Annukka Lehikoinen

Optimizing the recovery efficiency of Finnish oil combating vessels in the Gulf of Finland using Bayesian Networks.

Oil transport has greatly increased in the Gulf of Finland over the years, and risks of an oil accident occurring have risen. Thus, an effective oil combating strategy is needed. We developed a Bayesian Network (BN) to examine the recovery efficiency and optimal disposition of the Finnish oil combating vessels in the Gulf of Finland (GoF), Eastern Baltic Sea. Four alternative home harbors, five accident points, and ten oil combating vessels were included in the model to find the optimal disposition policy that would maximize the recovery efficiency. With this composition, the placement of the oil combating vessels seems not to have a significant effect on the recovery efficiency. The process seems to be strongly controlled by certain random factors independent of human action, e.g. wave height and stranding time of the oil. Therefore, the success of oil combating is rather uncertain, so it is also important to develop activities that aim for preventing accidents. We found that the model developed is suitable for this type of multidecision optimization. The methodology, results, and practices are further discussed.

A Cross-disciplinary Approach to Minimising the Risks of Maritime Transport in the Gulf of Finland

The maritime traffic in the Gulf of Finland is predicted to rapidly increase in the near future, which increases the environmental risks both through direct environmental effects and by increasing the accident risk. This paper describes a multidisciplinary modelling approach, where, based on growth predictions, the maritime traffic in the Gulf of Finland in the year 2015 is modelled and the accident risk, the direct environmental effects and the risk of environmental accidents are evaluated. Finally, the effects of national and international legislation and other management actions are modelled, to produce advice and support for governmental decision makers. In the modelling work, Bayesian Networks (BNs) are applied. The approach produces unique information on the accident risks and their effects separately for each marine route used, which enables efficient local risk control actions to be taken by the decision makers to decrease the probability of accidents.

A Bayesian network for assessing the collision induced risk of an oil accident in the Gulf of Finland

The growth of maritime oil transportation in the Gulf of Finland (GoF), North-Eastern Baltic Sea, increases environmental risks by increasing the probability of oil accidents. By integrating the work of a multidisciplinary research team and information from several sources, we have developed a probabilistic risk assessment application that considers the likely future development of maritime traffic and oil transportation in the area and the resulting risk of environmental pollution. This metamodel is used to compare the effects of two preventative management actions on the tanker collision probabilities and the consequent risk. The resulting risk is evaluated from four different perspectives. Bayesian networks enable large amounts of information about causalities to be integrated and utilized in probabilistic inference. Compared with the baseline period of 2007-2008, the worst-case scenario is that the risk level increases 4-fold by the year 2015. The management measures are evaluated and found to decrease the risk by 4-13%, but the utility gained by their joint implementation would be less than the sum of their independent effects. In addition to the results concerning the varying risk levels, the application provides interesting information about the relationships between the different elements of the system.

A software system for assessing the spatially distributed ecological risk posed by oil shipping

A maritime accident involving an oil tanker may lead to large scale mortality or reductions in populations of coastal species due to oil. The ecological value at stake is the biota on the coast, which are neither uniformly nor randomly distributed. We used an existing oil spill simulation model, an observation database of threatened species, and a valuation method and developed a software system for assessing the spatially distributed ecological risk posed by oil shipping. The approach links a tanker accident model to a set of oil spill simulations and further to a spatial ecological value data set. The tanker accident model is a Bayesian network and thus we present a case of using a Bayesian network in geographic analysis. A case in the Gulf of Finland is used for illustration of the methodology. The method requires and builds on an extensive data collection and generation effort and modeling. The main difference of our work to earlier works on using a Bayesian network in geospatial setting is that in our case the Bayesian network was used to compute the probabilities of spatial scenarios directly in a global sense while in earlier works Bayesian networks have been used for each location separately to obtain global results. The result was a software system that was used by a distributed research team. We develop a system for assessing spatially distributed ecological risk.We link geospatial scenarios with a Bayesian network to obtain an expected scenario.Assessment of ecological risk of oil spill requires an extensive data collection.Foreign function interfaces are effective in linking heterogeneous software.

Evaluating the impact of nutrient abatement measures on the ecological status of coastal waters: a Bayesian network for decision analysis

Environmental managers must make decisions about complex problems that have a high degree of uncertainty such as, which nutrient abatement measure optimally improves the condition of an ecosystem. Although data and models that provide information on this subject exist, their knowledge may be fragmentary and difficult to interpret. We present a user-friendly modelling tool that integrates results of different models and data-analyses. It can be used by decision-makers for assessing the probabilities of different nutrient abatement scenarios for achieving specific targets set by the Water Framework Directive for Finnish coastal waters in the Gulf of Finland. The results suggest that significant reductions in nutrient loads are required to achieve good ecological status in Finnish coastal waters, and in the event of increased precipitation these targets may be less likely to be attained. Moreover, different approaches to the status classification lead to very different conclusions.

Essential fish habitats (EFH)

Many fish species in the Baltic Sea are dependent on shallow and sheltered near-shore habitats for their spawning, nursery, feeding and migration. Still, the role of these essential fish habitats, ...