Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6962594 | Environmental Modelling & Software | 2016 | 14 Pages |
Abstract
A Bayesian inference method was employed to quantify uncertainty in an Integrated Multi-Trophic Aquaculture (IMTA) model. A deterministic model was reformulated as a Bayesian Hierarchical Model (BHM) with uncertainty in the parameters accounted for using “prior” distributions and unresolved time varying processes modelled using auto-regressive processes. Observations of kelp grown in 3 seeding densities around salmon pens were assimilated using a Sequential Monte Carlo method implemented within the LibBi package. This resulted in a considerable reduction in the variability in model output for both the observed and unobserved state variables. A reduction in variance between the prior and posterior was observed for a subset of model parameters which varied with seeding density. Kullback-Liebler (KL) divergence method showed the reduction in variability of the state and parameters was approximately 90%. A low to medium seeding density results in the most efficient removal of excess nutrients in this simple system.
Related Topics
Physical Sciences and Engineering
Computer Science
Software
Authors
Scott Hadley, Emlyn Jones, Craig Johnson, Karen Wild-Allen, Catriona Macleod,