Journal Description
Fire
Fire
is an international, peer-reviewed, open access journal about the science, policy, and technology of fires and how they interact with communities and the environment, published monthly online by MDPI. The Global Wildland Fire Network is affiliated with Fire.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), AGRIS, PubAg, and other databases.
- Journal Rank: JCR - Q1 (Forestry) / CiteScore - Q2 (Forestry)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 18.4 days after submission; acceptance to publication is undertaken in 2.9 days (median values for papers published in this journal in the first half of 2024).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Paper Types: in addition to regular articles we accept Perspectives, Case Studies, Data Descriptors, Technical Notes, and Monographs.
Impact Factor:
3.0 (2023);
5-Year Impact Factor:
3.4 (2023)
Latest Articles
Comprehensive Building Fire Risk Prediction Using Machine Learning and Stacking Ensemble Methods
Fire 2024, 7(10), 336; https://doi.org/10.3390/fire7100336 (registering DOI) - 25 Sep 2024
Abstract
►
Show Figures
Building fires pose a critical threat to life and property. Therefore, accurate fire risk prediction is essential for effective building fire prevention and mitigation strategies. This study presents a novel approach to predicting fire risk in buildings by leveraging advanced machine learning techniques
[...] Read more.
Building fires pose a critical threat to life and property. Therefore, accurate fire risk prediction is essential for effective building fire prevention and mitigation strategies. This study presents a novel approach to predicting fire risk in buildings by leveraging advanced machine learning techniques and integrating diverse datasets. Our proposed model incorporates a comprehensive range of 34 variables, including building attributes, land characteristics, and demographic information, to construct a robust risk assessment framework. We applied 16 distinct machine learning algorithms, integrating them into a stacking ensemble model to address the limitations of individual models and significantly improve the model’s predictive reliability. The ensemble model classifies fire risk into five distinct categories. Notably, although the highest-risk category comprises only 22% of buildings, it accounts for 54% of actual fires, highlighting the model’s practical value. This research advances fire risk prediction methodologies by offering stakeholders a powerful tool for informed decision-making in fire prevention, insurance assessments, and emergency response planning.
Full article
Open AccessArticle
Interannual Variability in Seed Germination Response to Heat Shock in Cistus ladanifer
by
Belén Luna
Fire 2024, 7(10), 334; https://doi.org/10.3390/fire7100334 (registering DOI) - 25 Sep 2024
Abstract
►▼
Show Figures
Mediterranean climates, characterised by hot and dry summers, have predictable fire regimes, and many species with physical seed dormancy (PY) thrive after wildfires. While it is well known that PY is released after heat shock in these species, intraspecific variation in seed response
[...] Read more.
Mediterranean climates, characterised by hot and dry summers, have predictable fire regimes, and many species with physical seed dormancy (PY) thrive after wildfires. While it is well known that PY is released after heat shock in these species, intraspecific variation in seed response to heat is less understood. This research explores, for the first time, the variability in the traits of Cistus ladanifer seeds from the same central Spain population over eight years. It examines seed germination and viability under different heat shocks and the relationships among seed traits and climatic variables. While the response to heat shock remained constant over the years studied, achieving the highest germination percentages after heat shock at 100 °C, seed germination varied between years, and environmental conditions affected seed traits. Seed moisture content was negatively correlated with the maximum summer temperatures, and seed viability was positively related to annual precipitation. Germination at 100 °C was lower in warmer years as more seeds did not break their PY. In conclusion, despite the fact that PY appears to be genetically determined, it also depends on the environmental conditions experienced by the mother plant. This interannual phenotypic variability may help Cistus ladanifer to cope with the increasingly unpredictable conditions imposed by climate change.
Full article
Figure 1
Open AccessArticle
Enhancing Fire Safety Knowledge among Underwater Road Tunnel Users: A Survey in China
by
Chunling Lu, Dingli Liu, Yao Huang, Ying Li, Shanbin Chen, Weijun Liu and Jingya Wang
Fire 2024, 7(9), 333; https://doi.org/10.3390/fire7090333 - 23 Sep 2024
Abstract
In recent years, the number of underwater road tunnels in Chinese cities has increased. However, the current situation of personal fire safety literacy as it pertains to these tunnels remains unclear. To address this gap, a questionnaire survey was conducted to investigate people’s
[...] Read more.
In recent years, the number of underwater road tunnels in Chinese cities has increased. However, the current situation of personal fire safety literacy as it pertains to these tunnels remains unclear. To address this gap, a questionnaire survey was conducted to investigate people’s awareness of escape slides, evacuation signs, and the correct evacuation paths for fire escape. A total of 1049 respondents in Changsha, China, were surveyed, with 791 valid questionnaires collected and analyzed. The findings revealed that a significant proportion of respondents (81.80%) were unaware of the presence of escape slides in underwater road tunnels, while 87.86% could not recognize them and 93.05% could not use them. Only 42.04% of respondents could identify evacuation signs in underwater road tunnels. In the event of a fire, just half of the respondents could select the appropriate escape or evacuation path. Additionally, demographic differences among respondents also influenced their level of fire safety literacy. Based on these findings, it is recommended that the government and relevant organizations should enhance the dissemination of knowledge regarding escape slides and evacuation signs in underwater road tunnels.
