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Results for “Uncertainty modelling”

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At least 289 records · Page 16Linked to original sources

PubMed2005

Stormwater quality modelling in combined sewers: calibration and uncertainty analysis.

Estimating the level of uncertainty in urban stormwater quality models is vital for their utilization. This paper presents the results of application of a Monte Carlo Markov Chain method based on the Bayesian theory for the calibration and uncertainty analysis of a storm water quality model commonly used in available software. The tested model uses a hydrologic/hydrodynamic scheme to estimate the accumulation, the erosion and the transport of pollutants on surfaces and in sewers. It was calibrated for four different initial conditions of in-sewer deposits. Calibration results showed large variability in the model's responses in function of the initial conditions. They demonstrated that the model's predictive capacity is very low.

Bayes Theorem↗

PubMed2000

Simulating uncertainty in climate-pest models with fuzzy numbers.

Inputs in climate-pest models are commonly expressed as point estimates ('crisp' numbers), which implies perfect knowledge of the system in study. In reality, however, all model inputs harbor some level of uncertainty. This is particularly true for climate change impact assessments where the inputs (i.e., climate projections) are highly uncertain. In this study, uncertainties in climate projections were expressed as 'fuzzy' numbers; these are uncertain numbers for which one knows that there is a range of possible values and that some values are 'more possible' than others. A generic pest risk model incorporating the combined effects of temperature, soil moisture, and cold stress was implemented in a fuzzy spreadsheet environment and run with three climate scenarios: (1) present climate (control run); (2) crisp climate change; and (3) fuzzy climate change. Under the crisp climate change scenario, winter and summer temperatures and precipitation were altered using best estimates (averaged predictions from the 1995 assessment report of the Intergovernmental Panel on Climate Change [IPCC]). Under the fuzzy scenario, climate changes were expressed as triangular fuzzy numbers, utilizing the extremes (lowest and highest predictions from the IPCC report) in addition to the best estimates. Under each scenario, environmental favorability was calculated for six locations in two geographical regions (Central North America and Southern Europe) with two hypothetical pest species having temperate or mediterranean climate requirements. Simulations with the crisp climate change scenario suggested only minor changes in overall environmental favorability compared with the control run. When simulations were conducted with the fuzzy climate change scenario, however, important changes in environmental favorability emerged, particularly in Southern Europe. In that region, the possibility of considerably increased winter precipitation led to increased values of environmental favorability. However, the simulations also showed that this result harbored a very broad range of possible outcomes. The results support the notion that uncertainty in climate change projections must be reduced before reliable impact assessments can be achieved.

Journal Article↗

PubMed2002

Effects of temporal modelling on the statistical uncertainty of spatiotemporal distributions estimated directly from dynamic SPECT projections.

Artefacts can result when reconstructing a dynamic image sequence from inconsistent single photon emission computed tomography (SPECT) projection data acquired by a slowly rotating gantry. The artefacts can lead to biases in kinetic parameters estimated from time-activity curves generated by overlaying volumes of interest on the images. Insufficient sampling and truncation of projections by cone-beam collimators can cause additional artefacts. To overcome these sources of bias in conventional image based dynamic data analysis, we have been investigating the estimation of time-activity curves and kinetic model parameters directly from dynamic SPECT projection data by modelling the spatial and temporal distribution of the radiopharmaceutical throughout the projected field of view. In the present work, we perform Monte Carlo simulations to study the effects of the temporal modelling on the statistical variability of the reconstructed spatiotemporal distributions. The simulations utilize fast methods for fully four-dimensional (4D) direct estimation of spatiotemporal distributions and their statistical uncertainties, using a spatial segmentation and temporal B-splines. The simulation results suggest that there is benefit in modelling higher orders of temporal spline continuity. In addition, the accuracy of the time modelling can be increased substantially without unduly increasing the statistical uncertainty, by using relatively fine initial time sampling to capture rapidly changing activity distributions.

Algorithms↗

PubMed1999

Uncertainties in physiologically based pharmacokinetic models caused by several input parameters.

