Bayesian Network applications for environmental risk assessment

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Environmental risk assessment (ERA) is a process of estimating the probability and consequences of an adverse event due to pressures or changes in environmental conditions resulting from human activities. Its purpose is to search the optimal courses of action under uncertainty when striving for the sustainable use of environment through minimizing the potential losses. As environmental issues are typically multidisciplinary, addressing large amount of eco-societal inter-linkages, an optimal tool for the ERA should enable the efficient integration and meta-analysis of multidisciplinary knowledge. By describing the causalities and studying the interactions among its components, this kind of integrative analysis provides us better understanding about the environmental system in focus. In addition, the functional ERA application should allow exploring, explaining and forecasting the responses of an environmental system to changes in natural and human induced stressors, serving as a decision support model that enables the search of optimal management strategy, also in the presence of imperfect knowledge.

Bayesian Network (BN) is a graphical model that enables the integration of both quantitative and qualitative data and knowledge to a causal chain of inference. It is a powerful tool for synthesising knowledge, logic and rules, providing aid for thinking about complex systems that are too demanding to be analysed by human brains alone. In a BN, all the knowledge is handled in the form of probability distributions, thus the result represents the prevailing state of knowledge. The method facilitates analysing the location and amount of uncertainty explicitly, as well as enables studying its significance when it comes to the decision making.

The main contribution of this thesis is to share experiences and ideas about the development and use of the ERA applications executed by using the BN as method. The perspective of the work is dichotomic. The objective in the separate studies presented in the articles have been on one hand to develop tools for integrating available knowledge and materials to enable the quantitative assessment of the environmental risks. On the other hand, the ultimate aim has been to learn more about the environmental risks and their potential management in the case study area of the Gulf of Finland. In this thesis, both of these perspectives are considered. Eutrophication and oil transportations at the Gulf of Finland are used as the case issues.

The thesis concludes that Bayesian networks have plenty of properties that are useful for ERA and the method can be used for solving problems typical for that field analytically. By planting the developed graphical BNs in the commonly used Drivers-Pressures-States-Impacts-Responses -problem structuring framework, it is also demonstrated that combining these two approaches can be helpful in conceptual modeling, enabling the better framing of the research problem at hand and thinking about it systematically. The greatest challenges concerning the BN-ERA modeling are found to be related to the computational limitations of the current BN software, when it comes to the joint use of the discretised and continuous variables, as well as the restricted capacity to include the spatial resolution to the models. Producing the prior probability distributions by using deterministic models is also noted to be relatively tedious and time-consuming. The issues of end use of the applications, problems related to the scientific publishing of them, as well as the advantages and challenges of working in the multidisciplinary research teams are discussed.
Originalspråkengelska
Tryckta ISBNISBN 978-951-51-0109-9
Elektroniska ISBNISBN 978-951-51-0110-5
StatusPublicerad - 2014
MoE-publikationstypG5 Doktorsavhandling (artikel)

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