Full article
(This article belongs to the Special Issue Evacuation Design and Smoke Control in Fire Safety Management)
►▼
Show Figures
Figure 1
Open AccessArticle
Dehazing Algorithm Integration with YOLO-v10 for Ship Fire Detection
by
Farkhod Akhmedov, Rashid Nasimov and Akmalbek Abdusalomov
Fire 2024, 7(9), 332; https://doi.org/10.3390/fire7090332 - 23 Sep 2024
Abstract
Ship fire detection presents significant challenges in computer vision-based approaches due to factors such as the considerable distances from which ships must be detected and the unique conditions of the maritime environment. The presence of water vapor and high humidity further complicates the
[...] Read more.
Ship fire detection presents significant challenges in computer vision-based approaches due to factors such as the considerable distances from which ships must be detected and the unique conditions of the maritime environment. The presence of water vapor and high humidity further complicates the detection and classification tasks for deep learning models, as these factors can obscure visual clarity and introduce noise into the data. In this research, we explain the development of a custom ship fire dataset, a YOLO (You Only Look Once)-v10 model with a fine-tuning combination of dehazing algorithms. Our approach integrates the power of deep learning with sophisticated image processing to deliver comprehensive solutions for ship fire detection. The results demonstrate the efficacy of using YOLO-v10 in conjunction with a dehazing algorithm, highlighting significant improvements in detection accuracy and reliability. Experimental results show that the YOLO-v10-based developed ship fire detection model outperforms several YOLO and other detection models in precision (97.7%), recall (98%), and [email protected] score (89.7%) achievements. However, the model reached a relatively lower score in terms of F1 score in comparison with YOLO-v8 and ship-fire-net model performances. In addition, the dehazing approach significantly improves the model’s detection performance in a haze environment.
Full article
(This article belongs to the Section Fire Science Models, Remote Sensing, and Data)
►▼
Show Figures
Figure 1
Open AccessArticle
Drivers of Pinus halepensis Plant Community Structure across a Post-Fire Chronosequence
by
Dimitris Kazanis, Sofie Spatharis, Giorgos D. Kokkoris, Panayiotis G. Dimitrakopoulos and Margarita Arianoutsou
Fire 2024, 7(9), 331; https://doi.org/10.3390/fire7090331 - 22 Sep 2024
Abstract
The Pinus halepensis (Aleppo pine) forests prevailing in the western part of the Mediterranean Basin are amongst the most severely affected by fire due to their inherent flammability. Our understanding of the environmental factors driving post-fire community dynamics is currently limited by the
[...] Read more.
The Pinus halepensis (Aleppo pine) forests prevailing in the western part of the Mediterranean Basin are amongst the most severely affected by fire due to their inherent flammability. Our understanding of the environmental factors driving post-fire community dynamics is currently limited by the lack of time-series data at temporal scales. In this present study, we analyzed a chronosequence of Greek Aleppo pine forests spanning a post-fire period of 65 years. Our goal is to explore the role of post-fire age, altitude, exposure, slope level, parent-rock material, rock cover, and cover of evergreen sclerophyllous shrubs (maquis) on plant assemblage diversity (species richness and Menhinick’s diversity index) and composition. Post-fire age had a significant effect on taxonomic distinctness and community turnover but not on species richness. Taxonomic distinctness increased with post-fire age due to a higher prevalence of the families Fabaceae, Asteraceae, and Poaceae during the early post-fire period. Maquis cover was significantly associated with Menhinick’s diversity index, taxonomic distinctness, and community turnover. Exposure and slope influenced only Menhinick’s diversity index. The turnover in species composition was primarily driven by the geographical proximity of the forests and secondarily by post-fire age and the maquis cover. This highlights the importance of the initial floristic composition in the process of autosuccession after a fire in Mediterranean-climate ecosystems.
Full article
(This article belongs to the Special Issue Effects of Fires on Forest Ecosystems)
►▼
Show Figures
Figure 1
Open AccessArticle
Smoke Emissions and Buoyant Plumes above Prescribed Burns in the Pinelands National Reserve, New Jersey
by
Kenneth L. Clark, Michael R. Gallagher, Nicholas Skowronski, Warren E. Heilman, Joseph Charney, Matthew Patterson, Jason Cole, Eric Mueller and Rory Hadden
Fire 2024, 7(9), 330; https://doi.org/10.3390/fire7090330 - 21 Sep 2024
Abstract
►▼
Show Figures
Prescribed burning is a cost-effective method for reducing hazardous fuels in pine- and oak-dominated forests, but smoke emissions contribute to atmospheric pollutant loads, and the potential exists for exceeding federal air quality standards designed to protect human health. Fire behavior during prescribed burns
[...] Read more.
