Publications

Integrating Bayesian Belief Networks in a toolbox for decision support on plastic clean-up technologies in rivers and estuaries
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Four clusters of parameters were identified as potentially affecting bycatch generation. The environmental conditions in which the clean-up system is deployed, the characteristics of the biota with which the system interacts, the characteristics of the plastic items within the system, and the design and operation of the clean-up mechanism itself comprise this list. Transparent and objective analyses can help quantify and assess bycatch by cluster. This paper discusses the use of Bayesian Belief Networks as a promising probabilistic modelling method for a trade-off between removal efficiency and bycatch.

 

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