Keywords
oil transport, bayesian networks, risk analysis, decision support
Start Date
1-7-2012 12:00 AM
Abstract
The maritime traffic in the Gulf of Finland (GoF), North-eastern BalticSea, is predicted to rapidly grow in the near future, which increases theenvironmental risks through both direct environmental effects and by increasing therisk of severe oil accident. A multidisciplinary group of researchers has developeda prototype of risk assessment and decision support model, applying BayesianNetworks (BNs), for the evaluation of environmental risks arising from the oiltransport. It consists of sub-models on tanker collisions, causation probability(human factor), the resulting leak size, and the efficacy of open sea oil recovery.This meta-model is based on three alternative growth scenarios concerning themaritime traffic of the GoF in 2015 and the probability of a major oil accident giventhese conditions within four selected areas. The model can be used to compare theeffectiveness of some preventive management actions and oil recovery against theaccident risk. The multidisciplinary approach developed helps in comparing therisks in different parts of the oil accident cause – effect chain when currentknowledge and uncertainty are taken into account. In addition, a user interface forthe model has been created and tested for the analysis of spatial ecological riskarising from the oil transport in the GoF. A simplified version of the risk assessmentmeta-model is used to calculate probabilistic oil accident scenarios. The resultingprobability distributions are used as an input in Geographic Information System(GIS) -environment, where probabilistic oil drifting maps are calculated accordingly.In the end, these drift calculations are evaluated against information on the knownoccurrences of endangered species on the Finnish coastline and conservationvalue indexes related to them. This allows us to calculate and compare the totalrisk for endangered species given the conditions selected in the risk assessmentmeta-model. This approach provides an interesting, alternative viewpointconcerning the decisions on how and where the available risk managementresources should be directed.
Probabilistic Risk Assessment and Decision Support Tools for the Evaluation of Oil Transport in the Gulf of Finland, North-Eastern Baltic Sea
The maritime traffic in the Gulf of Finland (GoF), North-eastern BalticSea, is predicted to rapidly grow in the near future, which increases theenvironmental risks through both direct environmental effects and by increasing therisk of severe oil accident. A multidisciplinary group of researchers has developeda prototype of risk assessment and decision support model, applying BayesianNetworks (BNs), for the evaluation of environmental risks arising from the oiltransport. It consists of sub-models on tanker collisions, causation probability(human factor), the resulting leak size, and the efficacy of open sea oil recovery.This meta-model is based on three alternative growth scenarios concerning themaritime traffic of the GoF in 2015 and the probability of a major oil accident giventhese conditions within four selected areas. The model can be used to compare theeffectiveness of some preventive management actions and oil recovery against theaccident risk. The multidisciplinary approach developed helps in comparing therisks in different parts of the oil accident cause – effect chain when currentknowledge and uncertainty are taken into account. In addition, a user interface forthe model has been created and tested for the analysis of spatial ecological riskarising from the oil transport in the GoF. A simplified version of the risk assessmentmeta-model is used to calculate probabilistic oil accident scenarios. The resultingprobability distributions are used as an input in Geographic Information System(GIS) -environment, where probabilistic oil drifting maps are calculated accordingly.In the end, these drift calculations are evaluated against information on the knownoccurrences of endangered species on the Finnish coastline and conservationvalue indexes related to them. This allows us to calculate and compare the totalrisk for endangered species given the conditions selected in the risk assessmentmeta-model. This approach provides an interesting, alternative viewpointconcerning the decisions on how and where the available risk managementresources should be directed.