Prescribed burning is a cost-effective method for reducing hazardous fuels in pine- and oak-dominated forests, but smoke emissions contribute to atmospheric pollutant loads, and the potential exists for exceeding federal air quality standards designed to protect human health. Fire behavior during prescribed burns influences above-canopy sensible heat flux and turbulent kinetic energy (TKE) in buoyant plumes, affecting the lofting and dispersion of smoke. A more comprehensive understanding of how enhanced energy fluxes and turbulence are related during the passage of flame fronts could improve efforts to mitigate the impacts of smoke emissions. Pre- and post-fire fuel loading measurements taken during 48 operational prescribed burns were used to estimate the combustion completeness factors (CC) and emissions of fine particulates (PM2.5), carbon dioxide (CO2), and carbon monoxide (CO) in pine- and oak-dominated stands in the Pinelands National Reserve of southern New Jersey. During 11 of the prescribed burns, sensible heat flux and turbulence statistics were measured by tower networks above the forest canopy. Fire behavior when fire fronts passed the towers ranged from low-intensity backing fires to high-intensity head fires with some crown torching. Consumption of forest-floor and understory vegetation was a near-linear function of pre-burn loading, and combustion of fine litter on the forest floor was the predominant source of emissions, even during head fires with some crowning activity. Tower measurements indicated that above-canopy sensible heat flux and TKE calculated at 1 min intervals during the passage of fire fronts were strongly influenced by fire behavior. Low-intensity backing fires, regardless of forest type, had weaker enhancement of above-canopy air temperature, vertical and horizontal wind velocities, sensible heat fluxes, and TKE compared to higher-intensity head and flanking fires. Sensible heat flux and TKE in buoyant plumes were unrelated during low-intensity burns but more tightly coupled during higher-intensity burns. The weak coupling during low-intensity backing fires resulted in reduced rates of smoke transport and dispersion, and likely in more prolonged periods of elevated surface concentrations. This research facilitates more accurate estimates of PM2.5, CO, and CO2 emissions from prescribed burns in the Pinelands, and it provides a better understanding of the relationships among fire behavior, sensible heat fluxes and turbulence, and smoke dispersion in pine- and oak-dominated forests.
Full article
Figure 1
Open AccessArticle
Real-Time Fire Classification Models Based on Deep Learning for Building an Intelligent Multi-Sensor System
by
Youngchan Kim, Yoseob Heo, Byoungsam Jin and Youngchul Bae
Fire 2024, 7(9), 329; https://doi.org/10.3390/fire7090329 - 21 Sep 2024
Abstract
Fire detection systems are critical for mitigating the damage caused by fires, which can result in significant annual property losses and fatalities. This paper presents a deep learning-based fire classification model for an intelligent multi-sensor system aimed at early and reliable fire detection.
[...] Read more.
Fire detection systems are critical for mitigating the damage caused by fires, which can result in significant annual property losses and fatalities. This paper presents a deep learning-based fire classification model for an intelligent multi-sensor system aimed at early and reliable fire detection. The model processes data from multiple sensors that detect various parameters, such as temperature, humidity, and gas concentrations. Several deep learning architectures were evaluated, including LSTM, GRU, Bi-LSTM, LSTM-FCN, InceptionTime, and Transformer. The models were trained on data collected from controlled fire scenarios and validated for classification accuracy, loss, and real-time performance. The results indicated that the LSTM-based models (particularly Bi-LSTM and LSTM) could achieve high classification accuracy and low false alarm rates, demonstrating their effectiveness for real-time fire detection. The findings highlight the potential of advanced deep-learning models to enhance the reliability of sensor-based fire detection systems.
Full article
(This article belongs to the Special Issue Advances in Building Fire Safety Engineering)
►▼
Show Figures
Figure 1
Open AccessArticle
Study on Explosion Mechanism of Dimethyl Ether/H2-Blended Gas Based on Chemical Kinetics Method
by
Yong Zhou, Yang Kong, Qi Zhang, Qi Huang, Zhikai Wei and Huaheng Lu
Fire 2024, 7(9), 328; https://doi.org/10.3390/fire7090328 - 20 Sep 2024
Abstract
►▼
Show Figures
In order to reveal the deflagration mechanism of DME/H2-blended gasses, the micro-mechanism was studied based on the constructed UC San Diego 2016 pyrolysis oxidation mechanism model. The results show that adiabatic flame temperature and laminar flame speed increase with the increase
[...] Read more.
In order to reveal the deflagration mechanism of DME/H2-blended gasses, the micro-mechanism was studied based on the constructed UC San Diego 2016 pyrolysis oxidation mechanism model. The results show that adiabatic flame temperature and laminar flame speed increase with the increase in the equivalence ratio (Φ); they first increase and then decrease with the increase in the hydrogen (H2)-blended ratio (λ), and with the increase in λ, the Φ corresponding to the peak laminar flame speed of the blended gas increases. The addition of H2 increases the consumption of O2, and H2 reacts with CO to form H2O and CO2, promoting complete combustion. When Φ = 1.0–1.2, the equilibrium mole fraction of H and OH-activated radicals reach the maximum, and with the addition of H2, the concentration of activating radicals gradually increases, while the number of promoted elementary reactions increases by two, and the number of inhibited elementary reactions does not increase. Meanwhile, the addition of H2 increases the reaction rate of most reactions on the main chemical reaction path CH3OCH3→CH3OCH2→CH2O→HCO→CO→CO2 of DME and increases the risk of the deflagration of DME/H2-blended gas.
