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

被引:344
作者
Pollino, Carmel A.
Woodberry, Owen
Nicholson, Ann
Korb, Kevin
Hart, Barry T.
机构
[1] Monash Univ, Water Studies Ctr, Clayton, Vic 3800, Australia
[2] Monash Univ, Fac Informat Technol, Clayton, Vic 3800, Australia
关键词
Bayesian network; ecological risk assessment; ecology; fish;
D O I
10.1016/j.envsoft.2006.03.006
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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 Bus 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). (c) 2006 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1140 / 1152
页数:13
相关论文
共 38 条
[1]  
[Anonymous], 1988, PROBABILISTIC REASON, DOI DOI 10.1016/C2009-0-27609-4
[2]  
[Anonymous], 2002, IMPLICATIONS RIVERS
[3]  
[Anonymous], 2005, ECOLOGICAL RISK MANA
[4]  
Bhattacharyya A., 1943, B CALCUTTA MATH SOC, V35, P99, DOI DOI 10.1038/157869B0
[5]   A Bayesian network of eutrophication models for synthesis, prediction, and uncertainty analysis [J].
Borsuk, ME ;
Stow, CA ;
Reckhow, KH .
ECOLOGICAL MODELLING, 2004, 173 (2-3) :219-239
[6]   The use of Hugin® to develop Bayesian networks as an aid to integrated water resource planning [J].
Bromley, J ;
Jackson, NA ;
Clymer, OJ ;
Giacomello, AM ;
Jensen, FV .
ENVIRONMENTAL MODELLING & SOFTWARE, 2005, 20 (02) :231-242
[7]   Coupling real-time control and socio-economic issues in participatory river basin planning [J].
Castelletti, A. ;
Soncini-Sessa, R. .
ENVIRONMENTAL MODELLING & SOFTWARE, 2007, 22 (08) :1114-1128
[8]  
Cooke R., 1991, EXPERTS UNCERTAINTY
[9]   Properties of sensitivity analysis of Bayesian belief networks [J].
Coupé, VMH ;
van der Gaag, LC .
ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE, 2002, 36 (04) :323-356
[10]   Using sensitivity analysis for efficient quantification of a belief network [J].
Coupé, VMH ;
Peek, N ;
Ottenkamp, J ;
Habbema, JDF .
ARTIFICIAL INTELLIGENCE IN MEDICINE, 1999, 17 (03) :223-247