A Mutual Information-Based Bayesian Network Model for Consequence Estimation of Navigational Accidents in the Yangtze River

Wu, Bing; Yip, Tsz Leung; Yan, Xinping; Mao, Zhe

doi:10.1017/s037346331900081x

Cited by 21 publications

(14 citation statements)

References 36 publications

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“…In binary LR, the binary dependent variable comprises two categories which are generally represented as 0 and 1. On the other hand, more than two categories of dependent variables are handled in multinomial LR (Sapri et al, 2017; Wu et al, 2020). In this study, two levels of accident severity (the dependent variable) were taken into consideration: serious or non-serious.…”

Section: Methodsmentioning

confidence: 99%

Decision Tree and Logistic Regression Analysis to Explore Factors Contributing to Harbour Tugboat Accidents

2020

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As tugboats interact very closely with ships in restricted waters, the possibility of accidents increases in these operations. Despite the high accident possibility, there is a gap in studies on tugboat accidents. This study aims to analyse accidents involving tugboats using data mining. For this purpose, a tugboat accidents dataset consisting of a total of 496 accident records for the period from 2008 to 2019 was collected. Logistic regression and decision tree algorithms were implemented to the dataset. The results revealed that tugboat propulsion type is the most important and influential factor in the severity of tugboat accidents. The inferences drawn from these results could be beneficial for tugboat operators and port authorities in enhancing their awareness of the factors affecting tugboat accidents. In addition, the outputs of this study can be a reference for management units in developing strategies for preventing tugboat accidents and can also be used in effective planning for practicable prevention programmes and practices.

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Section: Methodsmentioning

confidence: 99%

Decision Tree and Logistic Regression Analysis to Explore Factors Contributing to Harbour Tugboat Accidents

2020

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“…Also, they showed that mutual information outperforms Wrapper methods in variable selection when the number of variables is large. The merit of mutual information is to avoid unnecessary work and reduce the need for expert judgment, provide a straightforward, fast, with relatively low computational complexity, and costeffective approach to recognize the influencing macroeconomic variables on port throughput (Wu et al, 2020;Yang et al, 2018). Mutual information also offers a useful visual tool for a better understanding of the dependencies among variables (Li et al, 2009).…”

Section: Mutual Information Analysismentioning

confidence: 99%

“…Qualitative (or expert-judgment-based) evaluation of influencing factors on port throughput may be time consuming, laborious, include biases (Wu et al, 2020) and rely on incomplete and subjective knowledge, which is conditional on the background and experience of experts (Hänninen, 2014). Furthermore, experts are not always available to determine the influencing variables (Montewka et al, 2014).…”

Section: Identification Of the Relation Between Port Throughput And Mmentioning

confidence: 99%

“…Mutual information has been used in previous maritime research, albeit not in the context of port throughput. For instance, Wu et al (2020) used mutual information to reduce needs for expert judgment in the identification of input variables for the Bayesian network for consequence estimation of navigational accidents. Yang et al (2018) applied mutual information to facilitate the recognition of insignificant variables that should be excluded from a Bayesian network.…”

Section: Introductionmentioning

confidence: 99%

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Mutual information analysis of the factors influencing port throughput

Eskafi

Kowsari

Dastgheib

et al. 2020

MABR

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Purpose Port throughput analysis is a challenging task, as it consists of intertwined interactions between a variety of cargos and numerous influencing factors. This study aims to propose a quantitative method to facilitate port throughput analysis by identification of important cargos and key macroeconomic variables. Design/methodology/approach Mutual information is applied to measure the linear and nonlinear correlation among variables. The method gives a unique measure of dependence between two variables by quantifying the amount of information held in one variable through another variable. Findings This study uses the mutual information to the Port of Isafjordur in Iceland to underpin the port throughput analysis. The results show that marine products are the main export cargo, whereas most imports are fuel oil, industrial materials and marine product. The aggregation of these cargos, handled in the port, meaningfully determines the non-containerized port throughput. The relation between non-containerized export and the national gross domestic product (GDP) is relatively high. However, non-containerized import is mostly related to the world GDP. The non-containerized throughput shows a strong relation to the national GDP. Furthermore, the results reveal that the volume of national export trade is the key influencing macroeconomic variable to the containerized throughput. Originality/value Application of the mutual information in port throughput analysis effectively reduces epistemic uncertainty in the identification of important cargos and key influencing macroeconomic variables. Thus, it increases the reliability of the port throughput forecast.

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“…The Bayesian network (BN) is a graphical network of probabilistic reasoning, and it can analyze the uncertain relationship between variables in a complex network [ 17 ]. This method is widely used in risk analysis [ 18 , 19 , 20 , 21 ], risk assessment [ 22 , 23 , 24 , 25 ] and decision-making [ 26 , 27 ]. Zhu et al constructed a BN model of chemical terrorist attacks to conduct risk analysis, which provided theoretical support for the security prevention work of the risk management department [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Network-Based Risk Analysis of Chemical Plant Explosion Accidents

Wang

Liu

2020

IJERPH

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The chemical industry has made great contributions to the national economy, but frequent chemical plant explosion accidents (CPEAs) have also caused heavy property losses and casualties, as the CPEA is the result of interaction of many related risk factors, leading to uncertainty in the evolution of the accident. To systematically excavate and analyze the underlying causes of accidents, this paper first integrates emergency elements in the frame of orbit intersection theory and proposes 14 nodes to represent the evolution path of the accident. Then, combined with historical data and expert experience, a Bayesian network (BN) model of CPEAs was established. Through scenario analysis and sensitivity analysis, the interaction between factors and the impact of the factors on accident consequences was evaluated. It is found that the direct factors have the most obvious influence on the accident consequences, and the unsafe conditions contribute more than the unsafe behaviors. Furthermore, considering the factor chain, the management factors, especially safety education and training, are the key link of the accident that affects unsafe behaviors and unsafe conditions. Moreover, effective government emergency response has played a more prominent role in controlling environmental pollution. In addition, the complex network relationship between elements is presented in a sensitivity index matrix, and we extracted three important risk transmission paths from it. The research provides support for enterprises to formulate comprehensive safety production management strategies and control key factors in the risk transmission path to reduce CPEA risks.

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A Mutual Information-Based Bayesian Network Model for Consequence Estimation of Navigational Accidents in the Yangtze River

Cited by 21 publications

References 36 publications

Decision Tree and Logistic Regression Analysis to Explore Factors Contributing to Harbour Tugboat Accidents

Decision Tree and Logistic Regression Analysis to Explore Factors Contributing to Harbour Tugboat Accidents

Mutual information analysis of the factors influencing port throughput

Bayesian Network-Based Risk Analysis of Chemical Plant Explosion Accidents

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