Implementing Bayesian networks for ISO 31000:2018-based maritime oil spill risk management: State-of-art, implementation benefits and challenges, and future research directions

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Parviainen , T , Goerlandt , F , Helle , I , Haapasaari , P & Kuikka , S 2021 , ' Implementing Bayesian networks for ISO 31000:2018-based maritime oil spill risk management: State-of-art, implementation benefits and challenges, and future research directions ' , Journal of Environmental Management , vol. 278 , 111520 . https://doi.org/10.1016/j.jenvman.2020.111520

Title: Implementing Bayesian networks for ISO 31000:2018-based maritime oil spill risk management: State-of-art, implementation benefits and challenges, and future research directions
Author: Parviainen, Tuuli; Goerlandt, Floris; Helle, Inari; Haapasaari, Päivi; Kuikka, Sakari
Contributor: University of Helsinki, Helsinki Institute of Sustainability Science (HELSUS)
University of Helsinki, Environmental and Ecological Statistics Group
University of Helsinki, Environmental Sciences
University of Helsinki, Ecosystems and Environment Research Programme
Date: 2021-01-15
Language: eng
Number of pages: 13
Belongs to series: Journal of Environmental Management
ISSN: 0301-4797
URI: http://hdl.handle.net/10138/321809
Abstract: The risk of a large-scale oil spill remains significant in marine environments as international maritime transport continues to grow. The environmental as well as the socio-economic impacts of a large-scale oil spill could be substantial. Oil spill models and modeling tools for Pollution Preparedness and Response (PPR) can support effective risk management. However, there is a lack of integrated approaches that consider oil spill risks comprehensively, learn from all information sources, and treat the system uncertainties in an explicit manner. Recently, the use of the international ISO 31000:2018 risk management framework has been suggested as a suitable basis for supporting oil spill PPR risk management. Bayesian networks (BNs) are graphical models that express uncertainty in a probabilistic form and can thus support decision-making processes when risks are complex and data are scarce. While BNs have increasingly been used for oil spill risk assessment (OSRA) for PPR, no link between the BNs literature and the ISO 31000:2018 framework has previously been made. This study explores how Bayesian risk models can be aligned with the ISO 31000:2018 framework by offering a flexible approach to integrate various sources of probabilistic knowledge. In order to gain insight in the current utilization of BNs for oil spill risk assessment and management (OSRA-BNs) for maritime oil spill preparedness and response, a literature review was performed. The review focused on articles presenting BN models that analyze the occurrence of oil spills, consequence mitigation in terms of offshore and shoreline oil spill response, and impacts of spills on the variables of interest. Based on the results, the study discusses the benefits of applying BNs to the ISO 31000:2018 framework as well as the challenges and further research needs.
Subject: 1181 Ecology, evolutionary biology
Oil spills
Pollution preparedness and response
Bayesian networks
Uncertainty
Risk management
ISO 31000:2018
QUANTITATIVE-ANALYSIS
PROBABILISTIC MODEL
UNCERTAINTY
GULF
IMPACT
SAFETY
RECOVERY
FRAMEWORK
INFERENCE
ACCIDENT
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