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At least 253 records · Page 14Linked to original sources

Predicting streamflow response to fire-induced landcover change: implications of parameter uncertainty in the MIKE SHE model.

Fire is a primary agent of landcover transformation in California semi-arid shrubland watersheds, however few studies have examined the impacts of fire and post-fire succession on streamflow dynamics in these basins. While it may seem intuitive that larger fires will have a greater impact on streamflow response than smaller fires in these watersheds, the nature of these relationships has not been determined. The effects of fire size on seasonal and annual streamflow responses were investigated for a medium-sized basin in central California using a modified version of the MIKE SHE model which had been previously calibrated and tested for this watershed using the Generalized Likelihood Uncertainty Estimation methodology. Model simulations were made for two contrasting periods, wet and dry, in order to assess whether fire size effects varied with weather regime. Results indicated that seasonal and annual streamflow response increased nearly linearly with fire size in a given year under both regimes. Annual flow response was generally higher in wetter years for both weather regimes, however a clear trend was confounded by the effect of stand age. These results expand our understanding of the effects of fire size on hydrologic response in chaparral watersheds, but it is important to note that the majority of model predictions were largely indistinguishable from the predictive uncertainty associated with the calibrated model - a key finding that highlights the importance of analyzing hydrologic predictions for altered landcover conditions in the context of model uncertainty. Future work is needed to examine how alternative decisions (e.g., different likelihood measures) may influence GLUE-based MIKE SHE streamflow predictions following different size fires, and how the effect of fire size on streamflow varies with other factors such as fire location.

California↗

Deterministic approach to robust adaptive learning of fuzzy models.

This study is concerned with the adaptive learning of an interpretable Sugeno-type fuzzy inference system, in a deterministic framework, in the presence of data uncertainties and modeling errors. The authors explore the use of Hinfinity estimation theory and least squares estimation for online learning of membership functions and consequent parameters without making any assumption and requiring a priori knowledge of upper bounds, statistics, and distribution of data uncertainties and modeling errors. The issues of data uncertainties, modeling errors, and time variations have been considered mathematically in a sensible way. The proposed robust approach to the adaptive learning of fuzzy models has been illustrated through the examples of adaptive system identification, time-series prediction, and estimation of an uncertain process.

Algorithms↗

Influence of distributional shape of substance parameters on exposure model output.

Uncertainty of environmental concentrations is calculated with the regional multimedia exposure model of EUSES 1.0 by considering probability input distributions for aqueous solubility, vapor pressure, and octanol-water partition coefficient, K(ow). Only reliable experimentally determined data are selected from available literature for eight reference chemicals representing a wide substance property spectrum. Monte Carlo simulations are performed with uniform, triangular, and log-normal input distributions to assess the influence of the choice of input distribution type on the predicted concentration distributions. The impact of input distribution shapes on output variance exceeds the effect on the output mean by one order of magnitude. Both are affected by influence and uncertainty (i.e., variance) of the input variable as well. Distributional shape has no influence when the sensitivity function of the respective parameter is perfectly linear. For nonlinear relationships, overlap of probability mass of input distribution with influential ranges of the parameter space is important. Differences in computed output distribution are greatest when input distributions differ in the most influential parameter range.

Environmental Exposure↗

Uncertainty analysis in QUAL2E model of Zayandeh-Rood River.

Water-quality modeling and prediction is a complicated task because of inherent randomness and uncertainties associated with various processes and variables throughout the stream environment and the lack of appropriate data. Hence, the results of mathematical models are always approximate, lying within an uncertainty. This paper describes and demonstrates the application of the U.S. Environmental Protection Agency's water-quality model, QUAL2E-UNCAS, to the Zayandeh-Rood River in Iran. First-order reliability analysis is used to examine the variability of predicted water-quality parameters of total dissolved solids, dissolved oxygen, and biochemical oxygen demand. This analysis also determines key sources of uncertainty affecting prediction of the water-quality parameters. The results show that reliability analysis can help water-quality modelers and planners to quantify the reliability of the water-quality predictions and to carry out more efficiently planned sampling and data collection programs to reduce model-prediction uncertainty.

