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

被引：0
作者：
Ming Li
Mei Hong
Ren Zhang
机构：
[1] National University of Defense Technology,Research Center of Ocean Environment Numerical Simulation, Institute of Meteorology and Oceanography
[2] Nanjing University of Information Science and Technology,Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disaster
来源：
International Journal of Disaster Risk Science | 2018年 / 9卷
关键词：
Bayesian network; Genetic algorithm; Grey relational analysis; Risk assessment;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
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收藏
页码：237 / 248
页数：11
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