Bayesian belief networks as a meta-modelling tool in integrated river basin management — Pros and cons in evaluating nutrient abatement decisions under uncertainty in a Norwegian river basin

Barton, David N.; Saloranta, Tuomo; Moe, S. Jannicke; Eggestad, H.O.; Kuikka, Sakari

doi:10.1016/j.ecolecon.2008.02.012

Cited by 165 publications

(141 citation statements)

References 14 publications

Supporting

Mentioning

140

Contrasting

Order By: Relevance

“…An important limitation of Bayesian networks is their directed acyclic structure, which limits the description of relationships among nodes to one-way cause and effect. This prevents the representation of feedback relationships that may be known to exist in some systems (Uusitalo 2007, Barton et al 2008. It is also very difficult to represent temporal dynamics in Bayesian networks, although methods exist for doing so Ellis 2006, Uusitalo 2007).…”

Section: Discussionmentioning

confidence: 99%

“…Like all models, Bayesian networks are simplifications of real systems and the simplest possible network structure should be used (Barton et al 2008). Simplification can be achieved by limiting the random variables in the network to those that can be observed or that the modeler may want to query (Koller and Friedman, 2009).…”

Section: Best Practices For Constructing Bayesian Network Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian Networks for Modeling Dredging Decisions

Schultz¹,

Borrowman²,

Small³

2011

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Section: Best Practices For Constructing Bayesian Network Modelsmentioning

confidence: 99%

Bayesian Networks for Modeling Dredging Decisions

Schultz¹,

Borrowman²,

Small³

2011

View full text Add to dashboard Cite

“…Reducing H(X) by collecting information, in addition to the current knowledge about the variable X, is interpreted as reducing the uncertainty about the true state of X [63]. The entropy measure therefore enables an assessment of the additional information required to specify a particular alternative.…”

Section: Consistency and Quality Assessment Of The Bns Model And Its mentioning

confidence: 99%

Innovative Analysis of Runoff Temporal Behavior through Bayesian Networks

Molina

Zazo

Rodríguez‐Gonzálvez

et al. 2016

Water

View full text Add to dashboard Cite

Hydrological series are largely characterized by a strong random component in their behavior. More noticeable changes in the behavior patterns of rainfall/runoff temporal series are recently being observed. These modifications are not a trivial issue, especially in regards to peculiarities, non-linearities, diffused influences or higher time orders of dependence. This study mainly aimed to analyze the temporal dependence of an annual runoff series dynamically. This approach comprises a coupling between classic techniques (Autoregressive Moving Average Model, ARMA) and novel ones, based on Artificial Intelligent for hydrological research (Bayesian Networks, BNs). An ARMA model was built to provide reliable data to populate BNs. Then, causal reasoning, through Bayes's theorem, allows the identification of the logic structure of temporal dependences within time series. Furthermore, the resultant conditional probability permits the quantification of the relative percentage of annual runoff change, and provides the right time order of dependence. This research introduces an original methodology able to build a logic structure for a stochastic analysis of temporal behavior. This approach also aimed to provide a powerful and graphic modeling method for improving the understanding of the dynamic runoff series temporal behavior. This was successfully demonstrated in two unregulated river basin stretches, belonging to the Duero river basin which is the largest basin in Spain.

show abstract

“…In this way a range of information types can be included in the model. This flexibility has allowed BBNs to be applied to a wide range of issues, including environmental (Barton et al 2008;Borsuk et al 2004;Raphael et al 2001) and natural resource management (Bromley et al 2005), assessment of the impact of alternative management measures Nyberg et al 2006) and marine spatial planning (Stelzenmüller et al 2010b). …”

Section: Description Of Bayesian Belief Networkmentioning

confidence: 99%

Cumulative effects assessment: proof of concept marine mammals

Piet¹,

Boon²,

Jongbloed³

et al. 2017

View full text Add to dashboard Cite

Piet, G.J 1 ., Boon, A 2 . Jongbloed, R 1 ., van der Meulen, M 2 ., Tamis, J 1 . van der Wal, J.T 1 .Teal ,L SummaryThis development of the framework and approach for a Cumulative Effects Assessment (CEA) is based on a literature review. From this we adopted several definitions that guided this development.• A CEA is understood as "a systematic procedure for identifying and evaluating the significance of effects from multiple sources/activities and for providing an estimate on the overall expected impact to inform management measures. The analysis of the causes (source of pressures and effects), pathways and consequences of these effects on receptors is an essential and integral part of the process".• Cumulative effects are "the incremental impact of the action when added to the other past, present and reasonably foreseeable actions". In this approach we only consider a (cumulative) effect significant if it has an impact on a relevant ecosystem component.Therefore our framework and approach for a CEA is based on all human activities that may have a potential impact on any relevant (from a policy perspective) ecosystem component at an appropriate spatio-temporal scale.The literature also identified some key challenges that need to be addressed for CEA to evolve into a consistent, appropriate tool to assist decision-making. These challenges included• A clear distinction of the receptor-led CEA from the dominating stressor-led Environmental Impact Assessment (EIA) approaches and• Enabling CEA to provide ecosystem-relevant information at an appropriate regional scale.Therefore this CEA is explicitly developed to be a receptor-led and fully integrated framework, i.e.involving multiple occurrences of multiple pressures (from single and/or different sources) on multiple receptors, as opposed to other existing approaches dealing with only a subset of those pressures or receptors, hence our use of the phrase iCEA for integrated CEA. As a proof of concept for this iCEA we selected one receptor, the ecosystem component marine mammals.From the literature review we adopted (and slightly modified) a risk-based framework for defining and undertaking cumulative effects assessments which is aligned to the work in the OSPAR IntersessionalCorrespondence Group on Cumulative (ICG-C) Effects and the ICES Working Group on IntegratedAssessments of the North Sea (WGINOSE), thereby ascertaining this framework and approach is wellplaced within ongoing international North Sea initiatives. Furthermore, the CEA framework in this study should contribute to national North Sea policymaking, with a specific focus on the Marine Strategy framework Directive (MSFD). This literature review is presented in Chapter 1.Our iCEA framework consists of four phases each corresponding to a chapter in this report:1. Conception. This is where purpose and scope are defined (see Chapter 2). 3. Execution (importance). Here we establish the relative importance of each impact chain using a risk-based approach that calculates "Impact Risk", i.e. the contribution of...

show abstract

Bayesian belief networks as a meta-modelling tool in integrated river basin management — Pros and cons in evaluating nutrient abatement decisions under uncertainty in a Norwegian river basin

Cited by 165 publications

References 14 publications

Bayesian Networks for Modeling Dredging Decisions

Bayesian Networks for Modeling Dredging Decisions

Innovative Analysis of Runoff Temporal Behavior through Bayesian Networks

Cumulative effects assessment: proof of concept marine mammals

Contact Info

Product

Resources

About