Good practice in Bayesian network modelling
Introduction
Bayesian networks (BNs) represent systems as a network of interactions between variables from primary cause to final outcome, with all cause–effect assumptions made explicit. BNs are often considered suitable for modelling environmental systems due to their ability to integrate multiple issues, interactions and outcomes and investigate tradeoffs. Furthermore, they are apt for utilising data and knowledge from different sources and handling missing data. BNs readily incorporate and explicitly represent uncertain information, and this uncertainty is propagated through to and expressed in the model outputs. BNs are based on a relatively simple causal graphical structure, meaning they can be built without highly technical modelling skills and be understood by non-technical users and stakeholders. This is a very valuable feature of BNs, particularly in the context of natural resource management which benefits from interdisciplinary and participatory processes (Voinov and Bousquet, 2010).
In BNs, variables are represented by nodes, which are linked by arcs that symbolise dependent relationships between variables. The strength of these relationships is defined in the Conditional Probability Tables (CPTs) attached to each node. CPTs specify the degree of belief (expressed as probabilities) that the node will be in a particular state given the states of the parent nodes (the nodes that directly affect that node). Evidence is entered into the BN by substituting the a priori beliefs of one or more nodes with observation or scenario values. Through belief propagation using Bayes' Theorem, the a priori probabilities of the other nodes are updated. This belief propagation enables BNs to be used for diagnostic (‘bottom–up’ reasoning) or explanatory purposes (‘top–down’ reasoning) (Castelletti and Soncini-Sessa, 2007). Therefore unlike black-box models, such as neural networks (Chen et al., 2008), BN users can interrogate the reasoning behind the model outputs as interactions between variables are clearly displayed, providing transparency to users and promoting system learning. BNs can be used for classification and prediction of states or events even when data is partial or uncertain (Newton, 2010), which is a huge advantage over many other traditional statistical models that rely on large amounts of empirical data to be built (Marcot et al., 2006).
There is an enormous scope for the possible applications of BNs in natural resources management including species or community models (Marcot et al., 2001; Borsuk et al., 2006), management models (Bromley et al., 2004; Lynam et al., 2010; Nash and Hannah, 2011), integrated models (Ticehurst et al., 2007; Kragt et al., 2011) social models (Ticehurst et al., 2011), and risk assessment models (Pollino and Hart, 2005; Pollino et al., 2007b). The limitations of BNs include their inability to readily represent feedback loops and dynamic relationships (Uusitalo, 2007). However some software packages can handle dynamic models by representing each time slice with a separate network (Kjærulff, 1995) and there has also been some progress in the development of spatial BNs (Smith et al., 2007). The case study presented later in this paper is a spatial BN linked to GIS to model habitat suitability for an endangered species. More details about the advantages and limitations of BNs in environmental modelling can be found in Castelletti and Soncini-Sessa (2007) and Uusitalo (2007). Commonly used BN software platforms include Hugin Expert (Hugin, www.hugin.com), Netica (Norsys Software Corp., www.norsys.com), Analytica (Lumina Decision Systems, www.lumina.com), GeNIe and SMILE (University of Pittsburgh, genie.sis.pitt.edu), BUGS (MRC and Imperial College, www.mrc-bsu.cam.ac.uk/bugs) and BayesiaLab (Bayesia Ltd., www.bayesia.com).
Modelling can be a useful approach to understanding and supporting decisions on environmental systems. However, for a model to be of value, good practice in its construction, testing and application is essential, as is awareness of the purposes, capabilities and limitations of the modelling approach. Without this, there is a risk of the model user misinterpreting or misusing model outputs, and drawing invalid conclusions (Jakeman et al., 2006). Poor modelling practice reduces the credibility of the model and can lead to the model capabilities being ‘oversold’, potentially causing poor decisions to be made based on models, or where model transparency and testing has not been completed, users mistrusting models and their outputs (Refsgaard and Henriksen, 2004). Models that are not properly evaluated also risk being discredited. Consequently, guidelines for good modelling practice that create standards to help ensure the development and application of credible and purposeful models are essential.
