Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.5
3.2
Journals
Fire
Special Issues
Intelligent Fire Protection
share announcement

Intelligent Fire Protection

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

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 8273

Special Issue Editors

Dr. Fei Wang

E-Mail Website
Guest Editor
Institute of Safety Science and Technology, Tsinghua Shenzhen International Graduate School, Shenzhen 518055, China
Interests: disaster modelling; WUI fire; fire protection; ICT for emergency management
Prof. Dr. Siu Ming Lo

E-Mail Website
Guest Editor
Department of Architecture and Civil Engineering, City University of Hong Kong, Kowloon, Hong Kong
Interests: fire safety engineering; spatial planning for pedestrian flow traffic and evacuation modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to submit research papers on Intelligent Fire Prevention in this Special Issue of the journal Fire. As is known, fire (wildfires and building fires) is one of the most common and hazardous disasters, causing profound impacts on natural ecology, socio-economy, and even human lives. Effective fire prevention means are essential in assisting emergency agencies when making precise decisions; this could enable enhancing urban resilience and reducing losses. Therefore, it is worthwhile to comprehensively investigate fire prevention with more intelligent and advanced theories, methodology methods and technologies.

This Special Issue aims to widely discuss the topic of Intelligent Fire Protection (i.e., theory, algorithm, model, system, and equipment) on both wildfires and building fires.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • The theory and methodology of fire protection;
  • Intelligent modeling methods (i.e., deep learning, reinforcement learning) on risk assessment, fire detection and numerical modeling;
  • Informatic decision-making system;
  • Fire protection technology and equipment.

I look forward to receiving your contributions.

Dr. Fei Wang
Prof. Dr. Siu Ming Lo
Guest Editors

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

  • fire science
  • fire protection
  • intelligent models
  • decision-making system
  • protection equipment
  • risk assessment
  • fire detection
  • AI

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review, Other

14 pages, 5237 KiB  
Article
Comparing Accuracy of Wildfire Spread Prediction Models under Different Data Deficiency Conditions
by Jiahao Zhou, Wenyu Jiang, Fei Wang, Yuming Qiao and Qingxiang Meng
Fire 2024, 7(4), 141; https://doi.org/10.3390/fire7040141 - 16 Apr 2024
Viewed by 611
Abstract
Wildfire is one of the most severe natural disasters globally, profoundly affecting natural ecology, economy, and health and safety. Precisely predicting the spread of wildfires has become an important research topic. Current fire spread prediction models depend on inputs from a variety of [...] Read more.
Wildfire is one of the most severe natural disasters globally, profoundly affecting natural ecology, economy, and health and safety. Precisely predicting the spread of wildfires has become an important research topic. Current fire spread prediction models depend on inputs from a variety of geographical and environmental variables. However, unlike the ideal conditions simulated in the laboratory, data gaps often occur in real wildfire scenarios, posing challenges to the accuracy and robustness of predictions. It is necessary to explore the extent to which different missing items affect prediction accuracy, thereby providing rational suggestions for emergency decision-making. In this paper, we tested how different conditions of missing data affect the prediction accuracy of existing wildfire spread models and quantified the corresponding errors. The final experimental results suggest that it is necessary to judge the potential impact of data gaps based on the geographical conditions of the study area appropriately, as there is no significant pattern of behavior yet identified. This study aims to simulate the impact of data scarcity on the accuracy of wildfire spread prediction models in real scenarios, thereby enabling researchers to better understand the priority of different environmental variables for the model and identify the acceptable degree of missing data and the indispensable data attributes. It offers new insights for developing spread prediction models applicable to real-world scenarios and rational assessment of the effectiveness of model outcomes. Full article
(This article belongs to the Special Issue Intelligent Fire Protection)
►▼ Show Figures

Figure 1

18 pages, 2375 KiB  
Article
Utilizing Volunteered Geographic Information for Real-Time Analysis of Fire Hazards: Investigating the Potential of Twitter Data in Assessing the Impacted Areas
by Janine Florath, Jocelyn Chanussot and Sina Keller
Fire 2024, 7(1), 6; https://doi.org/10.3390/fire7010006 - 21 Dec 2023
Cited by 1 | Viewed by 1273
Abstract
Natural hazards such as wildfires have proven to be more frequent in recent years, and to minimize losses and activate emergency response, it is necessary to estimate their impact quickly and consequently identify the most affected areas. Volunteered geographic information (VGI) data, particularly [...] Read more.
Natural hazards such as wildfires have proven to be more frequent in recent years, and to minimize losses and activate emergency response, it is necessary to estimate their impact quickly and consequently identify the most affected areas. Volunteered geographic information (VGI) data, particularly from the social media platform Twitter, now X, are emerging as an accessible and near-real-time geoinformation data source about natural hazards. Our study seeks to analyze and evaluate the feasibility and limitations of using tweets in our proposed method for fire area assessment in near-real time. The methodology involves weighted barycenter calculation from tweet locations and estimating the affected area through various approaches based on data within tweet texts, including viewing angle to the fire, road segment blocking information, and distance to fire information. Case study scenarios are examined, revealing that the estimated areas align closely with fire hazard areas compared to remote sensing (RS) estimated fire areas, used as pseudo-references. The approach demonstrates reasonable accuracy with estimation areas differing by distances of 2 to 6 km between VGI and pseudo-reference centers and barycenters differing by distances of 5 km on average from pseudo-reference centers. Thus, geospatial analysis on VGI, mainly from Twitter, allows for a rapid and approximate assessment of affected areas. This capability enables emergency responders to coordinate operations and allocate resources efficiently during natural hazards. Full article
(This article belongs to the Special Issue Intelligent Fire Protection)
►▼ Show Figures

