scholarly journals A Bayesian Network risk model for assessing oil spill recovery effectiveness in the ice-covered Northern Baltic Sea

2019 ◽  
Vol 139 ◽  
pp. 440-458 ◽  
Author(s):  
Liangliang Lu ◽  
Floris Goerlandt ◽  
Osiris A. Valdez Banda ◽  
Pentti Kujala ◽  
Anders Höglund ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Baltic Sea ◽  
Oil Spill ◽  
Risk Model ◽  
Network Risk

scholarly journals Fire and explosion analysis of filling station based on fuzzy mathematics and Bayesian network model

2021 ◽  
Vol 261 ◽  
pp. 03055
Author(s):  
Kezhen Chen ◽  
Jihong Ye ◽  
Xiaofeng Zhang ◽  
Qingqing Lv
Keyword(s):  
Bayesian Network ◽  
Risk Model ◽  
Fault Tree ◽  
Fuzzy Mathematics ◽  
Compressed Natural Gas ◽  
Filling Station ◽  
Fire And Explosion ◽  
Accurate Probability ◽  
Explosion Accidents ◽  
Network Risk

In order to explore the basic events and risk occurrence probability of fire and explosion accidents in CNG (Compressed Natural Gas) filling station, a corresponding Bayesian network risk model was established based on the fault tree of filling station. The prior probability was modified by introducing fuzzy mathematics in the process of transforming the fault tree into Bayesian network, and the posterior probability of the basic events of CNG filling station fire and explosion accidents was analyzed and calculated by GeNIe software. Finally, through case analysis, it is found out that the most dangerous factors that lead to the greatest risk of fire and explosion accidents in a filling station are: personnel misoperation, management defects, etc. After verifying the model, it shows that paying attention to the polymorphism of the base events and determining the rationality of the logical relationship between the base events can calculate the more accurate probability distribution of the base events, and at the same time provide reasonable suggestions for the accident prevention of the gas filling station.

scholarly journals Next-Generation Smart Response Web (NG-SRW): An Operational Spatial Decision Support System for Maritime Oil Spill Emergency Response in the Gulf of Finland (Baltic Sea)

2021 ◽  
Vol 13 (12) ◽  
pp. 6585
Author(s):  
Mihhail Fetissov ◽  
Robert Aps ◽  
Floris Goerlandt ◽  
Holger Jänes ◽  
Jonne Kotta ◽  
...  
Keyword(s):  
Baltic Sea ◽  
Oil Spill ◽  
Situational Awareness ◽  
Oil Spills ◽  
Relevant Information ◽  
Response Strategy ◽  
Spatial Decision Support System ◽  
Next Generation ◽  
Oil Spill Response ◽  
The Baltic

The Baltic Sea is a unique and sensitive brackish-water ecosystem vulnerable to damage from shipping activities. Despite high levels of maritime safety in the area, there is a continued risk of oil spills and associated harmful environmental impacts. Achieving common situational awareness between oil spill response decision makers and other actors, such as merchant vessel and Vessel Traffic Service center operators, is an important step to minimizing detrimental effects. This paper presents the Next-Generation Smart Response Web (NG-SRW), a web-based application to aid decision making concerning oil spill response. This tool aims to provide, dynamically and interactively, relevant information on oil spills. By integrating the analysis and visualization of dynamic spill features with the sensitivity of environmental elements and value of human uses, the benefits of potential response actions can be compared, helping to develop an appropriate response strategy. The oil spill process simulation enables the response authorities to judge better the complexity and dynamic behavior of the systems and processes behind the potential environmental impact assessment and thereby better control the oil combat action.

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

2019 ◽  
Vol 2 (1) ◽  
pp. 3-14 ◽  
Author(s):  
Mawuli Afenyo ◽  
Faisal Khan ◽  
Brian Veitch ◽  
Adolf K. Y. Ng ◽  
Zaman Sajid ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Arctic Ocean ◽  
Network Model ◽  
Oil Spill ◽  
Object Oriented ◽  
The Arctic ◽  
Bayesian Network Model ◽  
The Arctic Ocean ◽  
Oil Spill Response

Baseline Studies and Risk Analyses in the Baltic Sea

1991 ◽  
Vol 1991 (1) ◽  
pp. 81-86
Author(s):  
Klavs Bender ◽  
Preben Østfeldt ◽  
Hanne Bach
Keyword(s):  
Baltic Sea ◽  
Oil Spill ◽  
Oil And Gas ◽  
Source Rocks ◽  
The Baltic Sea ◽  
Oil Companies ◽  
Total Hydrocarbon ◽  
Hydrocarbon Content ◽  
The Baltic ◽  
Total Hydrocarbon Content