Several authors have developed modelling guidelines (Refsgaard and Henriksen, 2004; Jakeman et al., 2006; Crout et al., 2008), where the key components for good practice include:
- •
Clearly defining model purpose and the assumptions underlying the model
- •
Thorough evaluation of the model and its results
- •
Transparent reporting of the whole modelling process, including its formulation, parameterisation, implementation and evaluation
Good modelling practice will result in better understanding of the development and application of models, benefitting not only the modelling community but also model users.
The objective of this paper is to introduce a good practice framework for developing and evaluating BN models of environmental systems. BN modelling protocols have been published by Cain (2001) and Marcot et al. (2006). Cain (2001) provided guidelines to using BNs for supporting planning and management of natural resources, with a large emphasis on facilitating stakeholder consultation. In the context of natural resources management, stakeholder consultation is seen as essential to ensuring that the management plan is followed through and implemented (Cain, 2001). Marcot et al. (2006) developed guidelines for Bayesian networks applied to wildlife and ecological assessment, with the steps to developing and updating the BNs described at three model levels: alpha, beta and gamma. The alpha-level model is the initial functioning BN, suitable only for internal use and review. The BN is considered a beta-level model after formal peer review and revision is conducted. The gamma-level or final application model, is created by further testing, calibrating, validating and updating the beta-level model (Marcot et al., 2006). This paper presents an updated set of guidelines relevant to the wide scope of possible applications of the modelling approach.
The development and evaluation process of BNs is explored following the generic guidelines for good modelling practice outlined by Jakeman et al. (2006) and demonstrated by Welsh (2008), Robson et al. (2008) and Blocken and Gualtieri (2012). It is envisaged that adhering to the guidelines will enhance the quality and value of BNs in generating and sharing knowledge on environmental systems and providing advice on their management. Good practice in BN modelling is discussed, followed by a case study BN that models the habitat suitability of an endangered invertebrate species, Astacopsis gouldi.
Section snippets
Generic good modelling practice guidelines
This paper is intended to be used in conjunction with the good modelling practice framework by Jakeman et al. (2006), which consist of ten iterative steps:
- (1)
Define model purpose
- (2)
Specify modelling context (scope and resources)
- (3)
Conceptualise the system, specify data and other prior knowledge
- (4)
Select model features and families
- (5)
Decide how to find model structure and parameter values
- (6)
Select estimation performance criteria and technique
- (7)
Identify model structure and parameters
- (8)
Conditional verification and
Case study: juvenile A. gouldi habitat suitability model
This section presents a habitat suitability model for juvenile A. gouldi, the giant freshwater crayfish to demonstrate good practice in BN modelling. A. gouldi is the largest known freshwater invertebrate, and is endemic to Tasmania, Australia. The model is linked to GIS, thereby attempting to overcome BNs' weakness in representing spatial relationships. The integration of GIS with BN has gained considerable interest in recent years. Examples of such applications include BNs used to model the
Conclusion
The ability of BNs to integrate data and knowledge from different sources and handle uncertainty and missing data, makes the approach appealing for modelling environmental systems. Furthermore the logical and visual nature of BNs enables non-experts to understand the model structure and contribute to parts of the model development with relative ease, making the approach highly suitable for participatory modelling. The modular architecture of BNs facilitates their application in integrated
Acknowledgements
This paper is an extension of work presented at the 2010 International Congress on Environmental Modelling and Software, Ottawa. The authors would like to acknowledge Landscape Logic for providing support for this research. They wish to thank Peter Davies (University of Tasmania), Sarah Munks (Forestry Practices Authority Tasmania) and Danielle Hardie (Department of Primary Industries, Parks, Water and Environment Tasmania) for their advice and help in providing data for the study. Thanks also
References (67)
- et al.
Hybrid Bayesian network classifiers: application to species distribution models
Environmental Modelling and Software
(2010) - et al.
Bayesian networks in environmental modelling
Environmental Modelling and Software
(2011) - et al.
