Methods for Uncertainty Analysis: A Comparative Survey

Cox, D. C.; Baybutt, Paul

doi:10.1111/j.1539-6924.1981.tb01425.x

Cited by 113 publications

(41 citation statements)

References 11 publications

(1 reference statement)

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“…There are several methods commonly used for such analyses (as reviewed variously in Cox and Baybutt, 1981;Beck, 1987;Iman and Helton, 1988;and Zimmerman et al, 1990): (i) a firstorder error analysis (also referred to as a differential analysis or a small perturbation analysis); (ii) Monte Carlo simulation, possibly with a more efficient sampling scheme; and (iii) methods of response surface analysis. It may in addition be of interest to establish which, among the various elements of the above sources of uncertainty, contributes most to the resulting uncertainty of the model's predictions.…”

Section: Sensitivity (Astm E 978 -84)mentioning

confidence: 99%

On the problem of model validation for predictive exposure assessments

Beck

Ravetz

Mulkey

et al. 1997

Stochastic Hydrol Hydraul

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The development and use of models for predicting exposures are increasingly common and are essential for many risk assessments of the United States Environmental Protection Agency (EPA). Exposure assessments conducted by the EPA to assist regulatory or policy decisions are often challenged to demonstrate their "scientific validity". Model validation has thus inevitably become a major concern of both EPA officiMs and the regulated community, sufficiently so that the EPA's Risk Assessment Forum is considering guidance for model validation. The present paper seeks to codify the issues and extensive foregoing discussion of validation with special reference to the development and use of models for predicting the impact of novel chemicals on the environment. Its preparation has been part of the process in formulating a White Paper for the EPA's Risk Assessment Forum. Its subject matter has been drawn from a variety of fields, including ecosystem analysis, surface water quality management, the contamination of groundwaters from high-level nuclear waste, and the control of air quality. The philosophical and conceptual bases of model validation are reviewed, from which it is apparent that validation should be understood as a task of product (or tool) design, for which some form of protocol for quality assurance will ultimately be needed. The commonly used procedures and methods of model validation are also reviewed, including the analysis of uncertainty. Following a survey of pe~st attempts at resolving the issue of modeI validation, we close by introducing the notion of a model having maximum relevance to the performance of a specific task, such as, for example, a predictive exposure assessment.Key words. Model validation; analysis of uncertainty; model verification; quality assurance; system identification; model calibration. I n t r o d u c t i o nThe construction and use of m a t h e m a t i c a l models are essential in predicting the possible consequences of releasing chemicals, some of which m a y be quite novel, into new environments. Substantial costs, and substantial damages to the environment, m a y attach to the regulatory decisions that are informed and thus guided by the predictions derived from a model. T h e risk of making a wrong decision will be strongly dependent on the reliability of these predictions, in just the same way as it

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Section: Sensitivity (Astm E 978 -84)mentioning

confidence: 99%

On the problem of model validation for predictive exposure assessments

Beck

Ravetz

Mulkey

et al. 1997

Stochastic Hydrol Hydraul

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“…Cox & Baybutt 1981). Values of the life history parameters were randomly selected from their associated distributions and incorporated into the Lotka equation, and a growth rate specific to that set of parameters was determined.…”

Section: Methodsmentioning

confidence: 99%

Population abundance and growth rate of western gray whales Eschrichtius robustus

Bradford

Weller²,

Wade³

et al. 2008

Endang. Species Res.

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The western population of gray whales Eschrichtius robustus is one of the most endangered whale populations in the world. Recent studies of this population off the northeastern coast of Sakhalin Island, Russia, have produced a photographic dataset that was utilized for the first markrecapture assessment of western gray whale abundance. Given encounter histories of 129 individually identified whales spanning 25 monthly capture occasions from 1997 to 2003, a closed capture estimator was employed to estimate the number of individuals using the study area in each year. Temporary emigration probabilities were then applied to the closed capture estimates to enumerate the total population size of whales off northeastern Sakhalin Island. Total abundances from 1997 to 2003 were estimated as 64 ± 5.1 (SE), 55 to 75 (95% CI); 75 ± 4.9, 66 to 85; 86 ± 3.1, 80 to 93; 77 ± 4.7, 68 to 87; 91 ± 3.4, 84 to 98; 98 ± 4.1, 90 to 106; and 99 ± 4.9, 90 to 109, respectively. These abundance estimates, particularly the last values in the series, most likely approximate the size of the entire western gray whale population. For comparison to the trend in the abundance estimates, life history data were used to estimate the growth rate of the population. Depending on the range of potential fecundity values incorporated, the resulting growth rate estimates indicate an annual population increase that is between 2.5 and 3.2%. The extremely small population size and slow rate of increase documented here further highlight concern about the viability of this critically endangered population.

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“…From the estimation results, we can perform an uncertainty analysis: what is the prediction uncertainty on the model outputs regarding the uncertainty of the inputs [19], [65]. Such analysis allows to study the predictive ability of the model: from the covariance matrix of parameter estimates obtained from the parametric bootstrap, we simulate the propagation of the uncertainty in the dynamic system.…”

Section: Uncertainty Analysismentioning

confidence: 99%

Development and Evaluation of Plant Growth Models: Methodology and Implementation in the PYGMALION platform

Cournède

Chen

et al. 2013

Math. Model. Nat. Phenom.

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Abstract. Mathematical models of plant growth are generally characterized by a large number of interacting processes, a large number of model parameters and costly experimental data acquisition. Such complexities make model parameterization a difficult process. Moreover, there is a large variety of models that coexist in the literature with generally an absence of benchmarking between the different approaches and insufficient model evaluation. In this context, this paper aims at enhancing good modelling practices in the plant growth modeling community and at increasing model design efficiency. It gives an overview of the different steps in modelling and specify them in the case of plant growth models specifically regarding their above mentioned characteristics.Different methods allowing to perform these steps are implemented in a dedicated platform PYGMALION (Plant Growth Model Analysis, Identification and Optimization). Some of these methods are original. The C++ platform proposes a framework in which stochastic or deterministic discrete dynamic models can be implemented, and several efficient methods for sensitivity analysis, uncertainty analysis, parameter estimation, model selection or data assimilation can be used for model design, evaluation or application.Finally, a new model, the LNAS model for sugar beet growth, is presented and serves to illustrate how the different methods in PYGMALION can be used for its parameterization, its evaluation and its application to yield prediction. The model is evaluated from real data and is shown to have interesting predictive capacities when coupled with data assimilation techniques.

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Methods for Uncertainty Analysis: A Comparative Survey

Cited by 113 publications

References 11 publications

On the problem of model validation for predictive exposure assessments

On the problem of model validation for predictive exposure assessments

Population abundance and growth rate of western gray whales Eschrichtius robustus

Development and Evaluation of Plant Growth Models: Methodology and Implementation in the PYGMALION platform

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