Parameterisation and evaluation of a Bayesian network for use in an ecological risk assessment

  • Authors:

  • Carmel A. Pollino;Owen Woodberry;Ann Nicholson;Kevin Korb;Barry T. Hart

  • Affiliations:

  • Water Studies Centre, Monash University, 3800, Australia;Faculty of Information Technology, Monash University, 3800, Australia;Faculty of Information Technology, Monash University, 3800, Australia;Faculty of Information Technology, Monash University, 3800, Australia;Water Studies Centre, Monash University, 3800, Australia

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

Catchment managers face considerable challenges in managing ecological assets. This task is made difficult by the variable and complex nature of ecological assets, and the considerable uncertainty involved in quantifying how various threats and hazards impact upon them. Bayesian approaches have the potential to address the modelling needs of environmental management. However, to date many Bayesian networks (Bn) developed for environmental management have been parameterised using knowledge elicitation only. Not only are these models highly qualitative, but the time and effort involved in elicitation of a complex Bn can often be overwhelming. Unfortunately in environmental applications, data alone are often too limited for parameterising a Bn. Consequently, there is growing interest in how to parameterise Bns using both data and elicited information. At present, there is little formal guidance on how to combine what can be learned from the data with what can be elicited. In a previous publication we proposed a detailed methodology for this process, focussing on parameterising and evaluating a Bn. In this paper, we further develop this methodology using a risk assessment case study, with the focus being on native fish communities in the Goulburn Catchment (Victoria, Australia).