Risk Assessment of Underground Subway Stations to Fire Disasters Using Bayesian Network

Wu, Jiansong; Hu, Zhuqiang; Chen, Jinyue; Li, Zheng

doi:10.3390/su10103810

Cited by 37 publications

(29 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…BN models can simply calculate the joint probability distributions. If the probability of the variable X i 's parent node is defined as P a (X i ), the joint probability distribution P a (X i ) is expressed as follows [54]:…”

Section: Bn Modelmentioning

confidence: 99%

Hazard Mapping of the Rainfall–Landslides Disaster Chain Based on GeoDetector and Bayesian Network Models in Shuicheng County, China

Rong

Han

et al. 2020

Water

View full text Add to dashboard Cite

Landslides are among the most frequent natural hazards in the world. Rainfall is an important triggering factor for landslides and is responsible for topples, slides, and debris flows—three of the most important types of landslides. However, several previous relevant research studies covered general landslides and neglected the rainfall–topples–slides–debris flows disaster chain. Since landslide hazard mapping (LHM) is a critical tool for disaster prevention and mitigation, this study aimed to build a GeoDetector and Bayesian network (BN) model framework for LHM in Shuicheng County, China, to address these geohazards. The GeoDetector model will be used to screen factors, eliminate redundant information, and discuss the interaction between elements, while the BN model will be used for constructing a causality disaster chain network to determine the probability and risk level of the three types of landslides. The practicability of the BN model was confirmed by error rate and scoring rules validation. The prediction accuracy results were tested using overall accuracy, Matthews correlation coefficient, relative operating characteristics curve, and seed cell area index. The proposed framework is demonstrated to be sufficiently accurate to construct the complex LHM. In summary, the combination of the GeoDetector and BN model is very promising for spatial prediction of landslides.

show abstract

Section: Bn Modelmentioning

confidence: 99%

Hazard Mapping of the Rainfall–Landslides Disaster Chain Based on GeoDetector and Bayesian Network Models in Shuicheng County, China

Rong

Han

et al. 2020

Water

View full text Add to dashboard Cite

show abstract

“…In terms of urban security, Tang et al established a Bayesian network to analyze the risk of an urban dirty bomb attack [27]. Wu et al established a comprehensive model based on the Bayesian network (BN) and the Delphi method for the rapid and dynamic assessment of the fire evolution process and consequences, in underground subway stations [28]. In terms of natural disasters, Han et al proposed an earthquake disaster chain risk evaluation method that couples the Bayesian network and Newmark model based on natural hazard risk formation theory with the aim of identifying the influence of earthquake disaster chains [29].…”

Section: Introductionmentioning

confidence: 99%

Risk Assessment of Gas Leakage from School Laboratories Based on the Bayesian Network

Zhang

Bai

et al. 2020

IJERPH

Self Cite

View full text Add to dashboard Cite

In recent years, concerns about the safety of laboratories have been caused by several serious accidents in school laboratories. Gas leaks in the laboratory are often difficult to detect and cause serious consequences. In this study, a comprehensive model based on the Bayesian network is established for the assessment of the gas leaks evolution process and consequences in school laboratories. The model can quantitatively evaluate the factors affecting the probability and consequences of gas leakage. The results show that a model is an effective tool for assessing the risk of gas leakage. Among the various factors, the unsafe behavior of personnel has the greatest impact on the probability of gas leakage, and the concentration of toxic and harmful gases is the main factor affecting the consequences of accidents. Since the probability distribution of each node is obtained based on the experience of experts, there is a deviation in the quantitative calculation of the probability of gas leakage and consequences, but does not affect the risk analysis. This study could quantitatively assess the probability and consequences of gas leakage in the laboratory, and identify vulnerabilities, which helps improve the safety management level of gas in the school laboratory and reducing the possibility of gas leakage posing a threat to personal safety.

show abstract

“…For the minimization of damages from fire in the subways, the definition and methodology of the reactive processes against fire in the subways were verified to identify resource conflict detection in minimizing time required for reaction and to prioritize pertinent processes. Wu et al [15] applied the comprehensive model based on the Bayesian network (BN) and the Delphi method to raise the accuracy of the fire extinguishing process and results against fires in subways, and quantified the factors impacting consequences of fires, of causes of fires, and of preventive measures against fires to propose methods for emergency decision-making. Ying et al [16] studied the need for emergency response training for fires in subways.…”

Section: Introductionmentioning

confidence: 99%

Sustainable Urban Planning Technique of Fire Disaster Prevention for Subway

Rie

Ryu

2020

Sustainability

View full text Add to dashboard Cite

Subway infrastructure is a representative urban infrastructure for sustainable urban development as part of its policy to harmonize with economic growth. As the transportation infrastructure of large cities develops with high speed and intelligence, more attention will be paid to its safety. The main cause of death in subway fires is asphyxiation, due to the closed specificity of the underground space. Therefore, smoke exhaust facilities should be capable of minimizing the effects of smoke to ensure the safe evacuation of passengers in the event of fire. In this study, three kinds of fire locations are adopted to analyze the distribution of platform temperature, CO, and visibility in connection with the smoke exhaust system operation method. We evaluate the performance of the applicable smoke exhaust system from ASET-based untenable area analysis. Fatality evaluation by escape analysis according to the smoke exhaust system estimates the fatality based on the tenability performance evaluation. Moreover, the FED method was used to evaluate tenability performance. Therefore, the result of this study suggests a solution for sustainable subway Disaster response from the performance evaluation of the subway platform smoke exhaust system for safe evacuation of passengers, which is essential for subway construction or remodeling.

show abstract

Risk Assessment of Underground Subway Stations to Fire Disasters Using Bayesian Network

Cited by 37 publications

References 34 publications

Hazard Mapping of the Rainfall–Landslides Disaster Chain Based on GeoDetector and Bayesian Network Models in Shuicheng County, China

Hazard Mapping of the Rainfall–Landslides Disaster Chain Based on GeoDetector and Bayesian Network Models in Shuicheng County, China

Risk Assessment of Gas Leakage from School Laboratories Based on the Bayesian Network

Sustainable Urban Planning Technique of Fire Disaster Prevention for Subway

Contact Info

Product

Resources

About