ABSTRACT In 1986 an oil and gas concession in the Baltic Sea was granted by the Danish Ministry of Energy to a group of oil companies, with Norsk Hydro as operator. A paper describing the goals achieved midway through the program was presented at the 1989 Oil Spill Conference in San Antonio, Texas. This paper presents the final results of the program. The chemical baseline study has shown that the only parameter that exhibits a marked variation is the total hydrocarbon content, where values are seven to eight times higher in the 1989 samples than in the 1987 and 1988 samples. It is suspected that the reason is seasonal variation, since the 1989 samples were collected in June, while the 1987 and 1988 samples were collected in September. Even though the total hydrocarbon content varies significantly between some of the sample suites, only biomarkers related to recent organic material or immature petroleum source rocks are present in the samples. Petrogenic hydrocarbons related to spilled crude oil or exploration activities were not found in the area. The spreading and weathering calculations revealed the zones along the coast where an oil spill was most likely to occur. The transport time for an oil spill to reach the coast was also calculated by the model. In combination with mapping of sensitive resources, the results from the model test runs were used in risk assessments. The information gained in this study was used in the oil spill contingency plan.

INTEGRATING STATE-OF-THE-ART SHORELINE INTERACTION KNOWLEDGE INTO SPILL MODELING

2008 ◽  
Vol 2008 (1) ◽  
pp. 915-922 ◽  
Author(s):  
Dagmar Schmidt Etkin ◽  
Jacqueline Michel ◽  
Deborah French McCay ◽  
Michel Boufadel ◽  
Hailong Li
Keyword(s):  
Literature Review ◽  
Oil Spill ◽  
Risk Model ◽  
State Of The Art ◽  
Empirical Studies ◽  
Practical Method ◽  
Complex Interaction ◽  
Laboratory Research ◽  
It Impacts ◽  
Comprehensive Literature Review

ABSTRACT An understanding of the complex interaction between oil and shoreline sediments in nearshore areas after an oil spill is essential for more accurate oil spill risk analysis modeling. In particular, an estimation of the degree to which shoreline sediments hold and retain oil during the 10–30 days after a spill is required for modeling the fate and trajectory of oil as it impacts a shoreline and either strands on or penetrates into the sediment or refloats to be deposited elsewhere. A comprehensive literature review of empirical studies, laboratory research, and oil-shoreline modeling was conducted. For a spill risk model to be applied in a stochastic manner, a relatively simple and practical method to estimate the oil holding capacity of shoreline sediments based on shoreline type and oil properties was derived from empirical shoreline cleanup assessment team (SCAT) data and a theoretical hydraulics model. The suggested methodology can be applied by spill modelers needing a way to estimate the amount of oil held by a shoreline upon impact to allow a trajectory model to more accurately project the total spread of oil.

scholarly journals Oil spill modelling methods: application to the south–eastern part of the Baltic Sea

Baltica ◽  
2014 ◽  
Vol 27 (special) ◽  
pp. 15-22 ◽  
Author(s):  
Alexander Kileso ◽  
Boris Chubarenko ◽  
Petras Zemlys ◽  
Igor Kuzmenko
Keyword(s):  
Baltic Sea ◽  
Oil Spill ◽  
Oil Spills ◽  
The State ◽  
The South ◽  
The Baltic Sea ◽  
South Eastern ◽  
The Baltic ◽  
Modelling Methods ◽  
State Of Art

The state-of-art in oil spill modelling methods is summarized, focusing on development since 2000. Some recommendations for possible application of these methods to the south–eastern part of the Baltic Sea are prepared. Particular attention is paid on the methods of parameterization of volume of oil spill and calculation of advection of the oil spills. Consideration is also given to methods used in oil weathering models.

scholarly journals Integration of Interpretive Structural Modeling with Fuzzy Bayesian Network for Risk Assessment of Tunnel Collapse

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Leping He ◽  
Tao Tang ◽  
Qijun Hu ◽  
Qijie Cai ◽  
Zhijun Li ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Analysis ◽  
Bayesian Network ◽  
Risk Model ◽  
Multicriteria Decision Making ◽  
Structural Modeling ◽  
Interpretive Structural Modeling ◽  
Construction Risk ◽  
Tunnel Collapse ◽  
The Relationship

Frequent collapse accidents in tunnels are associated with many construction risk factors, and the interrelationship among these risk factors is complex and ambiguous. This study’s aim is to clarify the relationship among risk factors to reduce the tunnel collapse risk. A multicriteria decision-making method is proposed by combining interpretive structural modeling (ISM) and fuzzy Bayesian network (FBN). ISM is used to determine the hierarchical relationships among risk factors. FBN quantitatively analyzes the strength of the interaction among risk factors and conducts risk analysis. The ISM-FBN method contains three steps: (1) drawing the ISM-directed graph; (2) obtaining the probability of the FBN nodes; and (3) using GeNle to implement risk analysis. The proposed method is also used to assess the collapse risk and detect the critical factors in the Canglongxia Tunnel, China. This method’s tunnel collapse risk model can provide managers with clear risk information and better realize project management.

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