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Risk assessment for berthing of hazardous cargo vessels using Bayesian networks
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“RBN has been selected to build up the risk model in this paper due to its advantages such as modelling uncertain and complex domains (Uusitalo, 2007;Khan et al., 2021;Chen et al., 2022).”
From A BN driven FMEA approach to assess maritime cybersecurity risks (2023) · cites this paper as Khan et al., 2021
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Statistics and causal inference: A review
Pearl, J. (2003) · Test · doi:10.1007/bf02595718 · Cited by 144
Flagged first, then by citations
Research works printed but not linked: 35
- Abimbola, 2015, Safety and risk analysis of managed pressure drilling operation using Bayesian network, Saf. Sci., 76, 133, 10.1016/j.ssci.2015.01.010 · DOI
- Afenyo, 2017, Arctic shipping accident scenario analysis using Bayesian Network approach, Ocean Eng., 133, 224, 10.1016/j.oceaneng.2017.02.002 · DOI
- Ahmed, 2013, Automatic ship berthing using artificial neural network trained by consistent teaching data using nonlinear programming method, Eng. Appl. Artif. Intell., 26, 2287, 10.1016/j.engappai.2013.08.009 · DOI
- Akyuz, 2015, A methodological extension to human reliability analysis for cargo tank cleaning operation on board chemical tanker ships, Saf. Sci., 75, 146, 10.1016/j.ssci.2015.02.008 · DOI
- Akyuz, 2016, A hybrid human error probability determination approach: the case of cargo loading operation in oil/chemical tanker ship, J. Loss Prev. Process. Ind., 43, 424, 10.1016/j.jlp.2016.06.020 · DOI
- Baalisampang, 2018, Review and analysis of fire and explosion accidents in maritime transportation, Ocean Eng., 158, 350, 10.1016/j.oceaneng.2018.04.022 · DOI
- Bubbico, 2009, Preliminary risk analysis for LNG tankers approaching a maritime terminal, J. Loss Prev. Process. Ind., 22, 634, 10.1016/j.jlp.2009.02.007 · DOI
- Budipriyanto, 2017, A simulation study of collaborative approach to berth allocation problem under uncertainty, The Asian J. Shipp. Logist., 33, 127, 10.1016/j.ajsl.2017.09.003 · DOI
- Cunha, 2015, Review on hazardous and noxious substances (HNS) involved in marine spill incidents—an online database, J. Hazard Mater., 285, 509, 10.1016/j.jhazmat.2014.11.005 · DOI
- Eleye-Datubo, 2006, Enabling a powerful marine and offshore decision-support solution through Bayesian network technique, Risk Anal., 26, 695, 10.1111/j.1539-6924.2006.00775.x · DOI
- Ellis, 2011, Analysis of accidents and incidents occurring during transport of packaged dangerous goods by sea, Saf. Sci., 49, 1231, 10.1016/j.ssci.2011.04.004 · DOI
- Gruenefeld, 2018, vol. 12
- Huang, 2015, Facts related to August 12, 2015 explosion accident in Tianjin, China, Process Saf. Prog., 34, 313, 10.1002/prs.11789 · DOI
- Inanloo, 2015, Explosion impacts during transport of hazardous cargo: GIS-based characterization of overpressure impacts and delineation of flammable zones for ammonia, J. Environ. Manag., 156, 1, 10.1016/j.jenvman.2015.02.044 · DOI
- John, 2014, An integrated fuzzy risk assessment for seaport operations, Saf. Sci., 68, 180, 10.1016/j.ssci.2014.04.001 · DOI
- Karthikeyan, 2019, vol. 21, 6
- Khakzad, 2011, Safety analysis in process facilities: comparison of fault tree and Bayesian network approaches, Reliab. Eng. Syst. Saf., 96, 925, 10.1016/j.ress.2011.03.012 · DOI
- Khakzad, 2013, Quantitative risk analysis of offshore drilling operations: a Bayesian approach, Saf. Sci., 57, 108, 10.1016/j.ssci.2013.01.022 · DOI
- Khan, 2020, Risk assessment and decision support for sustainable traffic safety in Hong Kong waters, IEEE Access, 8, 72893, 10.1109/ACCESS.2020.2988201 · DOI
- Kuzu, 2017, Analytic comparison of different mooring systems, Global Perspect.: Towards Sustain. Green Integr. Maritime Transport, 265
