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Fire
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Advance in Fire Safety Science
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Advance in Fire Safety Science

A special issue of Fire (ISSN 2571-6255).

Deadline for manuscript submissions: 31 December 2024 | Viewed by 5965

Special Issue Editor

Dr. Yanyan Liu

E-Mail Website
Guest Editor
School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan 430070, China
Interests: environmental safety; fire ecology

Special Issue Information

Dear Colleagues,

Wildfire is both a natural and anthropogenic disturbance influencing the distribution, structure, and functioning of terrestrial ecosystems around the world. Wildfire plays a significant role in plant evolution and animal inhabitation. The ecological effect of products, such as heat, VOCs, smoke, ash, etc., generated by wildfire remains unclear. As is known, the atmospheric impact can be determined by the heat, soot, and particulate matters. Although ash is beneficial to the soil quality, the heavy metals and PAHs released during fires are detrimental to ecology. Therefore, the comprehensive study of wildfire ecology needs to draw great attention.

We welcome submissions on topics that include a broad range of research on the ecological relationships of wildfire to its environment and health, including, but not limited to:

  • Fire ecology (physical and biological fire effects, fire regimes, etc.);
  • Fire management;
  • Fire science and modeling;
  • Planning and risk management;
  • Fire monitoring;
  • Risk assessment.

I/We look forward to receiving your contributions.

Dr. Yanyan Liu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Fire is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • wildfire
  • heat
  • smoke
  • VOCs
  • heavy metals
  • PAHs
  • transformation
  • toxicity
  • assessment

