Public participation modelling using Bayesian networks in management of groundwater contamination

Henriksen, Hans Jørgen; Rasmussen, Per; Brandt, Gyrite; Bulow, Dorthe Von; Jensen, Finn Verner

doi:10.1016/j.envsoft.2006.01.008

Cited by 145 publications

(89 citation statements)

References 8 publications

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“…In this modeling approach, a system network is constructed that link various biophysical processes to environmental outcomes, and various decision scenarios can be explored to predict impact to the outcomes. Advantages of Bayesian networks include their conceptual simplicity, graphical support, friendly semantics, and possibility for interaction among different actors (Castelletti and Soncini-Sessa, 2007a;Henriksen et al, 2007). The development of a Bayesian network representation of the system under study, either offline or online, may help in structuring the thinking process, identifying causal relations, integrating different knowledge bases, and making assumptions and limitations of datasets and models more transparent (Henriksen et al, 2007;Olsson and Andersson, 2007).…”

Section: Communication Of Results Scenario Building and Participatomentioning

confidence: 99%

“…Advantages of Bayesian networks include their conceptual simplicity, graphical support, friendly semantics, and possibility for interaction among different actors (Castelletti and Soncini-Sessa, 2007a;Henriksen et al, 2007). The development of a Bayesian network representation of the system under study, either offline or online, may help in structuring the thinking process, identifying causal relations, integrating different knowledge bases, and making assumptions and limitations of datasets and models more transparent (Henriksen et al, 2007;Olsson and Andersson, 2007). Nevertheless, setting up successful participatory monitoring activities requires not only strong buy-in from relevant stakeholders, the process itself is time-consuming and relies upon a high level of leadership and interpersonal skills (Henriksen et al, 2007).…”

Section: Communication Of Results Scenario Building and Participatomentioning

confidence: 99%

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Citizen science in hydrology and water resources: opportunities for knowledge generation, ecosystem service management, and sustainable development

et al. 2014

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The participation of the general public in the research design, data collection and interpretation process together with scientists is often referred to as citizen science. While citizen science itself has existed since the start of scientific practice, developments in sensing technology, data processing and visualization, and communication of ideas and results, are creating a wide range of new opportunities for public participation in scientific research. This paper reviews the state of citizen science in a hydrological context and explores the potential of citizen science to complement more traditional ways of scientific data collection and knowledge generation for hydrological sciences and water resources management. Although hydrological data collection often involves advanced technology, the advent of robust, cheap, and low-maintenance sensing equipment provides unprecedented opportunities for data collection in a citizen science context. These data have a significant potential to create new hydrological knowledge, especially in relation to the characterization of process heterogeneity, remote regions, and human impacts on the water cycle. However, the nature and quality of data collected in citizen science experiments is potentially very different from those of traditional monitoring networks. This poses challenges in terms of their processing, interpretation, and use, especially with regard to assimilation of traditional knowledge, the quantification of uncertainties, and their role in decision support. It also requires care in designing citizen science projects such that the generated data complement optimally other available knowledge. Lastly, using 4 case studies from remote mountain regions we reflect on the challenges and opportunities in the integration of hydrologically-oriented citizen science in water resources management, the role of scientific knowledge in the decision-making process, and the potential contestation to established community institutions posed by co-generation of new knowledge.

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Section: Communication Of Results Scenario Building and Participatomentioning

confidence: 99%

Section: Communication Of Results Scenario Building and Participatomentioning

confidence: 99%

Citizen science in hydrology and water resources: opportunities for knowledge generation, ecosystem service management, and sustainable development

et al. 2014

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“…Some have shown successful use of bayesian networks as a tool of public participatory modelling for management purpose [Henriksen et al 2007]. But the authors insist on the need to adequately train the stakeholders (regarding probability theory) which is deemed unnecessary with our method.…”

Section: Related Workmentioning

confidence: 99%

Inference reasoning on fishers' knowledge using Bayesian causal maps

Beaufort

Sedki

Fontenelle

2015

Ecological Informatics

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Scientists and managers are not the only holders of knowledge regarding environmental issues: other stakeholders such as farmers or fishers do have empirical and relevant knowledge. Thus, new approaches for knowledge representation in the case of multiple knowledge sources, but still enabling reasoning, are needed. Cognitive maps and Bayesian networks constitute some useful formalisms to address knowledge representations. Cognitive maps are powerful graphical models for knowledge gathering or displaying. If they offer an easy means to express individuals judgments, drawing inferences in cognitive maps remains a difficult task. Bayesian networks are widely used for decision making processes that face uncertain information or diagnosis. But they are difficult to elicitate. To take advantage of each formalism and to overcome their drawbacks, Bayesian causal maps have been developed. In this approach, cognitive maps are used to build the network and obtain conditional probability tables. We propose here a complete framework applied on a real problem. From the different views of a group of shellfish dredgers about their activity, we derive a decision facilitating tool, enabling scenarios testing for fisheries management.qualitative modelling, cognitive maps, bayesian networks, fisher's knowledge, fisheries management, qualitative decision support 1

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“…through Baysian Network modelling for participatory processes (e.g. Henriksen et al 2006;Bromley 2005;.…”

Section: Prepare Strategy and Action Planmentioning

confidence: 99%

Identification of Major Sources of Uncertainty in Current IWRM Practice. Illustrated for the Rhine Basin

Keur

Henriksen

Refsgaard

et al. 2008

Water Resour Manage

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Integrated Water Resources Management (IWRM) can be viewed as a complex process in which the effect of adopted water management measures must be monitored and adjusted in an iterative way as new information and technology gradually become available under changing and uncertain external impacts, such as climate change. This paper identifies and characterises uncertainty as it occurs in the different stages of the IWRM process with respect to sources, nature and type of uncertainty. The present study develops a common terminology that honour the most important aspects from natural and social sciences and its application to the entire IWRM process. The proposed framework is useful by acknowledging a broad range of uncertainties regarding data, models, multiple frames and context. Relating this framework to the different steps of the IWRM cycle is helpful to determine the strategies to better handle and manage uncertainties. Finally, this general framework is illustrated for a case study in the transboundary Rhine river basin.

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Public participation modelling using Bayesian networks in management of groundwater contamination

Cited by 145 publications

References 8 publications

Citizen science in hydrology and water resources: opportunities for knowledge generation, ecosystem service management, and sustainable development

Citizen science in hydrology and water resources: opportunities for knowledge generation, ecosystem service management, and sustainable development

Inference reasoning on fishers' knowledge using Bayesian causal maps

Identification of Major Sources of Uncertainty in Current IWRM Practice. Illustrated for the Rhine Basin

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