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Uncertainty analysis methods
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Improvement of Dredged Material Management Decisions by Uncertainty Analysis (Cont.)
d. Interval Analysis, Fuzzy Arithmetic, or Dependency Bounds Analysis
(Ferson and Burgman 1995; Ferson and Kuhn 1992). When insufficient
information is available to conduct probabilistic analysis, these types of
analysis might be appropriate. These methods explicitly acknowledge
uncertainty but do not require the analyst to define distributions to input
parameters.
e. Expert elicitation (Cooke 1991; National Council on Radiation
Protection and Measurements (NCRP) 1996; Graham, Hawkins, and
Roberts 1988; Evans et al. 1994) is also used in conjunction with these
methods, where quantitative or qualitative information needed to assess
risk is not available in the published literature.
f. Bayesian statistical procedures (Box and Tiao 1973; Gelman et al. 1995;
Morgan and Henrion 1990) are based on the Bayesian, or subjective,
view that probabilities can be estimated using scientific knowledge,
expert judgment, experience, and intuition combined with new data.
The preliminary ranking of uncertainty sources in this report relies primarily
on expert judgment. However, this ranking will be used to support
recommendations for future, more detailed assessments of uncertainty in dredged
material management. It is important to recognize that these methods can
introduce new sources of uncertainty. For example, probabilistic analysis
depends on the analyst identifying the appropriate shape of input distributions
where there may be few data to support this selection.
Improvement of Dredged Material Management
Decisions by Uncertainty Analysis
Evaluation of dredged material impacts on the environment depends on many
input parameters and models that are subject to a great deal of uncertainty. How
this uncertainty is treated quantitatively can lead to overestimates or
underestimates of risk. The tiered approach for dredged material disposal
decisions is designed to err on the side of conservatism to account for
uncertainty. However, the approach does not explicitly address uncertainty
quantitatively, resulting in predictions with unknown levels of conservatism built
into them. The degree of conservatism varies from assessment to assessment,
depending on the number of and mathematical relationship among input
parameters (Ferson and Long 1995). Uncertainties must be acknowledged and
quantified as much as possible to understand how confident one can be about a
disposal decision.
Distinguishing sources of uncertainty and variability can improve predictions
of potential adverse impacts. Uncertainty refers to a lack of knowledge, while
variability refers to natural heterogeneity. Nature is replete with variability. The
composition and abundance of populations can vary with climatic events or
seasonal changes. Individuals within populations might vary in their response to
chemical stressors in dredged material. For example, amphipods are often used as
test species in whole-sediment bioassays, but a very different result might be
4
Chapter 1 Introduction

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