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

22 pages, 10366 KiB  
Article
Real-Time Smoke Detection in Surveillance Videos Using an Enhanced RT-DETR Framework with Triplet Attention and HS-FPN
by Lanyan Yang, Yuanhang Cheng, Fang Xu, Boning Li and Xiaoxu Li
Fire 2024, 7(11), 387; https://doi.org/10.3390/fire7110387 - 28 Oct 2024
Viewed by 774
Abstract
This study addresses the urgent need for an efficient and accurate smoke detection system to enhance safety measures in fire monitoring, industrial safety, and urban surveillance. Given the complexity of detecting smoke in diverse environments and under real-time constraints, our research aims to [...] Read more.
This study addresses the urgent need for an efficient and accurate smoke detection system to enhance safety measures in fire monitoring, industrial safety, and urban surveillance. Given the complexity of detecting smoke in diverse environments and under real-time constraints, our research aims to solve challenges related to low-resolution imagery, limited computational resources, and environmental variability. This study introduces a novel smoke detection system that utilizes the real-time detection Transformer (RT-DETR) architecture to enhance the speed and precision of video analysis. Our system integrates advanced modules, including triplet attention, ADown, and a high-level screening-feature fusion pyramid network (HS-FPN), to address challenges related to low-resolution imagery, real-time processing constraints, and environmental variability. The triplet attention mechanism is essential for detecting subtle smoke features, often overlooked due to their nuanced nature. The ADown module significantly reduces computational complexity, enabling real-time operation on devices with limited resources. Furthermore, the HS-FPN enhances the system’s robustness by amalgamating multi-scale features for reliable detection across various smoke types and sizes. Evaluation using a diverse dataset showcased notable improvements in average precision (AP50) and frames per second (FPS) metrics compared to existing state-of-the-art networks. Ablation studies validated the contributions of each component in achieving an optimal balance between accuracy and operational efficiency. The RT-DETR-based smoke detection system not only meets real-time requirements for applications like fire monitoring, industrial safety, and urban surveillance but also establishes a new performance benchmark in this field. Full article
(This article belongs to the Special Issue Advance in Fire Safety Science)
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19 pages, 3314 KiB  
Article
Exploring Spontaneous Combustion Characteristics and Structural Disparities of Coal Induced by Igneous Rock Erosion
by Mingqian Zhang, Zongxiang Li, Zhifeng Chen, Lun Gao, Yun Qi and Haifeng Hu
Fire 2024, 7(5), 159; https://doi.org/10.3390/fire7050159 - 4 May 2024
Viewed by 1369
Abstract
The erosion of igneous rocks affects the structural and spontaneous combustion characteristics of coal. A series of tests were conducted, including programmed heating, thermogravimetric analysis, FT-IR spectroscopy, low-temperature nitrogen adsorption, and pressed mercury experiments on samples from primary coal and coal eroded by [...] Read more.
The erosion of igneous rocks affects the structural and spontaneous combustion characteristics of coal. A series of tests were conducted, including programmed heating, thermogravimetric analysis, FT-IR spectroscopy, low-temperature nitrogen adsorption, and pressed mercury experiments on samples from primary coal and coal eroded by igneous rocks from the Tashan Mine and Xiaonan Mine within the same coal seam. Based on these experiments, we analyzed various properties of coal, such as the oxidation characteristics, spontaneous combustion limit, active functional group content, chemical structure, and pore structure, from both macroscopic and microscopic perspectives. The results indicated significant trends after the erosion of igneous rocks: (1) there were increases in the oxygen consumption rate, as well as the CO and CO2 release rates; (2) the upper limit of air leakage intensity increased, the minimum thickness of floating coal decreased, and the lower limit of oxygen volume fraction decreased; (3) there was a decrease in the activation energy required for coal ignition; (4) there was a decrease in the active functional group content while improving the structural stability; and (5) there were the alterations in the pore structure of coal. These promoted the oxidation reactions between oxygen and the active groups within the coal matrix, increasing the propensity for spontaneous combustion, particularly in the igneous rocks with low oxidation activity. Full article
(This article belongs to the Special Issue Advance in Fire Safety Science)
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15 pages, 2399 KiB  
Article
Study on the Prediction Model of Coal Spontaneous Combustion Limit Parameters and Its Application
by Wei Wang, Ran Liang, Yun Qi, Xinchao Cui, Jiao Liu and Kailong Xue
Fire 2023, 6(10), 381; https://doi.org/10.3390/fire6100381 - 7 Oct 2023
Cited by 6 | Viewed by 1418
Abstract
The limit parameters of coal spontaneous combustion are important indicators for determining the risk of spontaneous combustion in coal seams. By analyzing the limit parameters of coal spontaneous combustion, the dangerous areas of coal spontaneous combustion can be determined, and corresponding measures can [...] Read more.
The limit parameters of coal spontaneous combustion are important indicators for determining the risk of spontaneous combustion in coal seams. By analyzing the limit parameters of coal spontaneous combustion, the dangerous areas of coal spontaneous combustion can be determined, and corresponding measures can be taken to avoid the occurrence of fires. In order to accurately predict the limit parameters of coal spontaneous combustion, the prediction model of coal spontaneous combustion limit parameters based on GA-SVM was constructed by coupling genetic algorithm (GA) and support vector machine (SVM). Meanwhile, the GA and particle swarm optimization algorithm (PSO) were used to optimize the back propagation neural network (BPNN) to construct the GA-BPNN and PSO-BPNN prediction models, respectively. To predict the intensity of air leakage of the upper limit of coal spontaneous combustion in the goaf, the prediction results of the models were compared and analyzed using MAE, MAPE, RMSE, and R2 as the prediction performance evaluation indexes. The results show that the MAE of the GA-SVM model, the PSO-BPNN model, and the GA-BPNN model are 0.0960, 0.1086, and 0.1309, respectively; the MAPE is 2.46%, 3.11%, and 3.69%, respectively; the RMSE is 0.1180, 0.1789, and 0.2212, respectively; and the R2 is 0.9921, 0.9818, and 0.9722. The prediction results of the GA-SVM model are the most optimal in four evaluation indexes, followed by the PSO-BPNN and the GA-BPNN models. Applying each model to the prediction of minimum residual coal thickness in the goaf of a coal mine in Shanxi, the GA-SVM model has higher accuracy, which further verifies the universality and stability of the model and its suitability for the prediction of coal spontaneous combustion limit parameters. Full article
(This article belongs to the Special Issue Advance in Fire Safety Science)
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16 pages, 1384 KiB  
Article
Prediction Model of Borehole Spontaneous Combustion Based on Machine Learning and Its Application
by Yun Qi, Kailong Xue, Wei Wang, Xinchao Cui and Ran Liang
Fire 2023, 6(9), 357; https://doi.org/10.3390/fire6090357 - 12 Sep 2023
Cited by 4 | Viewed by 1418
Abstract
In order to quickly and accurately predict borehole spontaneous combustion danger and avoid borehole spontaneous combustion fires, a borehole spontaneous combustion prediction model combining the Hunger Games search optimization algorithm (HGS) and Random Forest (RF) algorithm was introduced. The number of trees and [...] Read more.
In order to quickly and accurately predict borehole spontaneous combustion danger and avoid borehole spontaneous combustion fires, a borehole spontaneous combustion prediction model combining the Hunger Games search optimization algorithm (HGS) and Random Forest (RF) algorithm was introduced. The number of trees and the minimum number of leaf nodes in RF were optimized by HGS. Based on the data obtained from the temperature rise experiment of spontaneous combustion characteristics in a Shandong mine laboratory, O2, CO, C2H4, CO/∆O2 and C2H4/C2H6 were selected as the input indexes for the prediction of borehole spontaneous combustion, and the spontaneous combustion temperature was selected as the output indexes to train the built model. The prediction performance and accuracy of the model were evaluated using four indexes: the mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2). At the same time, the prediction results of the HGS-RF model were compared with those of the RF model, Sparrow search algorithm (SSA) optimization RF model, particle swarm optimization RF model (PSO) optimization RF model and quantum particle swarm optimization RF model (QPSO) optimization. The results showed that the MAE of the RF, SSA-RF, PSO-RF, QPSO-RF and HGS-RF model samples were 17.541, 15.7752, 12.5903, 6.8594 and 6.6921, respectively. MAPE was 13.81%, 10.9766%, 9.6802%, 4.5731% and 5.1536%, respectively. RMSE values were 21.5646, 15.2017, 17.0091, 11.9879 and 12.1691, respectively. The R2 values were 0.9043, 0.9315, 0.9266, 0.9668, and 0.9717, respectively. At the same time, the reliability of the HGS-RF model was supplemented by taking the test data of the Zhengjia1204 coal mining face as an example. Finally, the model was applied to the prediction of borehole spontaneous combustion in the Jinniu Coal Mine, Shanxi Province. The prediction results show that the HGS-RF model can predict the spontaneous combustion temperature of different boreholes quickly and accurately. The results show that the HGS-RF model is more universal and stable than other models, indicating that the HGS-RF model is more suitable for the prediction of borehole spontaneous combustion. Full article
(This article belongs to the Special Issue Advance in Fire Safety Science)
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