Full article
Figure 1
Open AccessArticle
Research on Wildfires, Soil Erosion and Land Degradation in the XXI Century
by
António Bento-Gonçalves, António Vieira and Sarah Moura dos Santos
Fire 2024, 7(9), 327; https://doi.org/10.3390/fire7090327 - 20 Sep 2024
Abstract
This study carries out a comprehensive bibliometric analysis of scientific production on wildfires, soil erosion and land degradation, with the aim of understanding trends, critical gaps in scientific knowledge and research patterns. A total of 1400 articles published between 2001 and 2023 were
[...] Read more.
This study carries out a comprehensive bibliometric analysis of scientific production on wildfires, soil erosion and land degradation, with the aim of understanding trends, critical gaps in scientific knowledge and research patterns. A total of 1400 articles published between 2001 and 2023 were analyzed with bibliometric tools (Bibliometrix and VOSviewer), revealing a steady growth in the number of publications over time. International collaboration between countries such as the United States, Spain, China and Portugal is evident, highlighting the global approach to tackling these issues, as well as the mobility and collaboration between scientists. Analyzing the conceptual structure through the co-occurrence of keywords reveals central themes such as “soil erosion” and “wildfire”, indicating areas of primary focus in research. This study highlights the continuing importance of these themes and the need for global collaboration to tackle the environmental challenges affecting forest ecosystems, and particularly the soil layer, caused by wildfires, which affect wildlands all over the world.
Full article
(This article belongs to the Special Issue Post-fire Effects on Environment)
►▼
Show Figures
Figure 1
Open AccessArticle
Full-Scale Fire Experiment on Mezzanine Racks in Logistics Facilities
by
Byeongheun Lee, Nam Jeon and Jeongki Min
Fire 2024, 7(9), 326; https://doi.org/10.3390/fire7090326 - 20 Sep 2024
Abstract
The increased demand for contactless services has facilitated a rapid increase in logistics facilities. There are shorter distances between the shelf racks used in mezzansine racks in such facilities compared to standard racks and can store various items; however, research on fire safety
[...] Read more.
The increased demand for contactless services has facilitated a rapid increase in logistics facilities. There are shorter distances between the shelf racks used in mezzansine racks in such facilities compared to standard racks and can store various items; however, research on fire safety related to this remains insufficient. In this study, we visited four logistics facilities with mezzanine racks and one logistics facility using shelf racks to investigate their fundamental characteristics. Considering the characteristics of logistics facilities that store various combustibles, a fire test was conducted using unit shelf racks with packaging materials, boxes, and expandable polystyrene (EPS). Shelf racks loaded with corrugated fiberboard, cardboard boxes, and EPS exhibited the highest fire risk and were set as combustibles inside the rack. Before the experiment, the radiative heat flux was measured by considering the spacing distances of mezzanine racks observed on-site. The most frequently measured range was 43.7–67.3 kW/m2 at 1.0–1.5 m. After beginning the fire experiment, when simulating mezzanine racks with aisle widths of 1.2–2.0 m, fire owing to radiative heat occurred within 5 min in the separated shelf racks. Based on the results, we estimate that the minimum separation distance required to prevent radiant heat-based fires between shelving racks inside a mezzanine is 3.2 m. These findings are expected to be utilized in fire prevention by increasing the understanding of the spread of fire in shelf racks.
Full article
(This article belongs to the Section Fire Risk Assessment and Safety Management in Buildings and Urban Spaces)
►▼
Show Figures
Figure 1
Open AccessArticle
Impact of Forest Fires on the Trees and Wood Quality—A Case Study for a Beech Stand
by
Elena Camelia Mușat
Fire 2024, 7(9), 325; https://doi.org/10.3390/fire7090325 - 18 Sep 2024
Abstract
Wood quality has been an ongoing concern for science, having become increasingly important in the current context, in which the demand for wood is increasing and forest fires are more frequent and violent. This study aims to evaluate the quality of wood in
[...] Read more.
Wood quality has been an ongoing concern for science, having become increasingly important in the current context, in which the demand for wood is increasing and forest fires are more frequent and violent. This study aims to evaluate the quality of wood in trees affected by fires and the negative impact of these phenomena on the speed of wood degradation, as a result of weakening the trees due to the action of stress factors. The study was carried out using modern techniques on beech trees (Fagus sylvatica L.) remaining in an area affected by a litter fire in 2017. Measurements were taken with the Arbotom Rinntech sound tomograph, the IML Resi F-500S resist graph, and the Pressler core sampler to observe the quality of the wood inside the trees. It was found that all the trees were in various stages of decay, the tomograms being able to characterize the severity of decay only in the case of fully decayed wood as a result of the action of xylophages fungi, whose harmful influence is more pronounced when the injuries sustained by the trees are higher. Although the trees attempted to close the fire wounds through their own defense mechanisms, the destructive action of the fungi intensified with time. After the forest fires, for an effective assessment of the wood’s internal quality, the resist graph can be used. For valuable trees, one could use the tomograph, but the measurements have to be taken only by qualified operators.
