Bayesian network model of maritime safety management

Hänninen, Maria; Banda, Osiris A. Valdez; Kujala, Pentti

doi:10.1016/j.eswa.2014.06.029

Cited by 129 publications

(68 citation statements)

References 26 publications

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“…When the international safety management (ISM) code was introduced, the effectiveness of it was also conducted both in UK (Bhattacharya et al, 2012) and in Greece (Tzannatos and Kokotos 2009). Similar work for analyzing the effectiveness of safety management was also carried out in the gulf of Finland by using Bayesian network (Hänninen et al, 2014) and also on-board ships (Akyuz and Celik 2014).…”

Section: Effectiveness Of Emergency Managementmentioning

confidence: 99%

Emergency Management of Maritime Accidents in the Yangtze River: Problems, Practice and Prospects

Yan

Zhang

et al. 2017

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Maritime accidents have received considerable attentions due to the enormous property damage, casualties and serious environmental pollution. This paper first makes statistical analysis of the different types of maritime accidents in the period of 2012 to 2014 in the Yangtze River. Second, the problems of emergency management of maritime accidents are also proposed from the analysis of the major accident "Eastern Star".. Afterwards, four practice cases, including decision support for maritime accidents, emergency resource allocation, emergency simulation system and effectiveness of emergency management, are introduced to present the insights gained from these practices. Last, in order to address these problems, this paper proposes that an artificial societies, Computational experiments, and Parallel execution (ACP) approach should be introduced to establish an improved management system for maritime accidents in the future, and an ACP based maritime accident emergency management framework is proposed.

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Section: Effectiveness Of Emergency Managementmentioning

confidence: 99%

Emergency Management of Maritime Accidents in the Yangtze River: Problems, Practice and Prospects

Yan

Zhang

et al. 2017

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“…States are numbers of possible conditions that each risk factor may assume (Wang et al, 2011). Often these states are binary, mainly because the greater the number of states, greater the model complexity, as there is a need to specify each variable probability distribution for its n states (Hänninen et al, 2014). If there are latent variables that influence the risk, they cannot be measured directly; the structural equation modeling (SEM) is indicated to measure them indirectly (Chatterjee, 2014).…”

Section: Ordination Of Data (Od)mentioning

confidence: 99%

“…Allowing the understanding of how a harmful event is spread and recognize the trigger event (an event that starts the chain of harmful events), to identify the hypotheses and assumptions governing the relationship between occupational risk variables (García-Herrero et al, 2012a;Hänninen et al, 2014;Wang et al, 2011). The assumptions externalize the previous knowledge of the studied domain, and can be formulated from previous studies, knowledge of experts or through the use of predictive probability distributions (Hamra et al, 2014).…”

Section: Identification Of the Model's Purpose (Imp)mentioning

confidence: 99%

Modeling Bayesian Networks from a conceptual framework for occupational risk analysis

Vieira

Silva

2017

Prod.

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“…One problem that may rise when beliefs without solid foundation become commonly accepted is that the models developed for analysing the phenomenon and assessing the frequency of the accident may not represent the reality, Sormunen et al, 2014;Hänninen et al, 2014a). Moreover if the model is used for risk management and decision making it might …”

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confidence: 99%

Assessing Grounding Frequency using Ship Traffic and Waterway Complexity

et al. 2014

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Ship traffic is the factor that presents in almost all of the existing grounding risk models. It is considered to be one of the main factors affecting the expected frequency of ship groundings. This is mostly accepted by experts as common sense. However, there is no research available on the actual dependency between ship traffic and grounding accidents. In this paper, we conduct a study aimed at determining the statistical dependency between the density and distribution of traffic, the number and frequency of grounding accidents and the dependency between the complexity of waterways and an actual accident. For this purpose we utilise statistical analysis of maritime traffic, obtained from Automatic Identification System (AIS) data and grounding accidents, enhanced with the expert elicitation techniques delivering the waterway complexity index. The sea area under investigation is the Gulf of Finland. The results show statistical dependency between frequency of grounding and waterway complexity as well as the traffic distribution. However, the study does not reveal any significant dependency between grounding and traffic density. K E Y

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Bayesian network model of maritime safety management

Cited by 129 publications

References 26 publications

Emergency Management of Maritime Accidents in the Yangtze River: Problems, Practice and Prospects

Emergency Management of Maritime Accidents in the Yangtze River: Problems, Practice and Prospects

Modeling Bayesian Networks from a conceptual framework for occupational risk analysis

Assessing Grounding Frequency using Ship Traffic and Waterway Complexity

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