Dynamic Risk Analysis of Deepwater Gas Hydrate Drilling with a Riserless Drilling System Based on Uncertain Dynamic Bayesian Network Model

2022 ◽  
Vol 8 (1) ◽  
Author(s):  
Chuan Wang ◽  
Yong Xia ◽  
Qiao Zeng ◽  
Jiajun Ma ◽  
Guorong Wang ◽  
...  
Keyword(s):  
Risk Analysis ◽  
Bayesian Network ◽  
Network Model ◽  
Gas Hydrate ◽  
Dynamic Bayesian Network ◽  
Bayesian Network Model ◽  
Dynamic Risk ◽  
Drilling System

Dynamic Bayesian Network Model of a Country’s Economic Extension

2020 ◽  
pp. 145-159
Author(s):  
Mariia Voronenko ◽  
Dmytro Nikytenko ◽  
Jan Krejci ◽  
Nataliia Krugla ◽  
Oleksandr Naumov ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Network Model ◽  
Dynamic Bayesian Network ◽  
Bayesian Network Model

scholarly journals Risk Analysis of Earth-Rock Dam Breach Based on Dynamic Bayesian Network

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2305 ◽  
Author(s):  
Li ◽  
Wang ◽  
Ge ◽  
Wei ◽  
Li
Keyword(s):  
Risk Analysis ◽  
Bayesian Network ◽  
Dynamic Bayesian Network ◽  
Time Factors ◽  
Extended Model ◽  
Bayesian Network Model ◽  
Dam Breach ◽  
The Earth ◽  
Operation Period ◽  
Static Risk

Despite the fact that the Bayesian network has great advantages in logical reasoning and calculation compared with the other traditional risk analysis methods, there are still obvious shortcomings in the study of dynamic risk. The risk factors of the earth-rock dam breach are complex, which vary with time during the operation period. Static risk analysis, limited to a specific period of time, cannot meet the needs of comprehensive assessment and early warning. By introducing time factors, a dynamic Bayesian network model was established to study the dynamic characteristics of dam-breach probability. Combined with the calculation of the conditional probability of nodes based on the Leaky Noisy-Or gate extended model, the reasoning results of Bayesian networks were modified by updating the data of different time nodes. Taking an earth-rock dam as an example, the results show that it has less possibility to breach and keep stable along the time axis. Moreover, the factors with vulnerability and instability were found effective, which could provide guidance for dam risk management.

scholarly journals Time-varying dynamic Bayesian network model and its application to brain connectivity using electrocorticograph

2016 ◽  
Vol 65 (3) ◽  
pp. 038702
Author(s):  
Guo Miao-Miao ◽  
Wang Yu-Jing ◽  
Xu Gui-Zhi ◽  
Griffin Milsap ◽  
Nitish V. Thakor ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Network Model ◽  
Brain Connectivity ◽  
Dynamic Bayesian Network ◽  
Time Varying ◽  
Bayesian Network Model
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