Full article
(This article belongs to the Special Issue Post-fire Effects on Environment)
►▼
Show Figures
Figure 1
Open AccessArticle
Wildfire Burnt Area and Associated Greenhouse Gas Emissions under Future Climate Change Scenarios in the Mediterranean: Developing a Robust Estimation Approach
by
Tim van der Schriek, Konstantinos V. Varotsos, Anna Karali and Christos Giannakopoulos
Fire 2024, 7(9), 324; https://doi.org/10.3390/fire7090324 - 17 Sep 2024
Abstract
Wildfires burn annually over 400,000 ha in Mediterranean countries. By the end of the 21st century, wildfire Burnt Area (BA) and associated Green House Gas (GHG) emissions may double to triple due to climate change. Regional projections of future BA are urgently required
[...] Read more.
Wildfires burn annually over 400,000 ha in Mediterranean countries. By the end of the 21st century, wildfire Burnt Area (BA) and associated Green House Gas (GHG) emissions may double to triple due to climate change. Regional projections of future BA are urgently required to update wildfire policies. We present a robust methodology for estimating regional wildfire BA and GHG emissions under future climate change scenarios in the Mediterranean. The Fire Weather Index, selected drought indices, and meteorological variables were correlated against BA/GHG emissions data to create area-specific statistical projection models. State-of-the-art regional climate models (horizontal resolution: 12 km), developed within the EURO-CORDEX initiative, simulated data under three climate change scenarios (RCP2.6, RCP4.5, and RCP8.5) up to 2070. These data drove the statistical models to estimate future wildfire BA and GHG emissions in three pilot areas in Greece, Montenegro, and France. Wildfire BA is projected to increase by 20% to 130% up to 2070, depending on the study area and climate scenario. The future expansion of fire-prone areas into the north Mediterranean and mountain environments is particularly alarming, given the large biomass present here. Fire-smart landscape management may, however, greatly reduce the projected future wildfire BA and GHG increases.
Full article
(This article belongs to the Special Issue Effects of Climate Change on Fire Danger)
►▼
Show Figures
Figure 1
Open AccessData Descriptor
Intelligent Fire Suppression Devices Based on Microcapsules Linked to Sensor Internet of Things
by
Jong-Hwa Yoon, Xiang Zhao and Dal-Hwan Yoon
Fire 2024, 7(9), 323; https://doi.org/10.3390/fire7090323 - 17 Sep 2024
Abstract
Most fire spread is caused by the absence of suppression means at the beginning of the fire. This results in the missed golden time. There are various factors that cause initial fires, such as electrical outlets, general distribution circuits, and oil–vapor–gas cluster spaces.
[...] Read more.
Most fire spread is caused by the absence of suppression means at the beginning of the fire. This results in the missed golden time. There are various factors that cause initial fires, such as electrical outlets, general distribution circuits, and oil–vapor–gas cluster spaces. In most cases, these places are out of reach of human hands or they lose the initial suppression time when a fire occurs, causing the spread of fire. This study implements an intelligent fire suppression device that connects sensor IoT based on microcapsules to secure initial fire suppression and golden time in the event of a fire in blind spots that cannot be seen by humans or at a time when it is difficult to recognize a fire. The microcapsule is a micro-collection unit that collects Novec 1230 gas generated in the semiconductor production process. The microcapsule is molded into a form with a fire suppression function and, when a fire occurs, the molded body explodes and absorbs ambient oxygen to suppress the fire. The complex-sensor IoT executes smoke and heat detection generated when a fire is suppressed within 10 s, which ensures the reliability of the detector by notifying of the fire and detecting the ignition point through communication linkages such as Ieee 485 and WiFi or LoRa.
Full article
(This article belongs to the Special Issue Advanced Approaches to Wildfire Detection, Monitoring and Surveillance)
►▼
Show Figures
Figure 1
Open AccessArticle
Advanced Multi-Label Fire Scene Image Classification via BiFormer, Domain-Adversarial Network and GCN
by
Yu Bai, Dan Wang, Qingliang Li, Taihui Liu and Yuheng Ji
Fire 2024, 7(9), 322; https://doi.org/10.3390/fire7090322 - 15 Sep 2024
Abstract
Detecting wildfires presents significant challenges due to the presence of various potential targets in fire imagery, such as smoke, vehicles, and people. To address these challenges, we propose a novel multi-label classification model based on BiFormer’s feature extraction method, which constructs sparse region-indexing
[...] Read more.
