Risk Analysis of Discrete Dynamic Event Tree Based on Dynamic Bayesian Network

Fan, Liming; Jia, Lulu; Ren, Yi; Wang, Kun-Sheng; Yang, Dezhen

doi:10.1109/qr2mse46217.2019.9021120

Cited by 1 publication

(1 citation statement)

References 9 publications

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“…Dong et al [21] used a dynamic Bayesian network to model the characteristics of battery degradation during charging and used the established dynamic Bayesian network to predict the health of the battery. Some researchers also used Bayesian networks for risk assessment in different scenarios [22][23][24][25][26]. For example, Zhang et al [22] proposed fuzzy probabilistic Bayesian networks for network security assessment in industrial control systems; Ma et al [23] used the dynamic Bayesian network to make a reasonable quantitative assessment of the risks associated with driving, etc.…”

Section: Related Workmentioning

confidence: 99%

Predicting Typhoon Flood in Macau Using Dynamic Gaussian Bayesian Network and Surface Confluence Analysis

Zou,

Chu,

Dai

et al. 2024

Mathematics

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A typhoon passing through or making landfall in a coastal city may result in seawater intrusion and continuous rainfall, which may cause urban flooding. The urban flood disaster caused by a typhoon is a dynamic process that changes over time, and a dynamic Gaussian Bayesian network (DGBN) is used to model the time series events in this paper. The scene data generated by each typhoon are different, which means that each typhoon has different characteristics. This paper establishes multiple DGBNs based on the historical data of Macau flooding caused by multiple typhoons, and similar analysis is made between the scene data related to the current flooding to be predicted and the scene data of historical flooding. The DGBN most similar to the scene characteristics of the current flooding is selected as the predicting network of the current flooding. According to the topography, the influence of the surface confluence is considered, and the Manning formula analysis method is proposed. The Manning formula is combined with the DGBN to obtain the final prediction model, DGBN-m, which takes into account the effects of time series and non-time-series factors. The flooding data provided by the Macau Meteorological Bureau are used to carry out experiments, and it is proved that the proposed model can predict the flooding depth well in a specific area of Macau under the condition of a small amount of data and that the best predicting accuracy can reach 84%. Finally, generalization analysis is performed to further confirm the validity of the proposed model.

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