapotranspiration Index (SPEI)), elevation, and fire size were important drivers of biomass recovery. Our results demonstrate that all three ecoregions experienced a loss of overall biomass (15–23% (+/−40%)), with the largest losses occurring in the areas with high-severity burns (59% (+/−23%)) in the Southern Rockies compared to unburned forests within the first three decades. This study thus confirms GEDI’s ability to assess disturbance-driven vegetation biomass dynamics and provides an open-science methodology that could be utilized for other regions. In conclusion, our study indicates that an increase in fire severity within low-severity, high-frequency fire regimes, beyond historically observed levels, results in greater carbon losses. It is therefore important to consider the effects of increases in fire severity on vegetation recovery trajectories to infer the future carbon potential in these ecosystems. Full article

Páramos, high-mountain tropical ecosystems, are crucial for carbon storage and water regulation for many Andean cities. However, they are subjected to wildland fires that threaten the ecosystem services they provide. Fire activity varies substantially among páramos, making it essential to understand the drivers of this spatial variability. This study evaluates the relative influence of anthropogenic and biophysical factors on fire occurrence in Colombian páramos, analyzing burned area data from 2000 to 2022 using a Random Forest model. Results indicate that fire occurrence is shaped by the interaction between human pressures and biophysical characteristics. Annual precipitation was the most influential predictor: areas with lower mean annual precipitation (<1000–1500 mm/year) were linked to greater burned area. Vegetation cover, assessed using the Normalized Difference Vegetation Index (NDVI), showed a hump-shaped relationship, with intermediate greenness levels (0.13–0.25) being most prone to burning. Anthropogenic factors, especially proximity to buildings and agricultural zones, also had a significant impact. Our results show that fire occurrence in páramos cannot be attributed solely to human pressures but results from the combined effect of anthropogenic and biophysical drivers. Understanding of these interactions underscores the need for socio-ecological perspectives to guide integrated and adaptive management of strategic high-mountain ecosystems. Full article

Wildfire escalation is increasingly threatening ecosystems and communities in Khyber Pakhtunkhwa (KP), Pakistan, particularly in forest and rangeland landscapes where ecological flammability interacts with human activity. While environmental and climatic drivers are well studied, governance factors remain underexplored despite their decisive role in shaping how ecological risk translates into disasters. Regional forests show considerable ecological diversity, including chir pine-dominated stands, mixed temperate conifer forests, broadleaved oak-associated systems, and shrub rangeland mosaics, each differing in fuel structure and fire behavior. Dependence on fuelwood collection, grazing, and forest access further influences ignition probability and fire spread. This study examines how governance failures influence wildfire risk and severity through a Governance-Fire Risk Framework. Governance is treated as a determining institutional condition affecting prevention capacity, regulation of hazardous land use, fuel management, and emergency response effectiveness. A cross-sectional survey of 540 stakeholders from rural (Dir Lower, Dir Upper) and peri-urban districts (Swat, Mansehra, Abbottabad) was analyzed using SPSS (version 26) and AMOS (version 24) (CFA and SEM). Governance failure significantly escalates wildfire risk through delayed emergency response, regulatory non-compliance, political interference, and weak institutional coordination. Institutional preparedness and response capacity reduce risks, whereas corruption intensifies them. Corruption functions through illegal land conversion, diversion of fire management resources, procurement irregularities, nepotistic staffing, and selective enforcement, increasing ignition sources, fuel accumulation, and response delays. Rural districts show stronger governance-fire linkages. Wildfire escalation in KP is governance-driven in interaction with ecological conditions and community dependence on forest resources. Effective mitigation requires anti-corruption measures, rapid response systems, stronger enforcement, and improved preparedness. The study offers a transferable governance-focused framework for wildfire management in fire-prone developing regions. Full article