Forecasting↗

A two-stage interval-stochastic programming model for waste management under uncertainty.

This study introduces a two-stage interval-stochastic programming (TISP) model for the planning of solid-waste management systems under uncertainty. The model is derived by incorporating the concept of two-stage stochastic programming within an interval-parameter optimization framework. The approach has the advantage that policy determined by the authorities, and uncertain information expressed as intervals and probability distributions, can be effectively communicated into the optimization processes and resulting solutions. In the modeling formulation, penalties are imposed when policies expressed as allowable waste-loading levels are violated. In its solution algorithm, the TISP model is converted into two deterministic submodels, which correspond to the lower and upper bounds for the desired objective-function value. Interval solutions, which are stable in the given decision space with associated levels of system-failure risk, can then be obtained by solving the two submodels sequentially. Two special characteristics of the proposed approach make it unique compared with other optimization techniques that deal with uncertainties. First, the TISP model provides a linkage to predefined policies determined by authorities that have to be respected when a modeling effort is undertaken; second, it furnishes the reflection of uncertainties presented as both probabilities and intervals. The developed model is applied to a hypothetical case study of regional solid-waste management. The results indicate that reasonable solutions have been generated. They provide desired waste-flow patterns with minimized system costs and maximized system feasibility. The solutions present as stable interval solutions with different risk levels in violating the waste-loading criterion and can be used for generating decision alternatives.

Environment↗

Treatment of uncertainty and developing conceptual models for environmental risk assessments and radioactive waste disposal safety cases.

The common approach to performing quantitative risk assessments in the contaminated land industry in the UK lacks a formal methodology for the treatment of the full range of uncertainties and for documenting decisions regarding the development of conceptual models and the selection of computer codes. The approach presented here represents an alternative, more detailed, and systematic approach for developing conceptual models and addressing uncertainties when undertaking contaminated land risk assessments. It is intended that the advantages of this approach are recognised by practitioners in the contaminated land industry and adopted, where appropriate, to help improve the quality of contaminated land risk assessments. The identification of features, events, and processes (FEPs) has been applied to safety assessments of deep geological and near-surface disposal of radioactive wastes. One of the primary benefits of using this approach is in the development of conceptual models. The approach identifies the FEPs that need to be addressed during the development of conceptual models and in the selection of suitable computer codes that can be used to represent the conceptual models. This approach has been applied by BNFL at the low-level radioactive waste disposal site at Drigg in Cumbria and is currently being adopted for a contaminated land study at the Sellafield site, also in Cumbria. This paper presents the advantages of using FEPs in the development of conceptual models and the treatment of uncertainties. The paper also discusses the application of this approach to contaminated land studies and provides an example to demonstrate the application of the approach. BNFL's approach at the Drigg site involves the identification of components (features) and phenomena (events and processes) that govern interactions and dependencies between the components by arranging them in a matrix format.

Environmental Exposure↗

Uncertainty analysis for computer model projections of hurricane losses.

Projecting losses associated with hurricanes is a complex and difficult undertaking that is wrought with uncertainties. Hurricane Charley, which struck southwest Florida on August 13, 2004, illustrates the uncertainty of forecasting damages from these storms. Due to shifts in the track and the rapid intensification of the storm, real-time estimates grew from 2 to 3 billion dollars in losses late on August 12 to a peak of 50 billion dollars for a brief time as the storm appeared to be headed for the Tampa Bay area. The storm hit the resort areas of Charlotte Harbor near Punta Gorda and then went on to Orlando in the central part of the state, with early poststorm estimates converging on a damage estimate in the 28 to 31 billion dollars range. Comparable damage to central Florida had not been seen since Hurricane Donna in 1960. The Florida Commission on Hurricane Loss Projection Methodology (FCHLPM) has recognized the role of computer models in projecting losses from hurricanes. The FCHLPM established a professional team to perform onsite (confidential) audits of computer models developed by several different companies in the United States that seek to have their models approved for use in insurance rate filings in Florida. The team's members represent the fields of actuarial science, computer science, meteorology, statistics, and wind and structural engineering. An important part of the auditing process requires uncertainty and sensitivity analyses to be performed with the applicant's proprietary model. To influence future such analyses, an uncertainty and sensitivity analysis has been completed for loss projections arising from use of a Holland B parameter hurricane wind field model. Uncertainty analysis quantifies the expected percentage reduction in the uncertainty of wind speed and loss that is attributable to each of the input variables.

