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Deliverable 7.2-2: Bayesian Belief Networks: Linking abiotic and biotic data
- Source :
- van Geest, G, Kramer, L, Buijse, T, Moe, J, Couture, R-M, Solheim, A L, Molina Navarro, E, Andersen, H E, Trolle, D, Rankinen, K & Segurado, P 2017, Deliverable 7.2-2: Bayesian Belief Networks: Linking abiotic and biotic data . MARS Project Deliverables, vol. 7.2-2 .
- Publication Year :
- 2017
-
Abstract
- Aquatic ecosystems in Europe have been heavily degraded over the past century, as a result of stressors including eutrophication, hydromorphological alterations and overfishing. Accordingly, many measures have been carried out or are planned to improve the ecological status of water bodies. For this, models are required to forecast the effects of the measures planned. Over the past decade, Bayesian Belief Networks (BBNs) models are increasingly applied to aquatic ecosystems. BBNs have a number of advantages, such as explicit incorporation of uncertainty in the outcome, the ability to handle incomplete datasets, expert opinions and model simulations, and a relative simple graphical representation of complex ecosystems interactions. Accordingly, there is an increasing interest in the construction and application of BBNs in water management.
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- van Geest, G, Kramer, L, Buijse, T, Moe, J, Couture, R-M, Solheim, A L, Molina Navarro, E, Andersen, H E, Trolle, D, Rankinen, K & Segurado, P 2017, Deliverable 7.2-2: Bayesian Belief Networks: Linking abiotic and biotic data . MARS Project Deliverables, vol. 7.2-2 .
- Accession number :
- edsair.pure.au.......a1af34fc20ad45728dfa5255cf0c266a