Hazard Assessment of Earthquake Disaster Chains Based on a Bayesian Network Model and ArcGIS

The impacts of earthquakes and secondary disasters on ecosystems and the environment are attracting increasing global attention. Development of uncertainty reasoning models offers a chance to research these complex correlations. The primary aim of this research was to construct a disaster chain hazard assessment model that combines a Bayesian Network model and the ArcGIS program software for Changbai Mountain, China, an active volcano with a spate of reported earthquakes, collapses, and landslide events. Furthermore, the probability obtained by the Bayesian Networks was used to determine the disaster chain probability and hazard intensity of the earthquake events, while ArcGIS was used to produce the disaster chain hazard map. The performance of the Bayesian Network model was measured by error rate and scoring rules. The confirmation of the outcomes of the disaster chain hazard assessment model shows that the model demonstrated good predictive performance on the basis of the area under the curve, which was 0.7929. From visual inspection of the produced earthquake disaster chain hazard map, highly hazardous zones are located within a 15 km radius from the Tianchi center, while the northern and the western parts of the studied area are characterized mainly by “very low” to “low” hazard values.

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Designing Cognitively Diagnostic Assessment for Algebraic Content Knowledge and Thinking Skills

International Education Studies ◽

10.5539/ies.v11n2p106 ◽

2018 ◽

Vol 11 (2) ◽

pp. 106 ◽

Cited By ~ 1

Author(s):

Zhidong Zhang

Keyword(s):

Bayesian Network ◽

Content Knowledge ◽

Network Model ◽

Explanatory Variable ◽

Thinking Skills ◽

Assessment Method ◽

Assessment Model ◽

Diagnostic Assessment ◽

Bayesian Network Model ◽

Algebra Learning

This study explored a diagnostic assessment method that emphasized the cognitive process of algebra learning. The study utilized a design and a theory-driven model to examine the content knowledge. Using the theory driven model, the thinking skills of algebra learning was also examined. A Bayesian network model was applied to represent the theory model and the quantitative assessment structure. Simulated data was applied to the model to illustrate the purpose. The diagnostic assessment model was represented by a Bayesian network model. Four explanatory variables and nine evidential variables were identified. These were developed to describe the content domain and cognitive structure in an algebra learning process. The diagnostic assessment model both exhibited learning progresses and provided diagnostic feedback. Through students’ performance examples model-based achievement scores were reported at three levels: 1) evidential variable level, 2) explanatory variable lower level, and 3) explanatory variable higher level. This study revealed that the diagnostic assessment model can effectively report learners’ progress in algebra learning in both content knowledge and thinking skills.

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A Hierarchical Bayesian Network Model for Flood Resilience Quantification of Housing Infrastructure Systems

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part A Civil Engineering ◽

10.1061/ajrua6.0001108 ◽

2021 ◽

Vol 7 (1) ◽

pp. 04020060

Author(s):

Mrinal Kanti Sen ◽

Subhrajit Dutta ◽

Jahir Iqbal Laskar

Keyword(s):

Bayesian Network ◽

Network Model ◽

Hierarchical Bayesian ◽

Bayesian Network Model ◽

Infrastructure Systems

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

Journal of Hazardous Materials ◽

10.1016/j.jhazmat.2021.125075 ◽

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

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

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18084150 ◽

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

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

IEEE Access ◽

10.1109/access.2021.3069081 ◽

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

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A Causal Bayesian Network Model for Resolving Complex Wicked Problems

2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) ◽

10.1109/iemtronics52119.2021.9422608 ◽

2021 ◽

Author(s):

Daniel T. Semwayo ◽

Ritesh Ajoodha

Keyword(s):

Bayesian Network ◽

Network Model ◽

Wicked Problems ◽

Bayesian Network Model ◽

Causal Bayesian Network

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Probabilistic Model-Based Fault Diagnosis of the Rotor System

ASME 2007 Power Conference ◽

10.1115/power2007-22072 ◽

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.

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