Journal Article↗

Probabilistic emission and immission modelling: case-study of the combined sewer-WWTP-receiving water system at Dessel (Belgium).

The impact of the combined urban drainage and WWTP system of the village of Dessel (Belgium) on the Witte Nete receiving water is modelled both in terms of emissions and immissions. The hydrodynamic and water quality modelling is performed both in a deterministic and probabilistic way. For the deterministic modelling, detailed physically based and simplified conceptual models are used in a complementary way. In the probabilistic modelling, the different uncertainties in the deterministic model are classified in input uncertainties, parameter uncertainties and model-structure uncertainties. The probabilistic simulation results can be used in risk analysis and management, for the determination of the major uncertainty-sources and priorities in model improvement, for model bias elimination and for efficient model calibration.

Belgium↗

Impact of model, methodological, and parameter uncertainty in the economic analysis of vaccination programs.

Guidelines for economic evaluations insist that the sensitivity of model results to alternative parameter values should be thoroughly explored. However, differences in model construction and analytical choices (such as the choice of a cost-effectiveness or cost-benefit framework) also introduce uncertainty in results, though these are rarely subjected to a thorough sensitivity analysis. In this article, the authors quantify the effect of model, methodological, and parameter uncertainty, taking varicella vaccination as an example. They used 3 different models (a static model, a dynamic model that only looks at the effect of vaccination on varicella, and a dynamic model that also assesses the implications of vaccination for zoster epidemiology) and 2 forms of analysis (cost-benefit and cost-utility). They also varied the discount rate and time frame of analysis. Probabilistic sensitivity analyses were performed to estimate the impact of parameter uncertainty. In their example, model and methodological choice had a profound effect on estimated cost-effectiveness, but parameter uncertainty played a relatively minor role. Under cost-utility analysis, the probabilistic sensitivity analysis suggested that there was a near certainty that vaccination dominates no vaccination, or the other way around, depending on model choice and perspective. Under cost-benefit analysis, vaccination always appeared to be attractive. Thus, the authors clearly show that model and methodological assumptions can have greater impact on results than parameter estimates, although sensitivity analyses are rarely performed on these sources of uncertainty.

Adolescent↗

Combining the cost of reducing uncertainty with the selection of risk assessment models for remediation decision of site contamination.

The decision as to whether a contaminated site poses a threat to human health and should be cleaned up relies increasingly upon the use of risk assessment models. However, the more sophisticated risk assessment models become, the greater the concern with the uncertainty in, and thus the credibility of, risk assessment. In particular, when there are several equally plausible models, decision makers are confused by model uncertainty and perplexed as to which model should be chosen for making decisions objectively. When the correctness of different models is not easily judged after objective analysis has been conducted, the cost incurred during the processes of risk assessment has to be considered in order to make an efficient decision. In order to support an efficient and objective remediation decision, this study develops a methodology to cost the least required reduction of uncertainty and to use the cost measure in the selection of candidate models. The focus is on identifying the efforts involved in reducing the input uncertainty to the point at which the uncertainty would not hinder the decision in each equally plausible model. First, this methodology combines a nested Monte Carlo simulation, rank correlation coefficients, and explicit decision criteria to identify key uncertain inputs that would influence the decision in order to reduce input uncertainty. This methodology then calculates the cost of required reduction of input uncertainty in each model by convergence ratio, which measures the needed convergence level of each key input's spread. Finally, the most appropriate model can be selected based on the convergence ratio and cost. A case of a contaminated site is used to demonstrate the methodology.

Decision Making↗

Illustrating sensitivity and uncertainty in environmental fate models using partitioning maps.

