Bayesian network methodology for post-earthquake infrastructure risk management Armen Der Kiureghian, University of California, Berkeley, CA, USA Michelle Bensi, University of California, Berkeley, CA, USA Daniel Straub, University of California, Berkeley, CA, USA

2009 ◽  
pp. 221-234
Keyword(s):  
Risk Management ◽  
Bayesian Network ◽  
University Of California

scholarly journals Flood Risk Analysis of Different Climatic Phenomena during Flood Season Based on Copula-based Bayesian Network Method: A Case Study of Taihu Basin, China

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1534 ◽  
Author(s):  
Luo ◽  
Dong ◽  
Guan ◽  
Liu
Keyword(s):  
Risk Management ◽  
Risk Analysis ◽  
Bayesian Network ◽  
Water Level ◽  
Flood Risk ◽  
Taihu Lake ◽  
Flood Risk Management ◽  
Management Model ◽  
Flood Season ◽  
Taihu Basin

We propose a flood risk management model for the Taihu Basin, China, that considers the spatial and temporal differences of flood risk caused by the different climatic phenomena. In terms of time, the probability distribution of climatic phenomenon occurrence time was used to divide the flood season into plum rain and the typhoon periods. In terms of space, the Taihu Basin was divided into different sub-regions by the Copula functions. Finally, we constructed a flood risk management model using the Copula-based Bayesian network to analyze the flood risk. The results showed the plum rain period occurs from June 24 to July 21 and the typhoon period from July 22 to September 22. Considering the joint distribution of sub-region precipitation and the water level of Taihu Lake, we divided the Taihu Basin into three sub-regions (P-I, P-II, and P-III) for risk analysis in the plum rain period. However, the Taihu Basin was used as a whole for flood risk analysis in the typhoon period. Risk analysis indicated a probability of 2.4%, and 0.8%, respectively, for future adverse drainage during the plum rain period and the typhoon period, the flood risk increases rapidly with the rising water level in the Taihu Lake.

Application of BBN in Information Systems and Operational Risk Management

2017 ◽  
pp. 108-140
Keyword(s):  
Risk Management ◽  
Information Systems ◽  
Data Collection ◽  
Bayesian Network ◽  
Utility Theory ◽  
Operational Risk ◽  
Influence Diagrams ◽  
Learning From Data ◽  
Definition Of ◽  
Parameter Values

In this chapter, the model used to measure and maximize IS availability is described. The method of selecting independent variables will be presented, with a detailed definition of each variable in the model. This section presents a model based on Bayesian network, utility theory and influence diagrams. Finally, a method for probability elicitation through an interview with domains experts will be described, as recommended data collection model, for cases where it is not possible to set parameter' values based on learning from data.

Predictive Abnormal Events Analysis Using Continuous Bayesian Network

2017 ◽  
Vol 3 (4) ◽  
Author(s):  
Guozheng Song ◽  
Faisal Khan ◽  
Ming Yang ◽  
Hangzhou Wang
Keyword(s):  
Risk Management ◽  
Monte Carlo ◽  
Markov Chain ◽  
Bayesian Network ◽  
Posterior Distributions ◽  
Inherent Limitation ◽  
Reliable Prediction ◽  
Dynamic Prediction ◽  
Abnormal Events

The reliable prediction and diagnosis of abnormal events provide much needed guidance for risk management. The traditional Bayesian network (traditional BN) has been used to dynamically predict and diagnose abnormal events. However, its inherent limitation caused by discrete categorization of random variables degrades the assessment reliability. This paper applied a continuous Bayesian network (CBN)-based model to reduce the above-mentioned limitation. To compute complex posterior distributions of CBN, the Markov chain Monte Carlo method (MCMC) was used. A case study was conducted to demonstrate the application of CBN, based on which a comparative analysis of the traditional BN and CBN was presented. This work highlights that the use of CBN can overcome the drawbacks of traditional BN to make dynamic prediction and diagnosis analysis more reliable.

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