Fire最新文献

{"title":"Applicability of an Ionising Radiation Measuring System for Real-Time Effective-Dose-Optimised Route Finding Solution during Nuclear Accidents","authors":"Attila Zsitnyányi, János Petrányi, Jácint Jónás, Zoltán Garai, L. Kátai-Urbán, Iván Zádori, István Kobolka","doi":"10.3390/fire7040142","DOIUrl":"https://doi.org/10.3390/fire7040142","url":null,"abstract":"The reduction in the effective dose of evacuated injured persons through contaminated areas of nuclear accidents is an essential emergency services requirement. In this context, there appeared a need to develop a dose-optimised route finding method for firefighting rescue vehicles, which includes the development of a real-time decision support measurement and evaluation system. This determines and visualises the radiation exposure of possible routes in a tested area. The system inside and outside of the vehicle measures the ambient dose equivalent rate, the gamma spectra, and also the airborne radioactive aerosol and iodine levels. The method uses gamma radiation measuring NaI(Tl) scintillation detectors mounted on the outside of the vehicle, to determine the dose rate inside the vehicle using the previously recorded attenuation conversation function, while continuously collecting the air through a filter and using an alpha, beta, and gamma radiation measuring NaI(Tl)+ PVT + ZnS(Ag) scintillator to determine the activity concentration in the air, using these measured values to determine the effective dose for all routes and all kinds of vehicles. The energy-dependent shielding effect of the vehicle, the filtering efficiency of the collective protection equipment, and the vehicle’s speed and travel time were taken into account. The results were validated by using gamma point sources with different activity and energy levels. The measurement results under real conditions and available real accident data used in our simulations for three different vehicles and pedestrians proved the applicability of the system. During a nuclear accident based on our model calculations, the inhalation of radioactive aerosols causes a dose almost an order of magnitude higher than the external gamma radiation caused by the fallout contamination. The selection of the appropriate vehicle and its route is determined by the spectrum that can be measured at the accident site but especially by the radioactive aerosol concentration in the air that can be measured in the area. In the case of radiation measuring detectors, the shielding effect of the carrier vehicle must be taken into account, especially in the case of heavy shielding vehicles. The method provides an excellent opportunity to reduce the damage to the health of accident victims and first responders during rescue operations.","PeriodicalId":508952,"journal":{"name":"Fire","volume":"24 46","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140696388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"YOLO-Based Models for Smoke and Wildfire Detection in Ground and Aerial Images","authors":"Leon Augusto Okida Gonçalves, Rafik Ghali, Moulay A. Akhloufi","doi":"10.3390/fire7040140","DOIUrl":"https://doi.org/10.3390/fire7040140","url":null,"abstract":"Wildland fires negatively impact forest biodiversity and human lives. They also spread very rapidly. Early detection of smoke and fires plays a crucial role in improving the efficiency of firefighting operations. Deep learning techniques are used to detect fires and smoke. However, the different shapes, sizes, and colors of smoke and fires make their detection a challenging task. In this paper, recent YOLO-based algorithms are adopted and implemented for detecting and localizing smoke and wildfires within ground and aerial images. Notably, the YOLOv7x model achieved the best performance with an mAP (mean Average Precision) score of 80.40% and fast detection speed, outperforming the baseline models in detecting both smoke and wildfires. YOLOv8s obtained a high mAP of 98.10% in identifying and localizing only wildfire smoke. These models demonstrated their significant potential in handling challenging scenarios, including detecting small fire and smoke areas; varying fire and smoke features such as shape, size, and colors; the complexity of background, which can include diverse terrain, weather conditions, and vegetation; and addressing visual similarities among smoke, fog, and clouds and the the visual resemblances among fire, lighting, and sun glare.","PeriodicalId":508952,"journal":{"name":"Fire","volume":"39 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140705385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parameters Affecting the Explosion Characteristics of Hybrid Mixtures Arising from the Use of Alternative Energy Sources","authors":"Matous Helegda, Jiri Pokorny, Iris Helegda, J. Skrinsky, Juraj Sinay","doi":"10.3390/fire7040139","DOIUrl":"https://doi.org/10.3390/fire7040139","url":null,"abstract":"Explosions