OBJECTIVE: One of the problems in the application of physiologically based pharmacokinetic (PB-PK) models is that authors often use different input parameters, with unknown influence on the results. Differences in the simulation results obtained with various sets of parameters are examined herein. METHOD: Chemicals considered were perchloroethylene, toluene, and styrene. Simulations of alveolar concentrations, blood concentrations, and urinary metabolite excretions were performed for the three solvents. The input parameters discussed herein are physiological values, metabolic constants, and partition coefficients. The influence of metabolic constants and partition coefficients is studied by comparison of models against one another. RESULTS: Metabolic parameters such as Vmax and K(m) varied considerably between authors. Tissue-gas partition coefficients, especially for the fat compartment, also differed according to the authors. Such differences in input parameter values proved to have a large influence on PB-PK model results and, therefore, increased their uncertainties. Uncertainties were much more significant in urinary metabolite concentration than in alveolar and blood concentration for chemicals that are poorly metabolized. On the other hand, uncertainties were more significant in alveolar and blood concentrations than in urinary metabolite excretions for chemicals that are well metabolized. CONCLUSION: Careful attention is necessary in the selection and/or citation of values from published data. The validity of PB-PK models should be simultaneously confirmed with both the blood and/or alveolar concentration and urinary metabolite concentrations.

Humans↗

PubMed2003

Forward and backward uncertainty propagation: an oxidation ditch modelling example.

In the field of water technology, forward uncertainty propagation is frequently used, whereas backward uncertainty propagation is rarely used. In forward uncertainty analysis, one moves from a given (or assumed) parameter subspace towards the corresponding distribution of the output or objective function. However, in the backward uncertainty propagation, one moves in the reverse direction, from the distribution function towards the parameter subspace. Backward uncertainty propagation, which is a generalisation of parameter estimation error analysis, gives information essential for designing experimental or monitoring programmes, and for tighter bounding of parameter uncertainty intervals. The procedure of carrying out backward uncertainty propagation is illustrated in this technical note by working example for an oxidation ditch wastewater treatment plant. Results obtained have demonstrated that essential information can be achieved by carrying out backward uncertainty propagation analysis.

Forecasting↗

PubMed1997

Thurstonian and Brunswikian origins of uncertainty in judgment: a sampling model of confidence in sensory discrimination.

As a preliminary step towards the presentation of a model of confidence in sensory discrimination, the authors propose a distinction between 2 different origins of uncertainty named after 2 of the great probabilists in the history of psychology, L.L. Thurstone and Egon Brunswik. The authors review data that suggest that there are empirical as well as conceptual differences between the 2 modes of uncertainty and thus that separate models of confidence are needed in tasks dominated by Thurstonian and Brunswikian uncertainty. The article presents a computational model for 1 class of tasks dominated by Thurstonian uncertainty: sensory discrimination with pair comparisons. The sensory sampling model predicts decisions, confidence assessments, and the complex pattern of response times in simple psychophysical discrimination tasks (J.V. Baranski and W.M. Petrusic, 1994). The model also accounts for the disposition towards underconfidence often observed in sensory discrimination with pair comparisons.

Adult↗

PubMed2001

Comparison of two inversion techniques of a semi-analytical model for the determination of lake water constituents using imaging spectrometry data.

In this study, two different inversion techniques for the determination of chlorophyll-a in water were compared by a sensitivity analysis: (i) a matrix inversion method, and (ii) a curve-fitting routine. Adding white noise to the reflectance spectrum led to clearly better results for the curve-fitting routine. If, however, the atmospheric parameter visibility was not exactly known, both methods behaved similarly well. The analyses implied that the performance depended on the quality of the input spectra, the knowledge of model parameters, and also on the inversion methods, even if they were based on the same semi-analytical model. Of course, not only the uncertainties of model parameters had to be considered for the testing of the performance, but also other factors, such as processing time, implementation of the inversion algorithm, number of relevant parameters, and the application of the method to different times and different lakes.

Algorithms↗

PubMed2001

Emission strength validation using four-dimensional data assimilation: application to primary aerosol and precursors to ozone and secondary aerosol.

