An explorative object-oriented Bayesian network model for oil spill response in the Arctic Ocean

被引：6

作者：

Mawuli Afenyo

Faisal Khan

Brian Veitch

Adolf K. Y. Ng

Zaman Sajid

Faisal Fahd

机构：

[1] Transport Institute, University of Manitoba, Drake Centre, 181 Freedman Crescent, Winnipeg, R3T 5V4, MB

[2] Centre for Risk, Integrity and Safety Engineering (C-RISE), Faculty of Engineering and Applied Science, Memorial University of Newfoundland, A1B 3X5, St. John’s, NL

[3] Department of Supply Chain Management, Asper School of Business, St. John’s College, University of Manitoba, Winnipeg, R3T 5V4, MB

来源：

Safety in Extreme Environments | 2020年 / 2卷 / 1期

基金：

加拿大自然科学与工程研究理事会;

关键词：

Bayesian network; Decision making; Oil spill; Response techniques;

D O I：

10.1007/s42797-019-00012-7

中图分类号：

学科分类号：

摘要：

This paper proposes an Object-Oriented Bayesian Network model to study oil spill mitigative measures using cost-effectiveness as a decision-making factor. The cost-effectiveness is defined as the ratio of dollar spent to the efficiency of oil removal in the spill area. The proposed model considers the complexity, lack of data, and uncertainties in an oil spill and response modelling scenario for Arctic shipping accident occurrence. The crude oil release, slick formation, weathering and transport, ecological impact, and response scenarios are modelled using Bayesian Network. Application of the model is demonstrated using a hypothetical scenario of an oil spill involving a ship in the Arctic region. The results show that a combination of in-situ burning, mechanical and manual recovery, and use of dispersants would cost the highest, while the use of in-situ burning, and dispersants gives the cheapest option. The study provides a deeper understanding of oil spill dynamics and effectiveness of the response techniques. The proposed model could serve as a useful tool for oil spill response decision-making. © Springer Nature Switzerland AG 2019.

引用

页码：3 / 14

页数：11

共 45 条

[21]

Canada’s Arctic and northern policy., (2019)

[22]

Hassler B., Accidental versus operational oil spills from shipping in the Baltic Sea: risk governance and management strategies, Ambio, 40, pp. 170-178, (2011)

[23]

Helle I., Ahtiainen H., Luoma E., Hanninen M., Kuikka S., A probabilistic approach for a cost-benefit analysis of oil spill management under uncertainty: a Bayesian network model for the Gulf of Finland, J Environ Manag, 158, pp. 122-132, (2015)

[24]

Herath D., Khan F., Yang M., Risk-based winterization to prevent hydrate formation in northern harsh environment, Ocean Eng, 119, pp. 208-216, (2016)

[25]

Hoang A.T., Pham V., Nguyen D., A report of oil spill recovery technologies, Int J Appl Eng Res, 13, pp. 4915-4928, (2018)

[26]

Juntunen T., Rosqvist T., Rytkonen J., Kuikka S., How to model the oil combating technologies and their impacts on ecosystem: A Bayesian networks application in the Baltic Sea, ICES CM., (2005)

[27]

Khakzad N., Khan F., Amyotte P., Quantitative risk analysis of offshore drilling operations: a Bayesian approach, Saf Sci, 57, pp. 108-117, (2013)

[28]

Khan B., Khan F., Veitch B., Yang M., An operational risk analysis tool to analyze marine transportation in Arctic waters, Reliab Eng Syst Saf, 169, pp. 485-502, (2018)

[29]

Lee K., Boufadel M., Chen B., Foght J., Hodson P., Swanson S., Venosa A., The behaviour and environmental impacts of crude oil released into aqueous environments, (2015)

[30]

Lehikoinen A., Hanninen M., Storgard J., Luoma E., Mantyniemi S., Kuikka S., A Bayesian network for assessing the collision induced risk of an oil accident in the Gulf of Finland, Environ Sci Technol, 49, pp. 5301-5309, (2015)

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