- Li, 2014, Bayesian network with quantitative input for maritime risk analysis, Transportmetrica A: Transport Sci., 10, 89, 10.1080/18128602.2012.675527 · DOI
- Li, 2014, Ship safety index, Transport. Res. Pol. Pract., 66, 75, 10.1016/j.tra.2014.04.016 · DOI
- Neuparth, 2011, Hazardous and Noxious Substances (HNS) in the marine environment: prioritizing HNS that pose major risk in a European context, Mar. Pollut. Bull., 62, 21, 10.1016/j.marpolbul.2010.09.016 · DOI
- Ren, 2008, A methodology to model causal relationships on offshore safety assessment focusing on human and organizational factors, J. Saf. Res., 39, 87, 10.1016/j.jsr.2007.09.009 · DOI · Paper page
- Ren, 2009, An offshore risk analysis method using fuzzy Bayesian network, J. Offshore Mech. Arctic Eng., 131, 41101, 10.1115/1.3124123 · DOI
- Sciberras, 2018, The UN's 2030 Agenda for sustainable development and the maritime transport domain: the role and challenges of IMO and its stakeholders through a grounded theory perspective, WMU J. Maritime Affairs, 17, 435, 10.1007/s13437-018-0147-2 · DOI
- Trbojevic, 2000, Risk based methodology for safety improvements in ports, J. Hazard Mater., 71, 467, 10.1016/S0304-3894(99)00094-1 · DOI
- Vidmar, 2018, Safety assessment of crude oil tankers, Saf. Sci., 105, 178, 10.1016/j.ssci.2018.02.009 · DOI
- Wang, 2020, The multi-state maritime transportation system risk assessment and safety analysis, Sustainability, 12
- Wang, 2016, Bayesian network-based risk assessment for hazmat transportation on the middle route of the south-to-north water transfer project in China, Stoch. Environ. Res. Risk Assess., 30, 841, 10.1007/s00477-015-1113-6 · DOI
- Xie, 2010, Estimation and application of a Bayesian network model for discrete travel choice analysis, Transport. Lett., 2, 125, 10.3328/TL.2010.02.02.125-144 · DOI
- Yang, 2010, A survey on hazardous materials accidents during road transport in China from 2000 to 2008, J. Hazard Mater., 184, 647, 10.1016/j.jhazmat.2010.08.085 · DOI
- Yang, 2018, Realising advanced risk-based port state control inspection using data-driven Bayesian networks, Transport. Res. Pol. Pract., 110, 38, 10.1016/j.tra.2018.01.033 · DOI
- Yeo, 2016, Dynamic risk analysis of offloading process in floating liquefied natural gas (FLNG) platform using Bayesian Network, J. Loss Prev. Process. Ind., 41, 259, 10.1016/j.jlp.2016.04.002 · DOI
- Zhao, 2012, Analysis of factors that influence hazardous material transportation accidents based on Bayesian networks: a case study in China, Saf. Sci., 50, 1049, 10.1016/j.ssci.2011.12.003 · DOI
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A BN driven FMEA approach to assess maritime cybersecurity risks
Park, C., Kontovas, C., Yang, Z., & Chang, C.-H. (2023) · Ocean & Coastal Management · doi:10.1016/j.ocecoaman.2023.106480 · Cited by 98
Metadata
- Authors
- Khan, R. U., Yin, J., Mustafa, F. S., & Anning, N.
- Affiliations
- Shanghai Jiao Tong University (CN)
- Corresponding author
- Yin, Jingbo
- Venue
- Ocean & Coastal Management
- Publisher
- Elsevier BV
- Published
- 2021-09
- Volume
- 210
- Pages
- 105673
- ISSN
- 0964-5691 (ISSN-L)
- OpenAlex
- W3171355605
- Cited by
- 88
- References
- 1
- Citations by year
- 2026: 10 · 2025: 19 · 2024: 23 · 2023: 18 · 2022: 16 · 2021: 2
- FWCI
- 10.50
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Ocean & Coastal Management (publishedVersion)
- Topics
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Risk and Safety Analysis
- Physical Sciences › Engineering › Ocean Engineering › Maritime Navigation and Safety
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Maritime Ports and Logistics
- Keywords
- ship berthing, risk assessment, Bayesian networks, binary logistic regression, expert opinion, maritime accidents, environmental pollution, ship crew training, wind force, water velocity, channel width, port location, risk management
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