Improved Bayesian Network-Based Risk Model and Its Application in Disaster Risk Assessment

Ming Li; Mei Hong; Ren Zhang

doi:10.1007/s13753-018-0171-z

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Article Navigation > International Journal of Disaster Risk Science > 2018 > 9(2): 237-248

Ming Li, Mei Hong, Ren Zhang. Improved Bayesian Network-Based Risk Model and Its Application in Disaster Risk Assessment[J]. International Journal of Disaster Risk Science, 2018, 9(2): 237-248. doi: 10.1007/s13753-018-0171-z

Citation:

Ming Li, Mei Hong, Ren Zhang. Improved Bayesian Network-Based Risk Model and Its Application in Disaster Risk Assessment[J]. International Journal of Disaster Risk Science, 2018, 9(2): 237-248. doi: 10.1007/s13753-018-0171-z

Citation:

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Improved Bayesian Network-Based Risk Model and Its Application in Disaster Risk Assessment

doi: 10.1007/s13753-018-0171-z

Ming Li¹,
Mei Hong^{1
,
,},
Ren Zhang^1,2

1 Research Center of Ocean Environment Numerical Simulation, Institute of Meteorology and Oceanography, National University of Defense Technology, Nanjing 211101, China;
2 Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disaster, Nanjing University of Information Science and Technology, Nanjing 210044, China

Funds:

This study was supported by the National Natural Science Foundation of China (Nos. 41375002, 51609254) and the Provincial Natural Science Fund (BK20161464) of Jiangsu.

Available Online: 2021-04-26

Abstract

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.
- Bayesian network,
- Genetic algorithm,
- Grey relational analysis,
- Risk assessment

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References

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