An evaluation of automated structure learning with Bayesian networks: an application to estuarine chlorophyll dynamics
Environmental Modelling and Software
(2011) - et al.
Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology
Journal of Hydrology
(2001) - et al.
Ten iterative steps for model development and evaluation applied to computational fluid dynamics for environmental fluid mechanics
Environmental Modelling and Software
(2012) Ecological Informatics: Bayesian networks
- et al.
Assessing the decline of brown trout (Salmo trutta) in Swiss rivers using a Bayesian probability network
Ecological Modelling
(2006) - et al.
A Bayesian network of eutrophication models for synthesis, prediction, and uncertainty analysis
Ecological Modelling
(2004) - et al.
Bayesian networks and participatory modelling in water resource management
Environmental Modelling and Software
(2007) - et al.
Artificial intelligence techniques: an introduction to their use for modelling environmental systems
Mathematics and Computers in Simulation
(2008)
Good modelling practice
A Bayesian belief network analysis of factors influencing wildfire occurrence in Swaziland
Environmental Modelling and Software
Distribution and conservation status of the Tasmanian giant freshwater lobster Astacopsis gouldi (Decapoda: Parastacidae)
Biological Conservation
Ten iterative steps in development and evaluation of environmental models
Environmental Modelling and Software
dHugin: a computational system for dynamic time-sliced Bayesian networks
International Journal of Forecasting
An integrated approach to linking economic valuation and catchment modelling
Environmental Modelling and Software
Inference in hybrid Bayesian networks
Reliability Engineering and System Safety
Structural uncertainty in a river water quality modelling system
Ecological Modelling
Adaptive modelling for adaptive water quality management in the Great Barrier Reef region, Australia
Environmental Modelling and Software
Using Bayesian belief networks to evaluate fish and wildlife population viability under land management alternatives from an environmental impact statement
Forest Ecology and Management
Integrated water resources management of overexploited hydrogeological systems using Object-Oriented Bayesian networks
Environmental Modelling and Software
Using Monte-Carlo simulations and Bayesian networks to quantify and demonstrate the impact of fertiliser best management practices
Environmental Modelling and Software
Use of a Bayesian network for Red Listing under uncertainty
Environmental Modelling and Software
Examination of conflicts and improved strategies for the management of an endangered Eucalypt species using Bayesian networks
Ecological Modelling
Parameterisation and evaluation of a Bayesian network for use in an ecological risk assessment
Environmental Modelling and Software
Modelling guidelines – terminology and guiding principles
Advances in Water Resources
On the usefulness of overparameterized ecological models
Ecological Modelling
Ten steps applied to development and evaluation of process-based biogeochemical models of estuaries
Environmental Modelling and Software
How to avoid a perfunctory sensitivity analysis
Environmental Modelling and Software
Using a Bayesian belief network to predict suitable habitat of an endangered mammal – the Julia Creek Dunnart (Sminthopsis douglasi)
Biological Conservation
Assessment of a Bayesian belief network-GIS framework as a practical tool to a support marine planning
Marine Pollution Bulletin
Using Bayesian networks to complement conventional analyses to explore landholder management of native vegetation
Environmental Modelling and Software
A Bayesian network approach to assess the sustainability of coastal lakes
Environmental Modelling and Software
Cited by (494)
Supporting spatial planning with a novel method based on participatory Bayesian networks: An application in Curaçao
2024, Environmental Science and PolicySeamounts ecological modelling: A comprehensive review and assessment of modelling suitability to emergent challenges
2024, Ocean and Coastal ManagementDynamic risk analysis of allision in port areas using DBN based on HFACS-PV
2024, Ocean EngineeringBalancing observational data and experiential knowledge in environmental flows modeling
2024, Environmental Modelling and SoftwareHow Bayesian networks are applied in the subfields of climate change: Hotspots and evolution trends
2024, Environmental Modelling and SoftwareA trade-off between farm production and flood alleviation using land use tillage preferences as a natural flood management (NFM) strategy
2023, Smart Agricultural Technology