A Bayesian network of eutrophication models for synthesis, prediction, and uncertainty analysis

@article{Borsuk2004ABN,
  title={A Bayesian network of eutrophication models for synthesis, prediction, and uncertainty analysis},
  author={Mark E. Borsuk and Craig A. Stow and Kenneth H. Reckhow},
  journal={Ecological Modelling},
  year={2004},
  volume={173},
  pages={219-239}
}

Figures from this paper

Assessing model structure uncertainty through an analysis of system feedback and Bayesian networks.
TLDR
The approach incorporates the effects of feedback on the model's response to a sustained change in one or more of its parameters, provides an efficient means to explore the effect of alternative model structures, mitigates the cognitive bias in expert opinion, and is amenable to stakeholder input.
Application of Bayesian Belief Networks for the prediction of macroinvertebrate taxa in rivers
TLDR
Assessment of the success of prediction of macroinvertebrate taxa in rivers by means of a selected number of environmental variables indicates that thoughtful input variable selection as well as sensitivity analysis will improve the models for practical use in river restoration management.
Application of Bayesian Inference Techniques for Calibrating Eutrophication Models
This research aims to integrate mathematical water quality models with Bayesian inference techniques for obtaining effective model calibration and rigorous assessment of the uncertainty underlying
Predicting the Frequency of Water Quality Standard Violations Using Bayesian Calibration of Eutrophication Models
The water quality standard setting process usually relies on mathematical models with strong mechanistic basis, as this provides assurance that the model will more realistically project the effects
Good practice in Bayesian network modelling
Continuous Bayesian network for studying the causal links between phosphorus loading and plankton patterns in Lake Simcoe, Ontario, Canada.
TLDR
This study suggests that a 15% phosphorus loading decrease will still result in >25% violations of the 4 μg chla/L value in the two embayments of Lake Simcoe, but the TP levels will decrease in response to the exogenous loading reductions and this improvement will be primarily manifested in the northcentral segments of the system.
Bayesian challenges in integrated catchment modelling
TLDR
Some of the key research problems associated with the use of BNs as decision-support tools for environmental management are discussed, including the discretisation problem for continuous variables, solutions to the problem of missing data, and the implementation of a knowledge elicitation framework.
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 111 REFERENCES
Cross-Scale Modeling of Riparian Ecosystem Responses to Hydrologic Management
ABSTRACT There is much demand for quantitative models to aid in comparison of policy options and design of adaptive management policies for riparian ecosystems. Such models must represent a wide
Enhancing Causal Assessment of Estuarine Fishkills Using Graphical Models
TLDR
It is found that the suggestion that toxic Pfiesteria cause fishkills is inconsistent with observation, and the data are more indicative of a model in which PLOs are stimulated to become actively toxic by the presence of already dead or dying fish.
On the usefulness of overparameterized ecological models
A survival model of the effects of bottom-water hypoxia on the population density of an estuarine clam (Macoma balthica)
The effect of bottom-water hypoxia on the population density of the clam Macoma balthica is estimated using a survival-based approach. We used Bayesian parameter estimation to fit a survival model to
Water quality modeling for environmental management: Lessons from the policy sciences
Models are used in many policy arenas to predict the future consequences of current decisions. A model is typically viewed as a rational, objective means of processing complex information to predict
Data-based mechanistic modelling of environmental, ecological, economic and engineering systems.
...
1
2
3
4
5
...