A Trustworthy Classification Model for Intelligent Building Fire Risk
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....
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9682751/ |
| _version_ | 1818715870292082688 |
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| author | Weilin Wu Yixiang Chen |
| author_facet | Weilin Wu Yixiang Chen |
| author_sort | Weilin Wu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-17T19:10:14Z |
| format | Article |
| id | doaj.art-db03f83df1b44800b655ffe376dbfdca |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T19:10:14Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-db03f83df1b44800b655ffe376dbfdca2022-12-21T21:35:53ZengIEEEIEEE Access2169-35362022-01-0110103711038310.1109/ACCESS.2022.31436149682751A Trustworthy Classification Model for Intelligent Building Fire RiskWeilin Wu0https://orcid.org/0000-0003-2244-6495Yixiang Chen1https://orcid.org/0000-0003-1235-5530MoE Engineering Center for Software/Hardware Co-Design Technology and Application, East China Normal University, Shanghai, ChinaMoE Engineering Center for Software/Hardware Co-Design Technology and Application, East China Normal University, Shanghai, ChinaThe 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.https://ieeexplore.ieee.org/document/9682751/Intelligent buildingfire risk assessmentfire risk trustworthy classification modelBayesian networktrustworthy computing |
| spellingShingle | Weilin Wu Yixiang Chen A Trustworthy Classification Model for Intelligent Building Fire Risk IEEE Access Intelligent building fire risk assessment fire risk trustworthy classification model Bayesian network trustworthy computing |
| title | A Trustworthy Classification Model for Intelligent Building Fire Risk |
| title_full | A Trustworthy Classification Model for Intelligent Building Fire Risk |
| title_fullStr | A Trustworthy Classification Model for Intelligent Building Fire Risk |
| title_full_unstemmed | A Trustworthy Classification Model for Intelligent Building Fire Risk |
| title_short | A Trustworthy Classification Model for Intelligent Building Fire Risk |
| title_sort | trustworthy classification model for intelligent building fire risk |
| topic | Intelligent building fire risk assessment fire risk trustworthy classification model Bayesian network trustworthy computing |
| url | https://ieeexplore.ieee.org/document/9682751/ |
| work_keys_str_mv | AT weilinwu atrustworthyclassificationmodelforintelligentbuildingfirerisk AT yixiangchen atrustworthyclassificationmodelforintelligentbuildingfirerisk AT weilinwu trustworthyclassificationmodelforintelligentbuildingfirerisk AT yixiangchen trustworthyclassificationmodelforintelligentbuildingfirerisk |