Variations of model predictions of the environmental fate of organic contaminants are usually analyzed for only one or at most a few selected chemicals, even though parameter sensitivity and contribution to uncertainty are widely different for different chemicals. A graphical method is introduced that allows for the comprehensive investigation of model sensitivity and uncertainty for all persistent organic nonelectrolytes at the same time. This is achieved by defining a two-dimensional hypothetical "chemical space" as a function of the equilibrium partition coefficients between air, water, and octanol (KOW, KAW, KOA), and plotting sensitivity and/or uncertainty of a specific model result to each input parameter as a function of this chemical space. The approach is illustrated for the bulk phase concentrations in air, water, soil, and sediment calculated by a level III model. Colored contour maps facilitate the identification of those input parameters that cause a high output variation of hypothetical and real chemicals. They also allow for the easy categorization of chemicals in terms of common parameter sensitivities, and thus comparable environmental behavior. Sensitivity varies with the mode of emission and the degradability of the chemicals, making it necessary to develop multiple sets of contour maps. Comparison of these sets of maps in turn allows the investigation of how parameter sensitivities change as a result of changes in mode of emission and persistence. The presented method can be used for investigating the sensitivity of any prediction obtained with any linear fate model that characterizes the partitioning behavior of organic chemicals with KAW, KoW, and KOA. Once the sensitivity maps have been constructed for a given environmental scenario, it is possible to perform a sensitivity analysis for a specific chemical by simple. placement of the substances' partitioning combinations within the chemical space. The maps can further contribute to the mechanistic understanding of a model's behavior, can aid in explaining observations of divergent environmental behavior of related substances, and can provide a rationale for grouping chemicals with similar model behavior, or for selecting representative example chemicals for a model investigation. They can also help in deciding when accurate and precise knowledge of physical chemical property data is crucial and when approximate numbers suffice to conduct a model investigation.

Air↗

Environmental uncertainty: implications for practice model redesign.

Analysis of one academic medical center revealed three distinct nursing practice environments distinguished by increasing levels of complexity, change, unpredictability, and uncertainty. Designing practice models to accommodate these conditions will facilitate more efficient and effective patient outcomes.

Academic Medical Centers↗

A Bayesian analysis of mouse infectivity data to evaluate the effectiveness of using ultraviolet light as a drinking water disinfectant.

Modelling disinfectant performance using Bayesian hierarchical methods can overcome problems with traditional methods and lead to improved estimates. Animal and cell-culture assays are used to estimate the degree of inactivation of a microorganism produced by a given disinfectant dose. Assay data traditionally are analyzed with logistic model or most probable number (MPN) method. These methods are limited particularly when assays show all (or no) animals or cells to be infected-estimates are reported as greater than (or less than) a measurement limit (i.e., censored data). The proposed Bayesian approach (1) properly models the propagation of uncertainty through the data analysis/modelling process, resulting in reduced model uncertainty, and (2) uses appropriate probability distribution models for the response variables, avoiding the censored data problem and more accurately describing statistical error when estimating dose-response behavior. This paper applies the Bayesian hierarchical models to logistic and MPN data from published papers for the ultraviolet (UV) inactivation of Cryptosporidium. Results are compared to those from three alternative models. The Bayesian model estimates a significantly lower UV dose for a given level of Cryptosporidium inactivation than the alternative models, due mainly to the reduced model uncertainty.

Animals↗

Quantitative magnetic resonance spectroscopy: semi-parametric modeling and determination of uncertainties.

A semi-parametric approach for the quantitative analysis of magnetic resonance (MR) spectra is proposed and an uncertainty analysis is given. Single resonances are described by parametric models or by parametrized in vitro spectra and the baseline is determined nonparametrically by regularization. By viewing baseline estimation in a reproducing kernel Hilbert space, an explicit parametric solution for the baseline is derived. A Bayesian point of view is adopted to derive uncertainties, and the many parameters associated with the baseline solution are treated as nuisance parameters. The derived uncertainties formally reduce to Cramér-Rao lower bounds for the parametric part of the model in the case of a vanishing baseline. The proposed uncertainty calculation was applied to simulated and measured MR spectra and the results were compared to Cramér-Rao lower bounds derived after the nonparametrically estimated baselines were subtracted from the spectra. In particular, for high SNR and strong baseline contributions the proposed procedure yields a more appropriate characterization of the accuracy of parameter estimates than Crémer-Rao lower bounds, which tend to overestimate accuracy.

