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Results for “Uncertainty modelling”

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PubMed2002

Evaluating and expressing the propagation of uncertainty in chemical fate and bioaccumulation models.

First-order analytical sensitivity and uncertainty analysis for environmental chemical fate models is described and applied to a regional contaminant fate model and a food web bioaccumulation model. By assuming linear relationships between inputs and outputs, independence, and log-normal distributions of input variables, a relationship between uncertainty in input parameters and uncertainty in output parameters can be derived, yielding results that are consistent with a Monte Carlo analysis with similar input assumptions. A graphical technique is devised for interpreting and communicating uncertainty propagation as a function of variance in input parameters and model sensitivity. The suggested approach is less calculationally intensive than Monte Carlo analysis and is appropriate for preliminary assessment of uncertainty when models are applied to generic environments or to large geographic areas or when detailed parameterization of input uncertainties is unwarranted or impossible. This approach is particularly useful as a starting point for identification of sensitive model inputs at the early stages of applying a generic contaminant fate model to a specific environmental scenario, as a tool to support refinements of the model and the uncertainty analysis for site-specific scenarios, or for examining defined end points. The analysis identifies those input parameters that contribute significantly to uncertainty in outputs, enabling attention to be focused on defining median values and more appropriate distributions to describe these variables.

Environmental Pollutants↗

PubMed2000

Handling uncertainty in cost-effectiveness models.

The use of modelling in economic evaluation is widespread, and it most often involves synthesising data from a number of sources. However, even when economic evaluations are conducted alongside clinical trials, some form of modelling is usually essential. The aim of this article is to review the handling of uncertainty in the cost-effectiveness results that are generated by the use of decision-analytic-type modelling. The modelling process is split into a number of stages: (i) a set of methods to be employed in a study are defined, which should include a 'reference case' of agreed methods to enhance the comparability of results; (ii) the clinical and demographic characteristics of the patients the model relates to should be specified as carefully as in any experimental study; and (iii) the data requirements of the model should be estimated using the principles of Bayesian statistics, such that prior distributions are specified for unknown model parameters. Monte Carlo simulation can then be employed to sample from these prior distributions to obtain a distribution of the cost effectiveness of the intervention. Such probabilistic analyses are related to parameter uncertainty. In addition, modelling uncertainty is likely to add a further layer of uncertainty to the results of particular analyses.

Cost-Benefit Analysis↗

PubMed2001

Reliability of the ICRP's dose coefficients for members of the public. 1. Sources of uncertainty in the biokinetic models.

During the decade following the Chernobyl accident, the International Commission on Radiological Protection (ICRP) developed dose coefficients (doses per unit intake) for ingestion or inhalation of radionuclides by members of the public. The level of uncertainty in those coefficients varies considerably from one radionuclide to another, due largely to differences in the level of understanding of the biological behaviour of different elements in the human body. This paper is the first in a series that examines the sources and extent of uncertainties in the ICRP's biokinetic and dosimetric models for members of the public and the dose coefficients derived from those models. The present paper describes the different types of information generally used to develop biokinetic models for radionuclides, the main sources of uncertainty associated with each type of information, and the approach used in subsequent papers in this series to quantify the uncertainties in biokinetic and dosimetric estimates.

Age Factors↗

PubMed2004

Online monitoring by dynamically refining imprecise models.

Model-based monitoring determines faults in a supervised system by comparing the available system's measurements with a priori information represented by the system's mathematical model. Especially in technical environments, a monitoring system must be able to reason with incomplete knowledge about the supervised system, to process noisy and erroneous observations and to react within a limited time. We present MOSES, a model-based monitoring system which is based on imprecise models where the structure is known and the parameters may be imprecisely specified by numerical intervals. As a consequence, only bounds on the trajectories can be derived with imprecise models. These bounds are computed using traditional numerical integration techniques starting from individual points on the external surface of the model's uncertainty space. When new measurements from the supervised system become available, MOSES checks the consistency of this new information with the model's prediction and refutes inconsistent parts from the uncertainty space of the model. A fault in the supervised system is detected when the complete model's uncertainty space has been refuted. MOSES bridges and extends methodologies from the FDI and DX communities by refining the model's uncertainty space conservatively through refutation, by applying standard numerical techniques for deriving the trajectories of imprecise models and by exploiting the measurements as soon as possible for online monitoring. The performance of MOSES is evaluated based on examples and by online monitoring a complex heating system.

Algorithms↗

PubMed2000

Experimental manipulation of intolerance of uncertainty: a study of a theoretical model of worry.

Intolerance of uncertainty has been identified as an important variable related to worry and Generalized Anxiety Disorder (GAD) [Dugas, M. J., Gagnon, F., Ladouceur, R., & Freeston, M. H. (1998). Generalized anxiety disorder: a preliminary test of a conceptual model. Behaviour Research and Therapy, 36, 215-226; Ladouceur, R., Dugas, M. J., Freeston, M. H., Rhéaume, J., Blais, F., Boisvert, J.-M., Gagnon, F., & Thibodeau, N. (1999). Specificity of Generalized Anxiety Disorder symptoms and processes. Behavior Therapy, 30, 197-207]. The goal of the present study was to clarify the relationship between this cognitive process and worry by experimentally manipulating intolerance of uncertainty. A gambling procedure was used to increase intolerance of uncertainty in one group (N = 21) and to decrease intolerance of uncertainty in another group (N = 21). The results indicate that participants whose level of intolerance of uncertainty was increased showed a higher level of worry, compared to participants whose level of intolerance of uncertainty was decreased. These results provide some initial clarifications as to the causal nature of the link between intolerance of uncertainty and worry. These results are coherent with our theoretical model of worry and GAD (Dugas et al., 1998), which stipulates that intolerance of uncertainty plays a key role in the acquisition and maintenance of excessive worry.

Adolescent↗

PubMed2006

Testing a model of symptoms, communication, uncertainty, and well-being, in older breast cancer survivors.

Among older, long-term breast cancer survivors, symptoms from previous treatment can generate uncertainty about whether they represent co-morbid conditions, recurrence, or normal aging. This uncertainty can result in emotional distress and thoughts of recurrence. Communication with health care providers may help women reduce uncertainty and improve both emotional and cognitive well-being. To assess the influence of symptoms, uncertainty, and communication with providers on well-being, data from 203 Caucasian and African American survivors, 5-9 years post treatment, were tested using structural equation modeling. Symptoms, age, and uncertainty had the strongest influence on well-being, regardless of race. There was an unexpected positive association between patient-provider communication and thoughts of recurrence. Descriptive analysis revealed that 52% of women were unable to achieve their desired decision-making role with health care providers.

Affect↗

PubMed2006

Parameter sensitivity and uncertainty of the forest carbon flux model FORUG: a Monte Carlo analysis.

The Monte Carlo technique can be used to propagate input variable uncertainty and parameter uncertainty through a model to determine output uncertainty. However, to carry out Monte Carlo simulations, the uncertainty distributions or the probability density functions (PDFs) of the model parameters and input variables must be known. This remains one of the bottlenecks in current uncertainty research in forest carbon flux modeling. Because forest carbon flux models involve many parameters, we questioned whether it is necessary to take into account all parameters in the uncertainty analysis. A sensitivity analysis can determine the parameters contributing most to the overall model output uncertainty. This paper illustrates the usefulness of the Monte Carlo simulation technique for ranking parameters for sensitivity and uncertainty in process-based forest flux models. The uncertainty of the output (net ecosystem exchange, NEE) of the FORUG model was estimated for the Hesse beech forest (1997). Based on the arbitrary uncertainty of ten key parameters, a standard deviation of 0.88 Mg C ha(-1) year(-1) NEE was found which is equal to 24% of the mean value of NEE. Sensitivity analysis showed that the overall output uncertainty of the FORUG model can largely be determined by accounting for the uncertainty of only a few key parameters. The results led to the identification of the key FORUG parameters and to the recommendation for a process-based description of the soil respiration process in the FORUG model.

Carbon↗

PubMed2006

Finite-element modeling of bones from CT data: sensitivity to geometry and material uncertainties.

The aim of this paper is to analyze how the uncertainties in modelling the geometry and the material properties of a human bone affect the predictions of a finite-element model derived from computed tomography (CT) data. A sensitivity analysis, based on a Monte Carlo method, was performed using three femur models generated from in vivo CT datasets, each subjected to two different loading conditions. The geometry, the density and the mechanical properties of the bone tissue were considered as random input variables. Finite-element results typically used in biomechanics research were considered as statistical output variables, and their sensitivity to the inputs variability assessed. The results showed that it is not possible to define a priori the influence of the errors related to the geometry definition process and to the material assignment process on the finite-element analysis results. The errors in the geometric representation of the bone are always the dominant variables for the stresses, as was expected. However, for all the variables, the results seemed to be dependent on the loading condition and to vary from subject to subject. The most interesting result is, however, that using the proposed method to build a finite-element model of a femur from a CT dataset of the quality typically achievable in the clinical practice, the coefficients of variation of the output variables never exceed the 9%. The presented method is hence robust enough to be used for investigating the mechanical behavior of bones with subject-specific finite-element models derived from CT data taken in vivo.

Computer Simulation↗

PubMed2005

Uncertainty in predictions of the climate response to rising levels of greenhouse gases.

The range of possibilities for future climate evolution needs to be taken into account when planning climate change mitigation and adaptation strategies. This requires ensembles of multi-decadal simulations to assess both chaotic climate variability and model response uncertainty. Statistical estimates of model response uncertainty, based on observations of recent climate change, admit climate sensitivities--defined as the equilibrium response of global mean temperature to doubling levels of atmospheric carbon dioxide--substantially greater than 5 K. But such strong responses are not used in ranges for future climate change because they have not been seen in general circulation models. Here we present results from the 'climateprediction.net' experiment, the first multi-thousand-member grand ensemble of simulations using a general circulation model and thereby explicitly resolving regional details. We find model versions as realistic as other state-of-the-art climate models but with climate sensitivities ranging from less than 2 K to more than 11 K. Models with such extreme sensitivities are critical for the study of the full range of possible responses of the climate system to rising greenhouse gas levels, and for assessing the risks associated with specific targets for stabilizing these levels.

Journal Article↗

PubMed2006

Methods to quantify and identify the sources of uncertainty for river basin water quality models.

Worldwide, the application of river basin water quality models is increasing, often imposed by law. It is, thus, important to know the degree of uncertainty associated with these models and their application to a specific watershed. These uncertainties lead to errors that are revealed when model outputs are compared to observations. Such uncertainty is typically described by calculating the residuals. However, residuals should not be seen as an estimate of total uncertainty, since through the calibration process, the residuals may be reduced by over-adjustment to the data, which is typically the case for over-parameterised models. Over-adjustment during a calibration period can also lead to highly biased results when the model is applied to other periods or environmental conditions. The total model uncertainties are, therefore, assessed by four components: the sum of the squares of the residuals (SSQ), parameter uncertainties (that can be ignored when their error is much smaller than SSQ), input data uncertainties, and an additional predictive uncertainty that is expressed when the model appears to be biased when it is applied for data other than the data used for calibration. The sources are ranked according to a quantification criterion (magnitude) as well as an identification criterion that depends on the number of observations that are covered by the confidence region. This approach is illustrated with SWAT2003 simulations for flow and sediment of Honey Creek, a tributary of the Sandusky River basin (Ohio). The results show the dominance of the model uncertainty. The input data uncertainty is less important.

Calibration↗

PubMed2006

Estimation of the false negative fraction of a diagnostic kit through Bayesian regression model averaging.

In modelling we usually endeavour to find a single 'best' model that explains the relationship between independent and dependent variables. Selection of a single model fails to take into account the prior uncertainty in the model space. The Bayesian model averaging (BMA) approach tackles this problem by considering the set of all possible models. We apply BMA approach to the estimation of the false negative fraction (FNF) in a particular case of a two-stage multiple screening test for bowel cancer. We find that after taking model uncertainty into consideration the estimate of the FNF obtained is largely dependent on the covariance structure of the priors. Results obtained when the Zellner g-prior for the prior variance is used is largely influenced by the magnitude of g.

Bayes Theorem↗

PubMed2006

Pencil kernel correction and residual error estimation for quality-index-based dose calculations.

Experimental data from 593 photon beams were used to quantify the errors in dose calculations using a previously published pencil kernel model. A correction of the kernel was derived in order to remove the observed systematic errors. The remaining residual error for individual beams was modelled through uncertainty associated with the kernel model. The methods were tested against an independent set of measurements. No significant systematic error was observed in the calculations using the derived correction of the kernel and the remaining random errors were found to be adequately predicted by the proposed method.

Biophysical Phenomena↗

PubMed2004

A modified ICRP 66 iodine gas uptake model and its parametric uncertainty.

Intakes via inhalation may occur from radionuclides released in the form of a gas. The chemical characteristics pertaining to the release influence the intake and subsequent dose to an exposed individual. Gases are taken up or absorbed in the entire respiratory tract and the associated uptake mechanisms are quite different from deposition of particulates. Gaseous iodine can exist in various chemical forms, e.g., elemental iodine, inorganic, and organic iodine compounds. These different chemical species play an integral role in the gaseous uptake o f iodine in t he respiratory tract. Gas uptake in the various regions of the respiratory tract results in the intake of iodinated material into the body. The radioactive iodine taken up in the gas-exchange tissues is absorbed into the bloodstream of an individual and subsequently transferred to other organs. Iodine in the circulatory system can then be taken up by the thyroid gland, with resulting dose to the thyroid. The magnitude and uncertainty in regional gas uptake is important in the assessment of individuals exposed to airborne releases of radioiodine. The current ICRP 66 model is rudimentary and estimates regional gas uptake based on solubility and reactivity of the different radionuclides entering the respiratory tract. The modified model proposed here employs methodology and a mathematical structure to determine estimates of fractional gas uptake rather than defaulting to literature values, as in the current ICRP model. Model parameters have been assigned input distributions and estimates of uncertainty have been determined. A sensitivity analysis of these parameters has been performed to demonstrate the importance of each of these parameters. The sensitivity analysis ranks the model-input parameters by their importance to estimates of regional gas uptake. The model developed herein may be used for improved estimation of gas uptake in the respiratory tract and subsequent dose estimates from the different chemical forms of radioiodine.

Adult↗

PubMed2002

The Gaussian atmospheric transport model and its sensitivity to the joint frequency distribution and parametric variability.

Reconstructed meteorological data are often used in some form of long-term wind trajectory models for estimating the historical impacts of atmospheric emissions. Meteorological data for the straight-line Gaussian plume model are put into a joint frequency distribution, a three-dimensional array describing atmospheric wind direction, speed, and stability. Methods using the Gaussian model and joint frequency distribution inputs provide reasonable estimates of downwind concentration and have been shown to be accurate to within a factor of four. We have used multiple joint frequency distributions and probabilistic techniques to assess the Gaussian plume model and determine concentration-estimate uncertainty and model sensitivity. We examine the straight-line Gaussian model while calculating both sector-averaged and annual-averaged relative concentrations at various downwind distances. The sector-average concentration model was found to be most sensitive to wind speed, followed by horizontal dispersion (sigmaZ), the importance of which increases as stability increases. The Gaussian model is not sensitive to stack height uncertainty. Precision of the frequency data appears to be most important to meteorological inputs when calculations are made for near-field receptors, increasing as stack height increases.

Air Pollutants, Radioactive↗

PubMed2004

Health-related quality of life parameters as prognostic factors in a nonmetastatic breast cancer population: an international multicenter study.

PURPOSE: The purpose of this research was to evaluate whether baseline health-related quality of life (HRQOL) parameters are prognostic factors for survival in locally advanced breast cancer patients. Although the literature highlights the important role of HRQOL parameters in predicting survival in advanced metastatic disease, little evidence exists for earlier stages. PATIENTS AND METHODS: The overall sample consisted of 448 patients randomly assigned to receive cyclophosphamide, epirubicin, and fluorouracil versus epirubicin, cyclophosphamide, and granulocyte colony-stimulating factor. Patients were enrolled in 12 countries. HRQOL baseline scores were assessed using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire C30. The Cox proportional hazards regression model was used for both univariate and multivariate analyses of survival. In addition, a bootstrap resampling technique was used to assess the stability of the outcomes. Bootstrap results were then applied for model averaging purposes as a means to account for the observed model selection uncertainty. RESULTS: The final multivariate model retained inflammatory breast cancer (T4d) as the only factor predicting overall survival (OS) with a hazard ratio of 1.375 (95% CI, 1.027 to 1.840; P =.03). The presence of inflammatory breast cancer lowers the median survival time from 6.6 to 4.2 years (36% reduction). None of the preselected HRQOL variables were prognostic for OS or disease-free survival, in either the univariate or multivariate analysis. CONCLUSION: Our findings suggest that baseline HRQOL parameters have no prognostic value in a nonmetastatic breast cancer population.

Adult↗

PubMed2006

A random search methodology for examining parametric uncertainty in water quality models.

The advent of the modern high-speed digital computer has tremendously enhanced the utility of Monte Carlo methods for evaluating complex environmental simulation models. In particular, random searching is becoming popular, as thousands of model runs can now be executed quickly and with minimal effort. Indeed, the issues of computational burden and inefficiency, hitherto the bane of random searching, are now receding. This paper presents one such method, uniform covering by probabilistic rejection (UCPR), which combines a pure random search with a probabilistic rejection algorithm that significantly enhances its efficiency. Using nearest-neighbor distances, an ensemble of points in a predefined parameter sampling domain migrates to locate and define a final distribution of optimal parameter vectors, thus providing a realistic depiction of parameter uncertainty. In a prototypical case study of the Oconee River (Georgia, USA), UCPR and regionalized sensitivity analysis, are employed for identifying the parameters of sediment-transport-associated nutrient dynamics, a dynamic river water quality model. Results indicate the existence of a complex interactive parameter structure, evidenced by multiple sets of optimal points widely dispersed over a broad domain of feasible parameter values.

Algorithms↗

PubMedDate not supplied

Assessment of uncertainty and risk in modeling regional heavy-metal accumulation in agricultural soils.

Present agricultural land use and atmospheric deposition may lead to heavy-metal accumulation rates in soils that may violate soil quality standards in the future. To undertake suitable preventive measures against heavy-metal enrichment, flux balances in agroecosystems and their uncertainties have to be assessed. For this reason we developed an empirical stochastic model, PROTERRA-S, that considers heavy-metal inputs through agricultural management as well as outputs by crop removal and leaching on a regional scale. In this manuscript we describe application of PROTERRA-S to the Sundgau region in Switzerland. Considering uncertainty in informational and natural variability, large variations of the aggregated regional cadmium and zinc balances were found, with standard deviations that were of the same order of magnitude as their average values. Uncertainty in the simulated net zinc flux originated mainly from uncertainty in the zinc concentrations of manure and crops and from uncertainty in atmospheric deposition of zinc. For cadmium, the main contribution to the total uncertainty came from uncertainty in crop concentration, regression functions to estimate Freundlich parameters, atmospheric deposition, and from spatial variation of soil pH and cation exchange capacity (CEC). For both zinc and cadmium, informational uncertainty in input data were large, indicating that significant uncertainty reduction could be achieved by additional data collection campaigns. A monetary risk value for the regional zinc accumulation rate in Sundgau was calculated to be on the order of 22 million Euro.

Agriculture↗

PubMed2004

Uncertainty analysis of parameters for modeling the transfer and fate of benzo(a)pyrene in Tianjin wastewater irrigated areas.

A Monte Carlo simulation for uncertainty analysis of three key parameters (local coal consumption rate Q(1L), dry deposition velocity of aerosol particulate Kp and biodegradation rate of benzo(a)pyrene in soil and sediment K(R3)) was conducted in this study. Results of the simulation indicate that the three parameters were influenced by uncertainty and that all equilibrium concentrations in the four bulk compartments and various sub-compartments were log-normally distributed. However, the results also indicated that among the six primary transfer fluxes, erosion associated with solids in soil and deposition associated with solids in water, along with output from sewers were also log-normally distributed, while deposition from air to soil and biodegradation in soil and sediment followed normal distributions. The effect of uncertainty on the model results of the three key parameters was derived using a comparison of upper and lower of confidence interval boundaries at the 95% level of confidence. The results reveal that uncertainty in the key parameters had a more significant influence on equilibrium concentrations of the chemical in the bulk compartments of soil and sediment than on concentrations in the other two bulk compartments, various sub-compartments and the six predominant transfer fluxes.

Benzo(a)pyrene↗