Detecting wildfires presents significant challenges due to the presence of various potential targets in fire imagery, such as smoke, vehicles, and people. To address these challenges, we propose a novel multi-label classification model based on BiFormer’s feature extraction method, which constructs sparse region-indexing relations and performs feature extraction only in key regions, thereby facilitating more effective capture of flame characteristics. Additionally, we introduce a feature screening method based on a domain-adversarial neural network (DANN) to minimize misclassification by accurately determining feature domains. Furthermore, a feature discrimination method utilizing a Graph Convolutional Network (GCN) is proposed, enabling the model to capture label correlations more effectively and improve performance by constructing a label correlation matrix. This model enhances cross-domain generalization capability and improves recognition performance in fire scenarios. In the experimental phase, we developed a comprehensive dataset by integrating multiple fire-related public datasets, and conducted detailed comparison and ablation experiments. Results from the tenfold cross-validation demonstrate that the proposed model significantly improves recognition of multi-labeled images in fire scenarios. Compared with the baseline model, the mAP increased by 4.426%, CP by 4.14% and CF1 by 7.04%.
Full article
(This article belongs to the Special Issue Advanced Approaches to Wildfire Detection, Monitoring and Surveillance)
►▼
Show Figures
Figure 1
Open AccessArticle
Preliminary Assessment of Tunic Off-Gassing after Wildland Firefighting Exposure
by
Kiam Padamsey, Adelle Liebenberg, Ruth Wallace and Jacques Oosthuizen
Fire 2024, 7(9), 321; https://doi.org/10.3390/fire7090321 - 14 Sep 2024
Abstract
►▼
Show Figures
Evidence has previously shown that outer tunics (turnout coats) worn by firefighters at structural fires are contaminated with harmful chemicals which subsequently off-gas from the material. However, there is limited research on whether this phenomenon extends to wildland firefighter uniforms. This pilot study
[...] Read more.
Evidence has previously shown that outer tunics (turnout coats) worn by firefighters at structural fires are contaminated with harmful chemicals which subsequently off-gas from the material. However, there is limited research on whether this phenomenon extends to wildland firefighter uniforms. This pilot study aimed to explore if the tunics of volunteer bushfire and forestry firefighters in Western Australia off-gas any contaminants after exposure to prescribed burns or bushfires, and whether there is a need to explore this further. Nine tunics were collected from firefighters following nine bushfire and prescribed burn events, with a set of unused tunics serving as a control. Chemical analysis was performed on these tunics to assess levels of acrolein, benzene, formaldehyde, and sulphur dioxide contamination. The assessment involved measuring chemical off-gassing over a 12 h period using infrared spectrometry. Tunics worn by firefighters appear to adsorb acrolein, benzene, formaldehyde, and sulphur dioxide from bushfire smoke and these contaminants are emitted from firefighting tunics following contamination at elevated concentrations. Further investigation of this research with a larger study sample will be beneficial to understand this phenomenon better and to determine the full extent and range of chemical contaminants absorbed by all firefighter clothing.
Full article
Figure 1
Open AccessArticle
Experimental Analysis of Ceiling Temperature Distribution in Sloped Integrated Common Services Tunnels
by
Linjie Li, Guang Wu, Zhaoguo Wu, Huixian Huang, Haibing Zhang and Zihe Gao
Fire 2024, 7(9), 320; https://doi.org/10.3390/fire7090320 - 13 Sep 2024
Abstract
In this study, a 1/10 reduced-scale model tunnel with one end closed was constructed to investigate maximum temperature profiles beneath the tunnel ceiling during fire events. By varying the heat release rates (HRRs) and tunnel slopes (0%, 2%, 5%, and 6%) and measuring
[...] Read more.
In this study, a 1/10 reduced-scale model tunnel with one end closed was constructed to investigate maximum temperature profiles beneath the tunnel ceiling during fire events. By varying the heat release rates (HRRs) and tunnel slopes (0%, 2%, 5%, and 6%) and measuring horizontal temperatures longitudinally along the tunnel ceiling, the effects of these parameters were systematically examined. The findings reveal that the distribution of maximum temperatures within a one-end-closed tunnel can be categorized into three distinct regions: far-field, transition, and near-field regions. Notably, milder tunnel slopes correspond to an elevated maximum temperature beneath the ceiling. By employing dimensional analysis, two prediction models were formulated to forecast maximum temperatures beneath the ceiling for fire sources located in the far-field and near-field regions, respectively. These predictive models were validated against experimental data, demonstrating favorable agreement. This study enhances our understanding of the impact of tunnel slope on temperature distribution during fire events in one-end-closed tunnels. Furthermore, the prediction models developed offer practical tools for assessing and mitigating fire risks in such tunnel configurations.