Bayes Theorem↗

Incorporation of statistical uncertainty in health economic modelling studies using second-order Monte Carlo simulations.

Health economic modelling studies are of interest to many parties with different responsibilities and diverging interests. Therefore, it is obvious that recognising the relevance of statistical uncertainty and dealing with it appropriately are required to obtain unbiased results from health economic modelling studies, especially when those data are being used for reimbursement decisions. In this manuscript we explore the relevance of the incorporation of statistical uncertainty in a health economic model and identify various types of statistical uncertainty. The concepts were applied to a hypothetical Markov model for a hypothetical antiparkinsonian (AP) product. The method was based on the incorporation of probability distributions in the input variables using a second-order Monte Carlo simulation and the definition of minimum relevant differences for clinical and economic input variables and outcomes. Our paper shows that the outcomes of a health economic model might be severely biased when statistical uncertainty is not taken into account, which justifies the need for the incorporation of statistical uncertainty in a health economic model.

Antiparkinson Agents↗

Modelling responses to a smallpox epidemic taking into account uncertainty.

Epidemiology and modelling are currently under pressure to build consistent scenarios of control in case of deliberate release of biological weapons. In order to assess the key parameters for the control of a smallpox outbreak in a large city (2 million inhabitants), we built a stochastic model to simulate the course of an epidemic controlled by ring vaccination and case isolation. Assuming a reference scenario with 100 index cases and implementation of intervention 25 days after the attack, the model forecasts an epidemic of 730 cases with an epidemic duration of 240 days. Setting intervention 20 days later would result in an almost fourfold increase in the epidemic size. A multivariate sensitivity analysis has selected three key parameters: the basic reproduction number (i.e. the number of secondary cases infected by one case in an entirely susceptible population, equal to 3 in the reference scenario), time to intervention, and proportion of traced and vaccinated contacts.

Algorithms↗

A Reed-Frost model taking into account uncertainties in the diagnostic of the infection.

In this paper, we model the epidemic course of a pathogen infection within a semi-closed group which generates clinical signals which do not necessarily permit its ready and certain identification. Typical examples of such a pathogen are influenza-type viruses. We allow for time-varying infectivity levels among individuals, and model the probability of infection per contact as a function of the clinical signals. In order to accomplish this, we introduce a modified chain-binomial Reed-Frost model. We obtain an expression for the basic reproduction ratio and determine conditions which guarantee that the epidemic does not survive in the long-term. These conditions being functions of the signal's distribution, they can be used to design and evaluate interventions, such as treatment protocols.

Disease Outbreaks↗

Effects of exposure uncertainties in the TSCE model and application to the Colorado miners data.

The simulations in this paper show that exposure measurement error affects the parameter estimates of the biologically motivated two-stage clonal expansion (TSCE) model. For both Berkson and classical error models, we show that likelihood-based techniques of correction work reliably. For classical errors, the distribution of true exposures needs to be known or estimated in addition to the distribution of recorded exposures conditional on true exposures. Usually the exposure uncertainty biases the model parameters toward the null and underestimates the precision. But when several parameters are allowed to be dependent on exposure, e.g. initiation and promotion, then their relative importance is also influenced, and more complicated effects of exposure uncertainty can occur. The application part of this paper shows for two different types of Berkson errors that a recent analysis of the data for the Colorado plateau miners with the TSCE model is not changed substantially when correcting for such errors. Specifically, the conjectured promoting action of radon remains as the dominant radiation effect for explaining these data. The estimated promoting action of radon increases by a factor of up to 1.2 for the largest assumed exposure uncertainties.

Body Burden↗