Three-dimensional air quality models (AQMs) represent the most powerful tool to follow the dynamics of air pollutants at urban and regional scales. Current AQMs can account for the complex interactions between gas-phase chemistry, aerosol growth, cloud and scavenging processes, and transport. However, errors in model applications still exist due in part to limitations in the models themselves and in part to uncertainties in model inputs. Four-dimensional data assimilation (FDDA) can be used as a top-down tool to validate several of the model inputs, including emissions inventories, based on ambient measurements. Previously, this FDDA technique was used to estimate adjustments in the strength and composition of emissions of gas-phase primary species and O3 precursors. In this paper, we present an extension to the FDDA technique to incorporate the analysis of particulate matter (PM) and its precursors. The FDDA approach consists of an iterative optimization procedure in which an AQM is coupled to an inverse model, and adjusting the emissions minimizes the difference between ambient measurements and model-derived concentrations. Here, the FDDA technique was applied to two episodes, with the modeling domain covering the eastern United States, to derive emission adjustments of domainwide sources of NO., volatile organic compounds (VOCs), CO, SO2, NH3, and fine organic aerosol emissions. Ambient measurements used include gas-phase inorganic and organic species and speciated fine PM. Results for the base-case inventories used here indicate that emissions of SO2 and CO appear to be estimated reasonably well (requiring minor revisions), while emissions of NOx, VOC, NH3, and organic PM with aerodynamic diameter less than 2.5 microm (PM2.5) require more significant revision.

Aerosols↗

PubMed2005

CFD model for a 3-D inhaling mannequin: verification and validation.

This work investigates the use of computational fluid dynamics (CFD) to model air flow and particle transport associated with an inhaling anatomical mannequin. The studied condition is typically representative of occupational velocities (Re = 1920) and at-rest breathing (R = U(o)/U(m) = 0.11). Methods to verify and validate CFD simulations are detailed to demonstrate convergence and describe the model's uncertainties. The standard k-epsilon model provided a reasonable flow field, although vertical velocity components were consistently smaller than the experimental validation data, owing to truncation of the computational model at hip height. Laminar particle trajectory studies indicated that the modeled velocity field resulted in a shift of particle aspiration fractions toward particles smaller than those determined experimentally, consistent with the vertical velocity field differences.

Air Movements↗

PubMedDate not supplied

Conditioned response timing and integration in the cerebellum.

Classical conditioning procedures instill knowledge about the temporal relationships between events. The unconditioned stimulus (US) is the event to be timed. The conditioned response (CR) is viewed as a prediction of the imminence of the US. Knowledge of the elapsed time between conditioned stimuli (CSs) and US delivery is expressed in the topological features of the CR. The peak amplitude of the CR coincides with the timing of the US. A simple connectionist network based on Sutton and Barto's Time Derivative (TD) Model of Pavlovian Reinforcement provides a mechanism that can account for and simulate CR timing in a variety of protocols. This article describes extensions of the model to predictive timing under temporal uncertainty. The model is expressed in terms of equations that operate in real time according to a competitive learning rule. The unfolding of time from the onsets and offsets of events such as CSs is represented by the propagation of activity along a sequence of time-tagged elements. The model can be aligned with anatomical circuits of the cerebellum and brain stem that are essential for learning and performance of conditioned eye-blink responses.

Animals↗

PubMed2002

Development of a Monte Carlo sampling shell for the pesticide root zone model and its application by the Federal Insecticide, Fungicide, and Rodenticide Act Environmental Modeling Validation Task Force.

A user interface to the U.S. Environmental Protection Agency pesticide root zone model (PRZM) was constructed to allow Monte Carlo sampling of input parameter distributions. The interface was constructed employing the Visual Basic for Applications development environment, along with the functionality of the Crystal Ball Professional forecasting and risk analysis package. The tool has been utilized by the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA) Environmental Model Validation Task Force to perform detailed statistical analyses of model input parameter uncertainty and the propagation of this uncertainty on the model outputs as well as comparisons of modeled and field-measured data.

Environment↗

PubMed2002

[Future scale and market capacity of urban water environmental infrastructure in China: a system dynamic model].

With the application of system dynamics, a dynamic, nonlinear model (SDMUWEIC) was developed in this paper in order to reflect the relationships of population, economic, resources and environment. Through a systematic procedure of model validation and uncertainty analysis, the model was applied for predicting and analyzing the future market capacity and constituents of urban water infrastructure. It illustrated the volumes and trends of potential capital market in construction, general mechanical equipments and water treatment instruments as well as their relevant influencing factors including water pricing and urbanization rate. Several different scenarios were further under test to reveal the sensitivity of different uncertain components.