Full article
(This article belongs to the Section Fire Risk Assessment and Safety Management in Buildings and Urban Spaces)
►▼
Show Figures
Figure 1
Open AccessArticle
Soil and Water Bioengineering in Fire-Prone Lands: Detecting Erosive Areas Using RUSLE and Remote Sensing Methods
by
Melanie Maxwald, Ronald Correa, Edwin Japón, Federico Preti, Hans Peter Rauch and Markus Immitzer
Fire 2024, 7(9), 319; https://doi.org/10.3390/fire7090319 - 13 Sep 2024
Abstract
Soil and water bioengineering (SWBE) measures in fire-prone areas are essential for erosion mitigation, revegetation, as well as protection of settlements against inundations and landslides. This study’s aim was to detect erosive areas at the basin scale for SWBE implementation in pre- and
[...] Read more.
Soil and water bioengineering (SWBE) measures in fire-prone areas are essential for erosion mitigation, revegetation, as well as protection of settlements against inundations and landslides. This study’s aim was to detect erosive areas at the basin scale for SWBE implementation in pre- and post-fire conditions based on a wildfire event in 2019 in southern Ecuador. The Revised Universal Soil Loss Equation (RUSLE) was used in combination with earth observation data to detect the fire-induced change in erosion behavior by adapting the cover management factor (C-factor). To understand the spatial accuracy of the predicted erosion-prone areas, high-resolution data from an Unmanned Aerial Vehicle (UAV) served for comparison and visual interpretation at the sub-basin level. As a result, the mean erosion at the basin was estimated to be 4.08 t ha−1 yr−1 in pre-fire conditions and 4.06 t ha−1 yr−1 in post-fire conditions. The decrease of 0.44% is due to the high autonomous vegetation recovery capacity of grassland in the first post-fire year. Extreme values increased by a factor of 4 in post-fire conditions, indicating the importance of post-fire erosion measures such as SWBE in vulnerable areas. The correct spatial location of highly erosive areas detected by the RUSLE was successfully verified by the UAV data. This confirms the effectivity of combining the RUSLE with very-high-resolution data in identifying areas of high erosion, suggesting potential scalability to other fire-prone regions.
Full article
(This article belongs to the Special Issue Remote Sensing of Wildfire: Regime Change and Disaster Response)
►▼
Show Figures
Figure 1
Open AccessArticle
Investigation on Flexural Behavior of Galvanized Cold-Formed Steel Beams Exposed to Fire with Different Stiffener Configurations
by
Varun Sabu Sam, Garry Wegara K Marak, Anand Nammalvar, Diana Andrushia, Beulah Gnana Ananthi Gurupatham and Krishanu Roy
Fire 2024, 7(9), 318; https://doi.org/10.3390/fire7090318 - 13 Sep 2024
Abstract
Cold-formed steel (CFS) sections, increasingly favored in the construction industry due to their numerous advantages over hot-rolled steel, have received limited attention in research concerning the flexural behavior of galvanized iron (GI)-based CFS at elevated temperatures. Understanding how these materials and structures behave
[...] Read more.
Cold-formed steel (CFS) sections, increasingly favored in the construction industry due to their numerous advantages over hot-rolled steel, have received limited attention in research concerning the flexural behavior of galvanized iron (GI)-based CFS at elevated temperatures. Understanding how these materials and structures behave under elevated temperatures is crucial for fire safety. The authors have performed experimental studies previously on GI-based CFS under elevated temperatures. In that study, CFS sections made of GI of grade E350 of 1.5 m long and 2 mm thickness were used. Built-up beam sections were tested under two-point loading after heating to 60 and 90 min durations and subsequently cooling them down using air and water. This study aims to uncover the influence of different stiffener configurations on the load carrying capacity of sections under elevated temperature parametrically. With the experimental study results from previous studies as a reference, authors used FEM analysis to comprehensively study the behavior of GI-based CFS sections under fire. Vertical, horizontal, and not providing a stiffener were the configurations selected to study the beams parametrically. Parametric analysis confirmed that different stiffener configurations did not alter the predominant failure mode, which remained distortional buckling across all specimens. Beams with vertical stiffeners demonstrated superior performance compared to those with horizontal stiffeners in parametric analysis. Lateral–torsional buckling was observed in the reference specimen, lacking stiffeners due to inadequate restraint at the supports.
Full article
(This article belongs to the Section Fire Risk Assessment and Safety Management in Buildings and Urban Spaces)
►▼
Show Figures
Figure 1
Open AccessArticle
Study on Response Time Hysteresis Model of Smoke Detectors in Aircraft Cargo Compartment
by
Hongwei Cui, Chenran Ruan, Shengdong Wang, Song Lu, Heping Zhang and Minqiang Wang
Fire 2024, 7(9), 317; https://doi.org/10.3390/fire7090317 - 13 Sep 2024
Abstract
►▼
Show Figures
A fire in the cargo compartment has a major impact on civil aviation flight safety, and according to the airworthiness clause of the CCAR-25, the detector must sound an alarm within 1 min of a fire in the cargo compartment. As for the
[...] Read more.
A fire in the cargo compartment has a major impact on civil aviation flight safety, and according to the airworthiness clause of the CCAR-25, the detector must sound an alarm within 1 min of a fire in the cargo compartment. As for the cargo compartment of large transport aircrafts, the internal space is high and open, and the smoke movement speed becomes slower with significant cooling in the process of diffusion. Hysteresis can occur in smoke detectors because of their internal labyrinth structure, which causes the detector’s internal and external response signals to be out of sync. This research employs a numerical simulation to examine the detector response parameters under an ambient wind speed of 0.1–0.2 m/s and fits a Cleary two-stage hysteresis model, where τ1= 0.09u−1.43 and τ2= 0.67u−1.59. Finally, multiple full-scale cargo cabin experiments were conducted to validate the prediction model. The results show that the model’s predicted alarm range is 43.1 s to 49.0 s, and the actual alarm time obtained by the experiment falls within this interval, confirming the model’s accuracy and providing theoretical support for the structural design and layout of the aircraft cargo cabin smoke detector.
Full article
Figure 1
Open AccessArticle
Prediction of Ceiling Temperature Rise in High-Voltage Cable Trenches with Identification of Ignition Points
by
Zhaochen Zhang, Liang Zou, Hongmin Yang and Zhiyun Han
Fire 2024, 7(9), 316; https://doi.org/10.3390/fire7090316 - 11 Sep 2024
Abstract
Early detection of cable trench fires by locating the fire source in a timely manner can reduce the risk of fire. However, existing fire warning methods have low accuracy, long calculation times and difficulty coping with sudden fire situations. We established experimental platforms
[...] Read more.
Early detection of cable trench fires by locating the fire source in a timely manner can reduce the risk of fire. However, existing fire warning methods have low accuracy, long calculation times and difficulty coping with sudden fire situations. We established experimental platforms for cable trenches with different structures and combined these with simulation analysis to investigate the relationship between the ignition point position and the temperature distribution at the ceiling. An exponential function for predicting the ignition point position and the maximum temperature rise of tunnels is proposed based on the extreme values of ceiling temperature. The results indicate that the vertical temperature of the ceiling exhibits an exponential function variation pattern. The maximum deviation for identifying the ignition point is 0.098 m, with an average deviation of 0.044 m and an average accuracy of 98.77%. The maximum temperature prediction error for the ceiling is 14 °C, with an average deviation of 12.33 °C and an average accuracy of 98.30%. Compared to traditional fire prediction methods, the method proposed here has higher accuracy and provides a theoretical basis for early prevention and control of cable trench fires.
Full article
(This article belongs to the Special Issue Unusual Fire in Open and Confined Space)
►▼
Show Figures
Figure 1
Journal Menu
► ▼ Journal Menu-
- Fire Home
- Aims & Scope
- Editorial Board
- Reviewer Board
- Topical Advisory Panel
- Instructions for Authors
- Special Issues
- Topics
- Sections & Collections
- Article Processing Charge
- Indexing & Archiving
- Editor’s Choice Articles
- Most Cited & Viewed
- Journal Statistics
- Journal History
- Journal Awards
- Society Collaborations
- Editorial Office
Journal Browser
► ▼ Journal BrowserHighly Accessed Articles
Latest Books
E-Mail Alert
News
Topics
Topic in
Drones, Fire, Forests, Remote Sensing, Sustainability
Application of Remote Sensing in Forest Fire
Topic Editors: Aqil Tariq, Na ZhaoDeadline: 31 December 2024
Topic in
AI, BDCC, Fire, GeoHazards, Remote Sensing
AI for Natural Disasters Detection, Prediction and Modeling
Topic Editors: Moulay A. Akhloufi, Mozhdeh ShahbaziDeadline: 25 July 2025
Conferences
Special Issues
Special Issue in
Fire
Understanding, Monitoring, and Responses to Wildfires with New Sensors
Guest Editors: Stefania Amici, Dario Spiller, Ioannis GitasDeadline: 25 September 2024
Special Issue in
Fire
Impacts of Fire-Related Emissions on Air Quality
Guest Editors: Jan Stefan Bihałowicz, Wioletta Rogula-Kozłowska, Adam KrasuskiDeadline: 30 September 2024
Special Issue in
Fire
Advance in Tunnel Fire Research
Guest Editors: Kaihua Lu, Jianping Zhang, Jie Wang, Xiaochun Zhang, Wei TangDeadline: 30 September 2024
Special Issue in
Fire
Intelligent Forest Fire Prediction and Detection
Guest Editors: Demin Gao, Shuo Zhang, Cheng HeDeadline: 30 September 2024
Topical Collections
Topical Collection in
Fire
Technical Forum for Fire Science Laboratory and Field Methods
Collection Editors: Claire Belcher, David M.J.S. Bowman, Evan Ellicott, Peter Hamlington, Chad Hoffman, William M. Jolly, Rodman Linn, Sara McAllister, Joseph O'Brien, Albert Simeoni, Alistair M. S. Smith, Wojciech Węgrzyński
Topical Collection in
Fire
Rethinking Wildland Fire Governance: A Series of Perspectives
Collection Editors: Alistair M. S. Smith, Stephen D. Fillmore