Bayesian networks (BNs), also known as Bayesian belief networks or Bayes nets, are a kind of probabilistic graphical model that has become very popular to practitioners mainly due to the powerful probability theory involved, which makes them able to deal with a wide range of problems. The goal of this review is to show how BNs are being used in environmental modelling. We are interested in the application of BNs, from January 1990 to December 2010, in the areas of the ISI Web of Knowledge related to Environmental Sciences. It is noted that only the 4.2% of the papers have been published under this item. The different steps that configure modelling via BNs have been revised: aim of the model, data pre-processing, model learning, validation and software. Our literature review indicates that BNs have barely been used for Environmental Science and their potential is, as yet, largely unexploited.

Review: Bayesian networks in environmental modelling

A wide range of quantitative and qualitative modelling research on ecosystem services (ESS) has recently been conducted. The available models range between elementary, indicator-based models and complex process-based systems. A semi-quantitative modelling approach that has recently gained importance in ecological modelling is Bayesian belief networks (BBNs). Due to their high transparency, the possibility to combine empirical data with expert knowledge and their explicit treatment of uncertainties, BBNs can make a considerable contribution to the ESS modelling research. However, the number of applications of BBNs in ESS modelling is still limited. This review discusses a number of BBN-based ESS models developed in the last decade. A SWOT analysis highlights the advantages and disadvantages of BBNs in ESS modelling and pinpoints remaining challenges for future research. The existing BBN models are suited to describe, analyse, predict and value ESS. Nevertheless, some weaknesses have to be considered, including poor flexibility of frequently applied software packages, difficulties in eliciting expert knowledge and the inability to model feedback loops. BBNs are increasingly used to analyse, predict and value ecosystem services (ESS).Most BBN applications in ESS modelling target only a single service.Numerous advantages of BBNs in ESS modelling are demonstrated in current applications.Model drawbacks are absence of feedback loops and obligatory variable discretization.Spatially explicit modelling and modelling of ESS bundles are future opportunities.

A review of Bayesian belief networks in ecosystem service modelling

Bayesian belief networks (BBNs) are probabilistic graphical models that can capture and integrate both quantitative and qualitative data, thus accommodating data-limited conditions. This paper systematically reviews applications of BBNs with respect to spatial factors, water domains, and the consideration of climate change impacts. The methods used for constructing and validating BBN models, and their applications in different forms of decision-making support are examined. Most reviewed publications originate from developed countries (70%), in temperate climate zones (42%), and focus mainly on water quality (42%). In 60% of the reviewed applications model validation was based on the expert or stakeholder evaluation and sensitivity analysis, and whilst in 27% model performance was not discussed. Most reviewed articles applied BBNs in strategic decision-making contexts (52%). Integrated modelling tools for addressing challenges of dynamically complex systems were also reviewed by analysing the strengths and weaknesses of BBNs, and integration of BBNs with other modelling tools. The application of BBNs to water resource management was rarely applied in developing countries and in tropical regions.Only 8% reviewed papers explored potential impacts of climate change on water resources.Only 11% and 6% of reviewed articles applied influence diagrams and Object-Oriented Bayesian Networks respectively.Most reviewed articles applied BBNs in strategic decision-making contexts (52%) for water resource management.Results from BBN models were rarely compared or tested against other modelling approaches to validate their performance.

Applications of Bayesian belief networks in water resource management

Numerous decision support systems have been developed for forest management over the past 20 years or more. In this chapter, the authors briefly review some of the more important and recent developments, including examples from North America, Europe, and Asia. In addition to specific systems, we also review some of the more-significant methodological approaches such as artificial neural networks, knowledge-based systems, and multicriteria decision models. A basic conclusion that emerges from this review is that the availability of DSSs in forest management has enabled more-effective analysis of the options for and implications of alternative management approaches for all components of forest ecosystems. The variety of tools described herein, and the approaches taken by the different systems, provide a sample of the possible methods that can be used to help stakeholders and decision makers arrive at reasoned and reasonable decisions.

Decision Support Systems in Forest Management

Assessing the environmental impact of agri-environment schemes (AESs) is complicated by the lack of both specific measurable objectives and dedicated environmental monitoring of the impacts. A methodology to estimate the environmental performance of AESs was applied in nine EU case study areas, and reduced the complexity of scheme structure into elements that were assessed by experts. Multi-criteria analysis (MCA) techniques helped produce aggregated judgements about single objectives or measures. Expert panels assessed the link between environmental measures and objectives by scoring specific criteria that reflect important factors for delivering environmental effectiveness: valid research models for measures (cause-and-effect); quality of implementation by farmers and institutions; extent of participation and degree of spatial targeting. Multi-criteria analysis enabled comparison of the degree to which environmental effectiveness (estimated from the criteria scores) within a scheme was achieved across e...

Ex post environmental evaluation of agri-environment schemes using experts' judgements and multicriteria analysis.

Use of belief network modelling to assess the impact of buffer zones on water protection and biodiversity

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Use of belief network modelling to assess the impact of buffer zones on water protection and biodiversity