A Bayesian approach for incorporating uncertainty and data worth in environmental projects

Abbaspour, Karim C.; Schulin, Rainer; Schläppi, E.; Flühler, H.

doi:10.1007/bf01874902

Cited by 26 publications

(14 citation statements)

References 17 publications

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“…Similar options for data collection and additional scientific study are available in virtually all environmental and engineering domains where uncertainties in key model assumptions, formulations, inputs and resulting predictions prevail (e.g., Morgan and Henrion, 1990;Patwardhan and Small, 1992;Smith and French, 1993;Brand and Small, 1995;Abbaspour et al, 1996;Chao and Hobbs, 1997;Casman et al, 1999). In general, the uncertainty variance of a variable is a priori expected to decrease as more data are collected.…”

Section: Value Of Information (Voi) Analysismentioning

confidence: 99%

Bayesian Networks for Modeling Dredging Decisions

Schultz¹,

Borrowman²,

Small³

2011

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Section: Value Of Information (Voi) Analysismentioning

confidence: 99%

Bayesian Networks for Modeling Dredging Decisions

Schultz¹,

Borrowman²,

Small³

2011

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“…Adaptation is akin to Bayesian methods that, in this context, translate new information acquired through "experiment" and experience with emissions controls to a revised risk assessment (Abbaspour et al 1999). For air quality, this means measuring actual emissions and air quality to determine if reductions are being achieve and having desired effects (Demerjian et al 1995).…”

Section: Periodic Iterative Adaptive Reviewmentioning

confidence: 99%

The ends of uncertainty: Air quality science and planning in Central California

Fine

2003

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Air quality planning in Central California is complicated and controversial despite millions of dollars invested to improve scientific understanding. This research describes and critiques the use of photochemical air quality simulation modeling studies in planning to attain standards for ground-level ozone in the San Francisco Bay Area and the San Joaquin Valley during the 1990's. Data are gathered through documents and interviews with planners, modelers, and policy-makers at public agencies and with representatives from the regulated and environmental communities. Interactions amongst organizations are diagramed to identify significant nodes of interaction. Dominant policy coalitions are described through narratives distinguished by their uses of and responses to uncertainty, their exposures to risks, and their responses to the principles of conservatism, civil duty, and caution. Policy narratives are delineated using aggregated respondent statements to describe and understand advocacy coalitions.I found thar models impacted the planning process significantly, but were used not purely for their scientific capabilities. Modeling results provided justification for decisions based on other constraints and political considerations. Uncertainties were utilized opportunistically by stakeholders instead of managed explicitly. Ultimately, the process supported the partisan views of those in control of the modeling. Based on these findings, as well as a review of model uncertainty analysis capabilities, I recommend modifying the planning process to allow for the development and incorporation of uncertainty information, while addressing the need for inclusive and meaningful public participation.By documenting an actual air quality planning process these findings provide insights about the potential for using new scientific information and understanding to achieve environmental goals, most notably the analysis of uncertainties in modeling applications. Concurrently, needed uncertainty information is identified and capabilities to produce it are assessed. Practices to facilitate incorporation of uncertainty information are suggested based on research findings, as well as theory from the literatures of the policy sciences, decision sciences, science and technology studies, consensus-based and communicative planning, and modeling.

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“…Comprehensive algorithms are needed to link the above seemingly different issues and considerations. An algorithm developed by Abbaspour et al [1996], the Bayesian uncertainty development algorithm (BUDA), can utilize all of the above techniques to achieve a higher reduction in uncertainty in environmental projects. The inverse procedure in this work is part of BUDA.…”

Section: Introductionmentioning

confidence: 99%

A sequential uncertainty domain inverse procedure for estimating subsurface flow and transport parameters

Abbaspour¹,

Genuchten²,

Schulin³

et al. 1997

Water Resources Research

180

108

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Abstract. A parameter estimation procedure, sequential uncertainty domain parameter fitting (SUFI), is presented and has the following characteristics. The procedure is sequential in nature, meaning that one more iteration can always be made before choosing the final estimates. The procedure has a Bayesian framework, indicating that the method operates within uncertainty domains (prior, posterior) associated with each parameter. The procedure is a fitting procedure, conditioning the unknown parameter estimates on an array of observed values. Finally, the procedure is iterative, requiring a stopping rule which is provided by a critical value of a goal function. Performance of the SUFI parameter estimation procedure is demonstrated using three examples of increasing complexity: (1) analysis of a solute breakthrough curve measured in the laboratory during steady state water flow, (2) estimation of the unsaturated soil hydraulic parameters from a transient drainage experiment carried out in a 6-m deep lysimeter, and (3) estimation of selected flow and transport parameters from a hypothetical ring infiltrometer experiment. The procedure was found to be general, stable, and always convergent.

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A Bayesian approach for incorporating uncertainty and data worth in environmental projects

Cited by 26 publications

References 17 publications

Bayesian Networks for Modeling Dredging Decisions

Bayesian Networks for Modeling Dredging Decisions

The ends of uncertainty: Air quality science and planning in Central California

A sequential uncertainty domain inverse procedure for estimating subsurface flow and transport parameters

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