Improved Bayesian Network-Based Risk Model and Its Application in Disaster Risk Assessment
Abstract The application of Bayesian network (BN) theory in risk assessment is an emerging trend. But in cases where data are incomplete and variables are mutually related, its application is restricted. To overcome these problems, an improved BN assessment model with parameter retrieval and decorre...
Published in: | International Journal of Disaster Risk Science |
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ftdoajarticles:oai:doaj.org/article:8445e0ad818646e8a1b093686827b9a4 2023-05-15T18:18:21+02:00 Improved Bayesian Network-Based Risk Model and Its Application in Disaster Risk Assessment Ming Li Mei Hong Ren Zhang 2018-05-01T00:00:00Z https://doi.org/10.1007/s13753-018-0171-z https://doaj.org/article/8445e0ad818646e8a1b093686827b9a4 EN eng SpringerOpen http://link.springer.com/article/10.1007/s13753-018-0171-z https://doaj.org/toc/2095-0055 https://doaj.org/toc/2192-6395 doi:10.1007/s13753-018-0171-z 2095-0055 2192-6395 https://doaj.org/article/8445e0ad818646e8a1b093686827b9a4 International Journal of Disaster Risk Science, Vol 9, Iss 2, Pp 237-248 (2018) Bayesian network Genetic algorithm Grey relational analysis Risk assessment Disasters and engineering TA495 article 2018 ftdoajarticles https://doi.org/10.1007/s13753-018-0171-z 2022-12-31T03:28:00Z Abstract The application of Bayesian network (BN) theory in risk assessment is an emerging trend. But in cases where data are incomplete and variables are mutually related, its application is restricted. To overcome these problems, an improved BN assessment model with parameter retrieval and decorrelation ability is proposed. First, multivariate nonlinear planning is applied to the feedback error learning of parameters. A genetic algorithm is used to learn the probability distribution of nodes that lack quantitative data. Then, based on an improved grey relational analysis that considers the correlation of variation rate, the optimal weight that characterizes the correlation is calculated and the weighted BN is improved for decorrelation. An improved risk assessment model based on the weighted BN then is built. An assessment of sea ice disaster shows that the model can be applied for risk assessment with incomplete data and variable correlation. Article in Journal/Newspaper Sea ice Directory of Open Access Journals: DOAJ Articles International Journal of Disaster Risk Science 9 2 237 248 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
Bayesian network Genetic algorithm Grey relational analysis Risk assessment Disasters and engineering TA495 |
spellingShingle |
Bayesian network Genetic algorithm Grey relational analysis Risk assessment Disasters and engineering TA495 Ming Li Mei Hong Ren Zhang Improved Bayesian Network-Based Risk Model and Its Application in Disaster Risk Assessment |
topic_facet |
Bayesian network Genetic algorithm Grey relational analysis Risk assessment Disasters and engineering TA495 |
description |
Abstract The application of Bayesian network (BN) theory in risk assessment is an emerging trend. But in cases where data are incomplete and variables are mutually related, its application is restricted. To overcome these problems, an improved BN assessment model with parameter retrieval and decorrelation ability is proposed. First, multivariate nonlinear planning is applied to the feedback error learning of parameters. A genetic algorithm is used to learn the probability distribution of nodes that lack quantitative data. Then, based on an improved grey relational analysis that considers the correlation of variation rate, the optimal weight that characterizes the correlation is calculated and the weighted BN is improved for decorrelation. An improved risk assessment model based on the weighted BN then is built. An assessment of sea ice disaster shows that the model can be applied for risk assessment with incomplete data and variable correlation. |
format |
Article in Journal/Newspaper |
author |
Ming Li Mei Hong Ren Zhang |
author_facet |
Ming Li Mei Hong Ren Zhang |
author_sort |
Ming Li |
title |
Improved Bayesian Network-Based Risk Model and Its Application in Disaster Risk Assessment |
title_short |
Improved Bayesian Network-Based Risk Model and Its Application in Disaster Risk Assessment |
title_full |
Improved Bayesian Network-Based Risk Model and Its Application in Disaster Risk Assessment |
title_fullStr |
Improved Bayesian Network-Based Risk Model and Its Application in Disaster Risk Assessment |
title_full_unstemmed |
Improved Bayesian Network-Based Risk Model and Its Application in Disaster Risk Assessment |
title_sort |
improved bayesian network-based risk model and its application in disaster risk assessment |
publisher |
SpringerOpen |
publishDate |
2018 |
url |
https://doi.org/10.1007/s13753-018-0171-z https://doaj.org/article/8445e0ad818646e8a1b093686827b9a4 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
International Journal of Disaster Risk Science, Vol 9, Iss 2, Pp 237-248 (2018) |
op_relation |
http://link.springer.com/article/10.1007/s13753-018-0171-z https://doaj.org/toc/2095-0055 https://doaj.org/toc/2192-6395 doi:10.1007/s13753-018-0171-z 2095-0055 2192-6395 https://doaj.org/article/8445e0ad818646e8a1b093686827b9a4 |
op_doi |
https://doi.org/10.1007/s13753-018-0171-z |
container_title |
International Journal of Disaster Risk Science |
container_volume |
9 |
container_issue |
2 |
container_start_page |
237 |
op_container_end_page |
248 |
_version_ |
1766194903033839616 |