Figure 1

18 pages, 2652 KiB  
Article
Risk Analysis of Laboratory Fire Accidents in Chinese Universities by Combining Association Rule Learning and Fuzzy Bayesian Networks
by Fuqiang Yang, Xin Li, Shuaiqi Yuan and Genserik Reniers
Fire 2023, 6(8), 306; https://doi.org/10.3390/fire6080306 - 07 Aug 2023
Viewed by 1278
Abstract
Targeting the challenges in the risk analysis of laboratory fire accidents, particularly considering fire accidents in Chinese universities, an integrated approach is proposed with the combination of association rule learning, a Bayesian network (BN), and fuzzy set theory in this study. The proposed [...] Read more.
Targeting the challenges in the risk analysis of laboratory fire accidents, particularly considering fire accidents in Chinese universities, an integrated approach is proposed with the combination of association rule learning, a Bayesian network (BN), and fuzzy set theory in this study. The proposed approach has the main advantages of deriving conditional probabilities of BN nodes based on historical accident data and association rules (ARs) and making good use of expert elicitation by using an augmented fuzzy set method. In the proposed approach, prior probabilities of the cause nodes are determined based on expert elicitation with the help of an augmented fuzzy set method. The augmented fuzzy set method enables the effective aggregation of expert opinions and helps to reduce subjective bias in expert elicitations. Additionally, an AR algorithm is applied to determine the probabilistic dependency between the BN nodes based on the historical accident data of Chinese universities and further derive conditional probability tables. Finally, the developed fuzzy Bayesian network (FBN) model was employed to identify critical causal factors with respect to laboratory fire accidents in Chinese universities. The obtained results show that H4 (bad safety awareness), O1 (improper storage of hazardous chemicals), E1 (environment with hazardous materials), and M4 (inadequate safety checks) are the four most critical factors inducing laboratory fire accidents. Full article
(This article belongs to the Special Issue Intelligent Fire Protection)
►▼ Show Figures

Figure 1

Review

Jump to: Research, Other

51 pages, 15309 KiB  
Review
Recent Advances and Emerging Directions in Fire Detection Systems Based on Machine Learning Algorithms
by Bogdan Marian Diaconu
Fire 2023, 6(11), 441; https://doi.org/10.3390/fire6110441 - 17 Nov 2023
Cited by 1 | Viewed by 3066
Abstract
Fire detection is a critical safety issue due to the major and irreversible consequences of fire, from economic prejudices to loss of life. It is therefore of utmost importance to design reliable, automated systems that can issue early alarms. The objective of this [...] Read more.
Fire detection is a critical safety issue due to the major and irreversible consequences of fire, from economic prejudices to loss of life. It is therefore of utmost importance to design reliable, automated systems that can issue early alarms. The objective of this review is to present the state of the art in the area of fire detection, prevention and propagation modeling with machine learning algorithms. In order to understand how an artificial intelligence application penetrates an area of fire detection, a quantitative scientometric analysis was first performed. A literature search process was conducted on the SCOPUS database using terms and Boolean expressions related to fire detection techniques and machine learning areas. A number of 2332 documents were returned upon the bibliometric analysis. Fourteen datasets used in the training of deep learning models were examined, discussing critically the quality parameters, such as the dataset volume, class imbalance, and sample diversity. A separate discussion was dedicated to identifying issues that require further research in order to provide further insights, and faster and more accurate models.. The literature survey identified the main issues the current research should address: class imbalance in datasets, misclassification, and datasets currently used in model training. Recent advances in deep learning models such as transfer learning and (vision) transformers were discussed. Full article
(This article belongs to the Special Issue Intelligent Fire Protection)
►▼ Show Figures

Figure 1

Other

Jump to: Research, Review

15 pages, 1079 KiB  
Essay
Fire Source Determination Method for Underground Commercial Streets Based on Perception Data and Machine Learning
by Yunhao Yang, Yuanyuan Zhang, Guowei Zhang, Tianyao Tang, Zhaoyu Ning, Zhiwei Zhang and Ziming Zhao
Fire 2024, 7(2), 53; https://doi.org/10.3390/fire7020053 - 10 Feb 2024
Viewed by 1121
Abstract
Determining fire source in underground commercial street fires is critical for fire analysis. This paper proposes a method based on temperature and machine learning to determine information about fire source in underground commercial street fires. Data was obtained through consolidated fire and smoke [...] Read more.
Determining fire source in underground commercial street fires is critical for fire analysis. This paper proposes a method based on temperature and machine learning to determine information about fire source in underground commercial street fires. Data was obtained through consolidated fire and smoke transport (CFAST) software, and a fire database was established based on the sampling to ascertain fire scenarios. Temperature time series were chosen for feature processing, and three machine learning models for fire source determination were established: decision tree, random forest, and LightGBM. The results indicated that the trained models can determine fire source information based on processed features, achieving a precision exceeding 95%. Among these, the LightGBM model exhibited superior performance, with macro averages of precision, recall, and F1 score being 99.01%, 98.45%, and 99.04%, respectively, and a kappa value of 98.81%. The proposed method for determining the fire source provides technical support for grasping the fire situation in underground commercial streets and has good application prospects. Full article
(This article belongs to the Special Issue Intelligent Fire Protection)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop