A Three‐Part Bayesian Network for Modeling Dwelling Fires and Their Impact upon People and Property

Matellini, DB; Wall, Alan; Jenkinson, Ian; Wang, J.; Pritchard, Robert W.

doi:10.1111/risa.13113

Cited by 11 publications

(13 citation statements)

References 46 publications

(64 reference statements)

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“…Combining this with the general actual situation of tourism scenic spots and reference to the prior related studies [66][67][68], we establish a fire safety evaluation index system as shown in Table 2. As we can see, the system is a hierarchical structure, including the target level, the base level, and the criteria level.…”

Section: Establishing the Evaluation Indexmentioning

confidence: 99%

Fire Safety Evaluation for Scenic Spots: An Evidential Best-Worst Method

Chen

Xie

2021

Journal of Mathematics

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Fire safety plays a vital role in tourism management, which can cause significant loss of life and property. It is necessary to present an efficient fire safety evaluation for scenic spots. However, some key issues are not well addressed in existing methods. For example, how to model experts’ opinions and how to combine them are still open problems. To address these issues, a new evaluation method based on the Dempster–Shafer evidence theory and best-worst method is presented. First, a fire safety evaluation index system is constructed using the domino model. Domain experts can evaluate different factors with linguistic assessments. The best-worst method is used to determine the weights of different factors. Then, these weighted linguistic assessments are efficiently fused by Dempster’s combination rule to obtain the evaluation result. Finally, a case study is illustrated to demonstrate the efficiency of the proposed method in fire safety evaluation for scenic spots. The main contribution of the proposed method is to represent and handle the uncertainty in experts’ linguistic assessments, so as to decrease the uncertainty and improve decision making. In addition, the weight determination method BWM is easier and more reliable than the existing method AHP.

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Section: Establishing the Evaluation Indexmentioning

confidence: 99%

Fire Safety Evaluation for Scenic Spots: An Evidential Best-Worst Method

Chen

Xie

2021

Journal of Mathematics

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“…Bayesian Networks (BN) are also applied to dwelling fires in [33]. In particular, a three-part BN model is developed to study dwelling fires and improve confidence in dwelling fire safety assessment.…”

Section: B Machine Learning Applied To Fire Datasetsmentioning

confidence: 99%

The Categorical Data Conundrum: Heuristics for Classification Problems—A Case Study on Domestic Fire Injuries

et al. 2022

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Machine learning is well developed amongst the scientific community in terms of theoretical foundations (statistics and algorithms) and frameworks (Tensorflow, PyTorch, H2O). However, machine learning is heavily focused on numerical data, or numerical data mixed with some categorical data. For numerical datasets, scientists and engineers can enjoy reasonable success with only a limited knowledge of theoretical foundations and the inner workings of machine learning frameworks. However, it is a different story when dealing with purely categorical datasets, which require a deeper understanding of machine learning frameworks and associated encodings and algorithms in order to achieve success. This paper addresses the issues in handling purely categorical datasets for multi-classification problems and provides a set of heuristics for dealing with purely categorical data. In particular, issues such as pre-processing, feature encoding and algorithm selection are considered. The heuristics are then demonstrated through a case study, based on a categorical data set of domestic fire injuries, covering a 10-year period. Novel contributions are made through the heuristics and the performance analysis of different encoding techniques. The case study itself also makes a novel contribution through the classification of different types of injuries, based on related features.

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“…(1) Setting prior probability Firstly, it is necessary to estimate the prior probability of 63 leaf nodes(input nodes). The prior probability in this paper came from fire data analysis, expert knowledge, and on-site sampling statistics of urban buildings over the past years [9,11,13,20]. As shown in figure 5, the node ClosedDoorAnd-Window shows that normally fireprevention doors and windows should be closed during fire inspection.…”

Section: ) Determine Network Parametersmentioning

confidence: 99%

“…Zhang et al [10] propose a Bayesian network analysis model of the fire in the dormitory of colleges and universities, establish a relevant index system, build a Bayesian model for fire risk analysis. Matellini et al [11] represent a three-part BN to simulate the different stages of ordinary residential fires from fire to extinguish. Liu et al [12] construct a BN model to predict the fire risk of urban buildings to calculate the probability of building fires.…”

Section: Introductionmentioning

confidence: 99%

A Trustworthy Classification Model for Intelligent Building Fire Risk

Wang

Chen

2022

IEEE Access

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The occurrence of intelligent building fires causes huge economic losses to the country and society, and even people's safety. It is necessary to accurately assess the degree of intelligent building fire risk so that the fire emergency management department can make scientific decisions. In this paper, a trustworthy classification model for intelligent building fire risk is proposed, which provides a scientific and reasonable model supporting the classification assessment of intelligent building fire risk. The model integrates Bayesian Network (BN) and software trustworthy computing approach. BN is used to calculate the risk value of attributes that describe the fire risk situation of the intelligent building from 7 profiles. Based on the fire risk attribute values, trustworthy computing is adopted to classify the fire risk into 5 ranks which indicates the severity degree of building fire risk: the higher the rank is, the greater the harm is. Taking the Shanghai Jing'an 11.15 fire as an example, the result confirms that the method proposed in this paper has good theoretical significance and practical value. In addition, we compare our method with 3 fire risk assessment methods in the reference. The comparisons illustrate that the trustworthy classification model proposed in this paper is more comprehensive, rational, and scientific.

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A Three‐Part Bayesian Network for Modeling Dwelling Fires and Their Impact upon People and Property

Cited by 11 publications

References 46 publications

Fire Safety Evaluation for Scenic Spots: An Evidential Best-Worst Method

Fire Safety Evaluation for Scenic Spots: An Evidential Best-Worst Method

The Categorical Data Conundrum: Heuristics for Classification Problems—A Case Study on Domestic Fire Injuries

A Trustworthy Classification Model for Intelligent Building Fire Risk

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