of hybrid mixtures are an interesting theoretical and experimental problem in explosion sciences, because they combine the physicochemical properties of flammable gases and dusts. A hybrid mixture is composed of at least two substances in two or more states. The influence of the common presence of flammable gas on the explosiveness parameters of the combustible dust itself is proven. In this study, we present the effect of higher initiation temperatures, different initial sources of initiation with different energies, and the effect of the volume of explosion chambers on the explosions of hybrid mixtures arising from the use of alternative energy sources. The experiments were carried out in 20 L and 1.00 m3 explosion chambers (according to EN 14034-1+A1:2011–EN 14034-4+A1:2011). The accredited method of the Energy Research Centre, VSB-TU Ostrava, for tests was used. The goal is to approximate the behaviour of these systems under different initiation conditions so that it is possible to avoid excessively conservative or overly optimistic results, which then affect the determination of explosion parameters for practical use. It was found that the volume of the explosion chambers in combination with the used initiation source has a fundamental influence on the course of the explosion characteristics.","PeriodicalId":508952,"journal":{"name":"Fire","volume":"42 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140705223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility of Using Combustion-Based Methods to Quantify Saline-Based Anti-Stripping Agent in Modified Asphalt Binders","authors":"Riyadul Hashem Riyad, Ji Wu, Junan Shen","doi":"10.3390/fire7040138","DOIUrl":"https://doi.org/10.3390/fire7040138","url":null,"abstract":"“Anti-stripping Agents” or “adhesion promoters” can enhance the chemical affinity between asphalt and aggregate by increasing their mutual attraction. Various forms of anti-stripping agents have been proposed to mitigate pavement stripping, and siloxane-based Zychotherm is one of them. Choosing the appropriate type and dose of anti-stripping additives is no doubt vital to the intended performance. Therefore, it is critically important to determine the dose of the additives used in the modification of asphalt binders. This research developed a feasible detection method that can closely measure the dose (0.05% and 0.1%) of siloxane-based anti-stripping liquid agents. Related test methods, including heat combustion test, residue visualization, burning, and ignition, were implemented. The heat combustion results showed that with the addition of the Zychotherm anti-stripping additive, the average heat combustion value decreased by 1.34% and 1.72% for 0.05% and 0.1% Zychotherm-modified binder, respectively. In the burning and ignition process, the modified binder left yellowish substances in the residue, which is an indication of the presence of Zychotherm. The weight of the yellowish residue related more to the quantity of Zychotherm in the asphalt binder.","PeriodicalId":508952,"journal":{"name":"Fire","volume":"60 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140705138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Influence of the Heat Transfer Mode on the Stability of Foam Extinguishing Agents","authors":"Xia Zhou, Zhihao An, Ziheng Liu, Hongjie Ha, Yixuan Li, Renming Pan","doi":"10.3390/fire7040137","DOIUrl":"https://doi.org/10.3390/fire7040137","url":null,"abstract":"The mass loss mechanisms of an aqueous film-forming foam (AF foam), an AR/AFFF water-soluble film-forming foam extinguishing agent (AR foam), and a Class A foam extinguishing agent (A foam) at different levels of thermal radiation, thermal convection, and heat conduction intensity were studied. At a relatively low thermal radiation intensity, the liquid separation rate of the AF, AR, and A foams is related to the properties of the foam itself, such as viscosity and surface/interface tension, which are relatively independent of the external radiation heat flux of the foam. At low radiation intensity (15 kW/m2 and 25 kW/m2), the liquid separation rate of the AF and A foams is relatively stable. When the heat flux intensity is 35 kW/m2, the liquid separation rate of the AF and A foams increases notably, which may be mainly due to the rapid decrease in foam viscosity. And the mass loss behavior is dominated by liquid separation in the AF, AR, and A foams under the influence of thermal radiation and thermal convection. Under the same experimental conditions, the liquid separation rate of AF is the fastest. There is no significant difference in the evaporation rates of the three kinds of foam in the same heat conduction condition. In addition, the AR and A foams usually have a 25% longer liquid separation time (t) under thermal radiation and thermal convection, and the thermal stability is better than AF foam. The temperature reached by the AF foam layer under thermal convection was lower than that of the AR and A foams, and the time for the foam layer to reach the highest temperature under heat conduction was longer than that of the AR and A foams.","PeriodicalId":508952,"journal":{"name":"Fire","volume":"12 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140712512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fire and Smoke Detection Using Fine-Tuned YOLOv8 and YOLOv7 Deep Models","authors":"Mohamed Chetoui, Moulay A. Akhloufi","doi":"10.3390/fire7040135","DOIUrl":"https://doi.org/10.3390/fire7040135","url":null,"abstract":"Viewed as a significant natural disaster, wildfires present a serious threat to human communities, wildlife, and forest ecosystems. The frequency of wildfire occurrences has increased recently, with the impacts of global warming and human interaction with the environment playing pivotal roles. Addressing this challenge necessitates the ability of firefighters to promptly identify fires based on early signs of smoke, allowing them to intervene and prevent further spread. In this work, we adapted and optimized recent deep learning object detection, namely YOLOv8 and YOLOv7 models, for the detection of smoke and fire. Our approach involved utilizing a dataset comprising over 11,000 images for smoke and fires. The YOLOv8 models successfully identified fire and smoke, achieving a mAP:50 of 92.6%, a precision score of 83.7%, and a recall of 95.2%. The results were compared with a YOLOv6 with large model, Faster-RCNN, and DEtection TRansformer. The obtained scores confirm the potential of the proposed models for wide application and promotion in the fire safety industry.","PeriodicalId":508952,"journal":{"name":"Fire","volume":"12 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140710184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictive Modeling of Fire Incidence Using Deep Neural Networks","authors":"C. Ku, Chih-Yu Liu","doi":"10.3390/fire7040136","DOIUrl":"https://doi.org/10.3390/fire7040136","url":null,"abstract":"To achieve successful prevention of fire incidents originating from human activities, it is imperative to possess a thorough understanding. This paper introduces a machine learning approach, specifically utilizing deep neural networks (DNN), to develop predictive models for fire occurrence in Keelung City, Taiwan. It investigates ten factors across demographic, architectural, and economic domains through spatial analysis and thematic maps generated from geographic information system data. These factors are then integrated as inputs for the DNN model. Through 50 iterations, performance indices including the coefficient of determination (R2), root mean square error (RMSE), variance accounted for (VAF), prediction interval (PI), mean absolute error (MAE), weighted index (WI), weighted mean absolute percentage error (WMAPE), Nash–Sutcliffe efficiency (NS), and the ratio of performance to deviation (RPD) are computed, with average values of 0.89, 7.30 × 10−2, 89.21, 1.63, 4.90 × 10−2, 0.97, 2.92 × 10−1, 0.88, and 4.84, respectively. The model’s predictions, compared with historical data, demonstrate its efficacy. Additionally, this study explores the impact of various urban renewal strategies using the DNN model, highlighting the significant influence of economic factors on fire incidence. This underscores the importance of economic factors in mitigating fire incidents and emphasizes their consideration in urban renewal planning.","PeriodicalId":508952,"journal":{"name":"Fire","volume":"9 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140711798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The House Is Burning: Assessment of Habitat Loss Due to Wildfires in Central Mexico","authors":"C. Mastachi-Loza, Jorge Paredes-Tavares, Rocio Becerril-Piña, M. D. Ruíz-Gómez, Carlos Alejandro Rangel Patiño, C. Díaz-Delgado","doi":"10.3390/fire7040134","DOIUrl":"https://doi.org/10.3390/fire7040134","url":null,"abstract":"Fire suppression and climate change have increased the frequency and severity of wildfires, but the responses of many organisms to wildfire are still largely unknown. In this study, we assessed the risk of habitat loss for amphibians, mammals, and reptiles caused by wildfires in central Mexico. We accomplished this by: (1) determining the likelihood of wildfire occurrence over a 12-year period using historical records and the Poisson probability mass function to pinpoint the most susceptible areas to wildfire; (2) evaluating species exposure by identifying natural land use that aligns with the potential distribution areas of biodiversity; (3) assessing species vulnerability based on the classifications established by the IUCN and CONABIO. Our findings have unveiled three regions exhibiting a concentration of high-risk values. Among these, two are positioned near major urban centers, while the third lies in the southeastern sector of the Nevado de Toluca protection area. Amphibians emerged as the taxonomic group most severely impacted, with a substantial number of species falling within the Critically Endangered and Endangered categories, closely followed by mammals and reptiles. Furthermore, we have identified a correlation between the location of risk zones and agricultural areas. This study revealed hotspots that can offer valuable guidance for strategic initiatives in fire-prone regions associated to the potential distribution of amphibians, mammals, and reptiles. Moreover, future studies should contemplate integrating field data to enhance our comprehension of the actual effects of wildfires on the spatial distribution of these animal groups.","PeriodicalId":508952,"journal":{"name":"Fire","volume":"104 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140709112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Relationships of Fire Rate of Spread with Spectral and Geometric Features Derived from UAV-Based Photogrammetric Point Clouds","authors":"J. P. Carbonell-Rivera, Christopher J. Moran, Carl A. Seielstad, Russell A. Parsons, Valentijn Hoff, Luis A. Ruiz, Jesús Torralba, Javier Estornell","doi":"10.3390/fire7040132","DOIUrl":"https://doi.org/10.3390/fire7040132","url":null,"abstract":"Unmanned aerial vehicles (UAVs) equipped with RGB, multispectral, or thermal cameras have demonstrated their potential to provide high-resolution data before, during, and after wildfires and prescribed burns. Pre-burn point clouds generated through the photogrammetric processing of UAV images contain geometrical and spectral information of vegetation, while active fire imagery allows for deriving fire behavior metrics. This paper focuses on characterizing the relationship between the fire rate of spread (RoS) in prescribed burns and a set of independent geometrical, spectral, and neighborhood variables extracted from UAV-derived point clouds. For this purpose, different flights were performed before and during the prescribed burning in seven grasslands and open forest plots. Variables extracted from the point cloud were interpolated to a grid, which was sized according to the RoS semivariogram. Random Forest regressions were applied, obtaining up to 0.56 of R2 in the different plots studied. Geometric variables from the point clouds, such as planarity and the spectral normalized blue–red difference index (NBRDI), are related to fire RoS. In analyzing the results, the minimum value of the eigenentropy (Eigenentropy_MIN), the mean value of the planarity (Planarity_MEAN), and percentile 75 of the NBRDI (NBRDI_P75) obtained the highest feature importance. Plot-specific analyses unveiled distinct combinations of geometric and spectral features, although certain features, such as Planarity_MEAN and the mean value of the grid obtained from the standard deviation of the distance between points (Dist_std_MEAN), consistently held high importance across all plots. The relationships between pre-burning UAV data and fire RoS can complement meteorological and topographic variables, enhancing wildfire and prescribed burn models.","PeriodicalId":508952,"journal":{"name":"Fire","volume":"11 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140715314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on the Exposure Risk Analysis of Wildfires with a Spatiotemporal Knowledge Graph","authors":"Xingtong Ge, Ling Peng, Yi Yang, Yinda Wang, Deyue Chen, Lina Yang, Weichao Li, Jiahui Chen","doi":"10.3390/fire7040131","DOIUrl":"https://doi.org/10.3390/fire7040131","url":null,"abstract":"This study focuses on constructions that are vulnerable to fire hazards during wildfire events, and these constructions are known as ‘exposures’, which are an increasingly significant area of disaster research. A key challenge lies in estimating dynamically and comprehensively the risk that individuals are exposed to during wildfire spread. Here, ‘exposure risk’ denotes the potential threat to exposed constructions from fires within a future timeframe. This paper introduces a novel method that integrates a spatiotemporal knowledge graph with wildfire spread data and an exposure risk analysis model to address this issue. This approach enables the semantic integration of varied and heterogeneous spatiotemporal data, capturing the dynamic nature of wildfire propagation for precise risk analysis. Empirical tests are employed for the study area of Xichang, Sichuan Province, using real-world data to validate the method’s efficacy in merging multiple data sources and enhancing the accuracy of exposure risk analysis. Notably, this approach also reduces the time complexity from O (m×n×p) to O (m×n).","PeriodicalId":508952,"journal":{"name":"Fire","volume":"6 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140715926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}