Risk Reasoning from Factor Correlation of Maritime Traffic under Arctic Sea Ice Status Association with a Bayesian Belief Network

Abstract

Sustainable growth should not only be beneficial to the shipping industry in the future, but is also an urgent need to respond to resource and environmental crises and strengthen shipping governance. Maritime traffic in Arctic waters is prone to encounter dangerous ice conditions, and it is essential to study the mechanism of ice collision risk formation in relation to ice conditions. Taking the ship-ice collision risk in Arctic waters as the research object, we propose a dynamic assessment model of ship-ice collision risk under sea ice status dynamic association (SDA) effect. By constructing the standard paradigm of risk factor dynamic association (DA) effect, taking SDA as the key association factor. Combing with other risk factors that affect ship-ice collision accidents, the coupling relationship between risk factors were analyzed. Then, using the Bayesian network method to build a ship-ice collision accident dynamic risk assessment model and combing with the ice monitoring data in summer Arctic waters, we screen five ships’ position information on the trans-Arctic route in August. The risk behavior of ship-ice collision accidents on the selected route under SDA is analyzed by model simulation. The research reveal that the degree of SDA is a key related factor for the serious ice condition and the possibility of human error during ship’s navigation, which significantly affects the ship-ice collision risk. The traffic in Arctic waters requires extra vigilance of the SDA effect from no ice threat to ice threat, and continuous ice threat. According to the ship-ice collision risk analysis under the SDA effect and without SDA effect, the difference in risk reasoning results on the five stations of the selected route are 32.69%, −32.33%, −27.64%, −10.26%, and −30.13% respectively. The DA effect can optimize ship-ice collision risk inference problem in Arctic waters.

Risk Assessment of Sudden Water Pollution Accidents Associated with Dangerous Goods Transportation on the Cross-Tributary Bridges of Baiyangdian Lake

“…The weather risk, road risk, time risk, and lighting risk belonged to unpredictable force majeure factors; thereby, it was necessary to consider these accidental factors in transportation distance and time arrangement, so as to reduce the possibility of sudden water pollution accidents [73]. Unforeseeable force majeure elements encompassed the hazards of weather, road conditions, time, and lighting.…”

Hybrid Causal Logic Model for Estimating the Probability of an Icebreaker–Ship Collision in an Ice Channel During an Escort Operation Along the Northeast Passage

“…This model only estimates the static probability of icebreaker-ship collision for the entire voyage. With the growing interests in the dynamic risk analysis in the Arctic (Khan et al, 2020;Li et al, 2020Li et al, , 2021aLi et al, 2021a,b), this model needs some modification to suit the requirements of this mission (e.g., integrating the FT with a binary decision approach, as proposed by Jiang et al (2021), or. transferring both the FT and ESD into a BN, as suggested by Khakzad et al (2013)).…”

A Port Entry Risk Assessment Model Based on Bayesian Networks and Elements of the e-Navigation Concept

“…In this work, through the elements of the e-Navigation system that have been previously tested through various projects, it is attempted to achieve all those elements that affect the safety of navigation are displayed through a unique model that will make it easier for the Master to make a quality decision about entering the port. Many authors dealt with the safety of the ship navi-gation from point A to point B in different areas and conditions, as well as risk analysis [11][12][13][14][15]. A study [11] investigated risk assessment of maritime accidents on passenger's vessels in non-linear coastal navigation.…”

“…A study [11] investigated risk assessment of maritime accidents on passenger's vessels in non-linear coastal navigation. In a paper [12] authors investigated the risk factor correlation of maritime traffic under Arctic sea ice status association with a Bayesian Belief Network. According to research [13], the authors propose a data-driven Bayesian network for the construction of a risk analysis model to quantify the impact of different types of maritime accidents.…”