Safety state evaluation and risk management of navigation environment in harbour waters based on Bayesian network

2016 ◽  
Author(s):  
Zhao Chen ◽  
Qingnian Zhang ◽  
Xingguo Wu ◽  
Jie Yang ◽  
Xiaoya Zhang
Keyword(s):  
Risk Management ◽  
Bayesian Network ◽  
State Evaluation

scholarly journals Digital Risk Management for Data Attacks against State Evaluation

2019 ◽  
Vol 8 (9S4) ◽  
pp. 197-201
Keyword(s):  
Risk Management ◽  
Cyber Attacks ◽  
Framework Design ◽  
State Evaluation ◽  
Data Trustworthiness ◽  
Data Location

Understanding reasonable framework cyber attacks is essential for creating material assurance and recuperation measures. Propelled attacks follow exploited contact at diminished expenses and recognize capacity. This paper behaviors chance investigation of joined data trustworthiness and handiness attacks against the office framework state evaluation. We will in general contrast the consolidated attacks and unadulterated honesty attacks - false data infusion attacks. A safety record for defenselessness appraisal to those two sorts of attacks is arranged and created because a blended number connected science drawback. We will in general demonstrate that such joined attacks will prevail with less assets than false data infusion attacks. The consolidated attacks with confined data of the framework design also open gifts to keep camouflage against the undesirable data location. At last, the risk of joined attacks to dependable framework activity is assessed abuse the outcomes from defenselessness evaluation and attacks sway examination. The discoveries during this paper are substantial and upheld by a top to bottom contextual investigation

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.

Sign in / Sign up

Export Citation Format

Share Document