scholarly journals Impact Mechanism of the Ecological Vulnerability of Highly Developed Islands Based on the Bayesian Network Model—Applied to the Changshan Islands

2021 ◽  
Vol 18 (8) ◽  
pp. 4150
Author(s):  
Keyu Qin ◽  
Haijun Huang ◽  
Jingya Liu ◽  
Liwen Yan ◽  
Yanxia Liu ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Network Model ◽  
Geographical Information ◽  
Global Changes ◽  
Bayesian Network Model ◽  
Island Ecosystems ◽  
Impact Mechanism ◽  
The North ◽  
Ecological Vulnerability ◽  
The Impact

Islands are one of the most sensitive interfaces between global changes and land and sea dynamic effects, with high sensitivity and low stability. Therefore, under the dynamic coupling effect of human activities and frequent natural disasters, the vulnerability of the ecological environment of islands shows the characteristics of complexity and diversity. For the protection of island ecosystems, a system for the assessment of island ecosystems and studies on the mechanism of island ecological vulnerability are highly crucial. In this study, the North and South Changshan Islands of China were selected as the study area. Considering various impact factors of island ecological vulnerability, the geographical information systems (GIS) spatial analysis, field surveys, data sampling were used to evaluate island ecological vulnerability. The Bayesian network model was used to explore the impact mechanism of ecological vulnerability. The results showed that the ecological vulnerability of the North Changshan Island is higher than that of the South Changshan Island. Among all the indicators, the proportion of net primary productivity (NPP) and the steep slope has the strongest correlation with ecological vulnerability. This study can be used as references in the relevant departments to formulate management policies and promote the sustainable development of islands and their surrounding waters

scholarly journals The efficiency improvement of port state control based on ship accident Bayesian networks

2018 ◽  
Vol 233 (1) ◽  
pp. 71-83 ◽  
Author(s):  
Lixian Fan ◽  
Zimeng Zhang ◽  
Jingbo Yin ◽  
Xingyuan Wang
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Network Model ◽  
Search Algorithms ◽  
Efficiency Improvement ◽  
State Control ◽  
Bayesian Network Model ◽  
Structural Connections ◽  
Structural Networks ◽  
The Impact

Ship accident has always been the focus in shipping and it is concerned by port state authorities. This study tries to investigate the impact of various factors on ship accident along with the port state’s detected deficiency items. Very importantly, it manages to identify the structural connections between the checked deficiency items. The data used in this study are mainly from Lloyd’s register of shipping, International Maritime Organization and Tokyo Memorandum of Understanding, with a total of 64,847 observations obtained. The Bayesian network model is employed and the Greedy thick thinning and Bayesian search algorithms are used to learn the structural networks. In addition to the impacts of the deficiency items and the ship inherent attributes on ship accidents, this study identifies some key deficiency items for port states. It also analyzes the intense connections between the key deficiency items with others. This helps simplify the port state’s inspection procedure and improve operational efficiency.

scholarly journals Predicting the risk of GenX contamination in private well water using a machine-learned Bayesian network model

2021 ◽  
pp. 125075
Author(s):  
Javad Roostaei ◽  
Sarah Colley ◽  
Riley Mulhern ◽  
Andrew A. May ◽  
Jacqueline MacDonald Gibson
Keyword(s):  
Bayesian Network ◽  
Network Model ◽  
Well Water ◽  
Bayesian Network Model

scholarly journals Misper-Bayes: A Bayesian Network Model for Missing Person Investigations

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Denis Reilly ◽  
Mark Taylor ◽  
Paul Fergus ◽  
Carl Chalmers ◽  
Steven Thompson
Keyword(s):  
Bayesian Network ◽  
Network Model ◽  
Bayesian Network Model

Probabilistic Model-Based Fault Diagnosis of the Rotor System

2007 ◽  
Author(s):  
Jiye Shao ◽  
Rixin Wang ◽  
Jingbo Gao ◽  
Minqiang Xu
Keyword(s):  
Fault Diagnosis ◽  
Bayesian Network ◽  
Network Model ◽  
Rotating Machinery ◽  
Rotor System ◽  
Bayesian Network Model ◽  
Diagnosis System ◽  
Model Based ◽  
Core Components ◽  
Fault Diagnosis System

The rotor is one of the most core components of the rotating machinery and its working states directly influence the working states of the whole rotating machinery. There exists much uncertainty in the field of fault diagnosis in the rotor system. This paper analyses the familiar faults of the rotor system and the corresponding faulty symptoms, then establishes the rotor’s Bayesian network model based on above information. A fault diagnosis system based on the Bayesian network model is developed. Using this model, the conditional probability of the fault happening is computed when the observation of the rotor is presented. Thus, the fault reason can be determined by these probabilities. The diagnosis system developed is used to diagnose the actual three faults of the rotor of the rotating machinery and the results prove the efficiency of the method proposed.

Corrosion threat assessment of crude oil flow lines using Bayesian network model

2015 ◽  
Vol 50 (3) ◽  
pp. 236-247 ◽  
Author(s):  
G. Koch ◽  
F. Ayello ◽  
V. Khare ◽  
N. Sridhar ◽  
A. Moosavi
Keyword(s):  
Bayesian Network ◽  
Crude Oil ◽  
Network Model ◽  
Threat Assessment ◽  
Bayesian Network Model ◽  
Flow Lines ◽  
Oil Flow
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