Elements and framework of the uncertainty analysis presented in this paper can be applied to other instruments that estimate stream discharge using a section-by-section method.
Within a general framework of managing uncertainty, balancing stability and flexibility in work processes and matching control and accountability for these processes are proposed as criteria for decisions on reducing, maintaining, and increasing uncertainty.
In the simpler framework of drift uncertainty only, there exists a worst-case measure (bar {P}in mathcal {R}) such that (pi ^{u}_{cdot }(cdot)=pi ^{u,bar {P}}_{cdot }(cdot)) holds and the good-deal hedging strategy (bar {phi }^{bar {P}}) in the model (bar {P}) is robust w.r.t.
This paper aims to address this challenge by presenting a stochastic geological modeling framework for uncertainty quantification of stratigraphic profiles using sparsely located observation information from geotechnical site investigations.
This framework for categorisation of uncertainty incorporates energy system modelling, decision making, and subsequent planning processes: (1) linguistic uncertainty, (2) knowledge or epistemic uncertainty, (3) variability or aleatory uncertainty, (4) decision uncertainty, (5) planning procedural uncertainty, and (6) level of uncertainty.
The effects of nuclear data uncertainties are studied on a typical PWR fuel assembly model in the framework of the OECD Nuclear Energy Agency UAM (Uncertainty Analysis in Modeling) expert working group.
For each source of discrepancy a correction factor, treated as a random variable, is assigned and guidelines for the quantification of the statistical parameters of these correction factors are presented within the framework of the proposed uncertainty analysis model.
Aside from its computational efficiency, the analytical features of the framework allows the assessment of uncertainty arising from the hydrogeological parameters.
