Decision-making on process risk of Arctic route for LNG carrier via dynamic Bayesian network modeling

Li, Zhuang; Hu, Shenping; Gao, Guoping; Yao, Chenyang; Fu, Shanshan; Xi, Yongtao

doi:10.1016/j.jlp.2021.104473

Cited by 37 publications

(14 citation statements)

References 41 publications

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“…Dabrowski and Villiers [45] constructed a DBN model of maritime piracy situation based on a switching linear dynamic system analysis, then simulated the spatiotemporal evolution of various vessels behavior of maritime piracy. Li et al [46] constructed the MC based on observation data to obtain the state transition probabilities of the dynamic factors in a DBN, to realize the reasoning of the temporal risk evolution of a ship navigation process in the Arctic waters. Khan et al [26] constructed a DBN model of ship-ice collision risk by considering the influence of objective factors such as visibility, ice condition and ship speed on an Arctic route.…”

Section: Maritime Risk Analysis Using Bn and Dbnmentioning

confidence: 99%

Risk evolution analysis of ship pilotage operation by an integrated model of FRAM and DBN

Guo

Jin

et al. 2023

Reliability Engineering & System Safety

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Section: Maritime Risk Analysis Using Bn and Dbnmentioning

confidence: 99%

Risk evolution analysis of ship pilotage operation by an integrated model of FRAM and DBN

Guo

Jin

et al. 2023

Reliability Engineering & System Safety

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“…Consequently, sensitivity analysis is conducted to rank the individual variables with respect to their contributions to the overall system (as the output) variability and then identify the key factor path that affects the whole system. The details of the model validation method for a DBN can be found in [44].…”

Section: Step 1: Identification Of Unsafe Actsmentioning

confidence: 99%

“…The BN can be incorporated with systematic approaches for processing the evolution, dynamic variation, and complex dependencies of maritime accidents, such as Accident Map (AcciMap) [34], System Theoretic Process Analysis (STPA) [40,41], and Functional Resonance Analysis Method (FRAM) [28,42]. Moreover, a Dynamic Bayesian Network (DBN) is an improved technique for modeling a time series or dynamic process by expanding BNs [43][44][45]. Combined with the advantage of BN, the DBN can (1) combine graph theory with probability theory to form the topological structure of causal links and achieve a graphical and intuitive description of the breeding, germination, and development of maritime accidents [43]; (2) obtain a posteriori probability within a context of information uncertainty by updating the prior probability after obtaining some data information in the case of limited accident sample data and asymmetric information, thus realizing the identification of critical causes of maritime accidents; (3) learn the data parameters to fit the network structure that most conforms to the data logic, and realize the deduction and decision-making of the accident process [44] under the condition of having a large number of historical accident data samples.…”

Section: Introductionmentioning

confidence: 99%

Framework for Process Analysis of Maritime Accidents Caused by the Unsafe Acts of Seafarers: A Case Study of Ship Collision

Wang

2022

JMSE

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Accurately describing and evaluating the effects of unsafe acts on maritime accidents is critical to establishing practical accident prevention and control options. This paper proposes a framework for the probabilistic analysis of maritime accidents caused by seafarers’ unsafe acts by incorporating a navigation simulation and dynamic Bayesian network (DBN) modeling. First, the unsafe acts of seafarers are identified according to an in-depth analysis of global maritime investigation reports. Then, a navigation simulation experiment is designed to collect the ship-handling data of seafarers during hazardous accident scenarios. Consequently, a dynamic probabilistic model is proposed using a DBN to describe the phases of maritime accidents based on the navigation simulation experiment data. Furthermore, an evolution analysis of maritime accidents is conducted to explore the causal chain of such accidents through sensitivity analysis. The typical navigational accident-collision is chosen as the case to interpret the proposed framework, considering the formation process of ship collision risks, from the occurrence of ship collision risk (phase 1) to the close-quarters situation (phase 2) and to immediate danger (phase 3). This framework is applied to explore the causal chain of collision accidents caused by the unsafe acts of seafarers.

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“…To study real-time risks more accurately, Bi et al [19] used dynamic irregular grids to analyze and evaluate navigation safety. Li et al [20,21] and Guo et al [22] proposed using DBN to study risk evolution. Therefore, the introduction of the DBN method not only solved the uncertainty measurement problem based on risk information but also facilitated the analysis of risk characteristics in the spatial and temporal dimensions.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Pirate Attack Process Risk along the Maritime Silk Road via Dynamic Bayesian Network Analysis

Xia

Xuan

et al. 2023

JMSE

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The Maritime Silk Road (MSR) is an important channel for maritime trade between China and other countries in the world. Maritime piracy has brought huge security risks to ships’ navigation and has seriously threatened the lives and property of crew members. To reduce the likelihood of attacks from pirates, it is necessary to study the risk to a ship exposed to attacks from pirates on the MSR. Firstly, risk factors were established from three risk component categories (hazard, mitigation capacity, and vulnerability and exposure) and the risk index system of piracy and armed robbery events was founded. Secondly, the dynamic Bayesian network (DBN) method was introduced to establish a pirate attack risk assessment model ad to conduct a quantitative analysis of the process risk of a ship being attacked by pirates. Finally, combined with the scene data of the MSR, the process risk of a ship being attacked by pirates was modeled and applied as an example. The results showed that the overall risk of a ship being attacked by pirates is the lowest in July and the highest in March. In the whole route, when the ship was in the Gulf of Guinea, the Gulf of Aden–Arabian Sea, and the Strait of Malacca, the risk of pirate attack was the highest. This dynamic network model can effectively analyze the level of risk of pirate attacks on ships, providing a reference for the safety decision-making of ships on ocean routes.

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Decision-making on process risk of Arctic route for LNG carrier via dynamic Bayesian network modeling

Cited by 37 publications

References 41 publications

Risk evolution analysis of ship pilotage operation by an integrated model of FRAM and DBN

Risk evolution analysis of ship pilotage operation by an integrated model of FRAM and DBN

Framework for Process Analysis of Maritime Accidents Caused by the Unsafe Acts of Seafarers: A Case Study of Ship Collision

Exploring the Pirate Attack Process Risk along the Maritime Silk Road via Dynamic Bayesian Network Analysis

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