Environment↗

PubMed1997

Health impacts of large releases of radionuclides. Transport and processes in freshwater ecosystems.

The partition coefficient (Kd) and the water retention rate (RR) are fundamental components of dynamic, mass-balance models, not just for radionuclides in fresh water but also for contaminants in all aquatic ecosystems. Kd may be regarded as an 'entry gate' and RR an 'exit gate'. Uncertainties in Kd and RR cause uncertainties in model predictions. Uncertainties in important rates for processes within ecosystems (such as sedimentation, diffusion, advection, bio-uptake and excretion) cannot be adequately evaluated when uncertainties exist for Kd and RR. Empirical data show that there may be a variation in Kd of two orders of magnitude with environmental factors such as pH. This is important because Kd regulates the amount of radionuclides in dissolved and particulate phases, and hence also pelagic and benthic transport. Pelagic transport is directly linked to the outflow and retention of substances in the water mass, and thus also to concentrations and ecological effects. There are many approaches for sub-models of Kd and RR. Which provide the best predictive power? This chapter gives a brief overview and discussion of the benefits and drawbacks of different alternatives for Kd and RR within the framework of a lake model for radiocaesium.

Animals↗

PubMed2002

Stochastic modelling of phosphorus transfers from agricultural land to aquatic ecosystems.

This paper describes a simple model of phosphorus (P) transfer from agricultural land to surface waters which incorporates the effects of spatial variability in catchment properties and uncertainty in model parameter values. TOPMODEL concepts are used to estimate water, solute and sediment fluxes to water bodies. The model predicts the spatial distribution of water table depth and saturation-excess overland flow based on topography. Dissolved P (DP) transfer is assumed to occur vertically in the unsaturated zone and laterally in the saturated zone. Readily soluble P is assumed to decrease exponentially with soil depth. Particulate P (PP) transfers are modelled by estimating overland flow discharge and associated sediment transport capacity. Uncertainty in the distribution of soil surface P concentrations and model parameters controlling the mobility of soil P are incorporated using Monte Carlo simulation. Predicted losses of DP are well correlated with discharge and those of PP are episodic. Highest losses of P tended to be predicted near to the stream where the water table is close to the surface. The combination of a deterministic model core with a stochastic generation of model parameters or state variables provides an attractive way of embracing variability and uncertainty in models of this kind.

Agriculture↗

PubMed1999

Chronic beryllium disease and cancer risk estimates with uncertainty for beryllium released to the air from the Rocky Flats Plant.

Beryllium was released into the air from routine operations and three accidental fires at the Rocky Flats Plant (RFP) in Colorado from 1958 to 1989. We evaluated environmental monitoring data and developed estimates of airborne concentrations and their uncertainties and calculated lifetime cancer risks and risks of chronic beryllium disease to hypothetical receptors. This article discusses exposure-response relationships for lung cancer and chronic beryllium disease. We assigned a distribution to cancer slope factor values based on the relative risk estimates from an occupational epidemiologic study used by the U.S. Environmental Protection Agency (EPA) to determine the slope factors. We used the regional atmospheric transport code for Hanford emission tracking atmospheric transport model for exposure calculations because it is particularly well suited for long-term annual-average dispersion estimates and it incorporates spatially varying meteorologic and environmental parameters. We accounted for model prediction uncertainty by using several multiplicative stochastic correction factors that accounted for uncertainty in the dispersion estimate, the meteorology, deposition, and plume depletion. We used Monte Carlo techniques to propagate model prediction uncertainty through to the final risk calculations. We developed nine exposure scenarios of hypothetical but typical residents of the RFP area to consider the lifestyle, time spent outdoors, location, age, and sex of people who may have been exposed. We determined geometric mean incremental lifetime cancer incidence risk estimates for beryllium inhalation for each scenario. The risk estimates were < 10(-6). Predicted air concentrations were well below the current reference concentration derived by the EPA for beryllium sensitization.

Adult↗

PubMed2026

Quantifying uncertainty of predictions from cancer progression models.

MOTIVATION: Cancer progresses through the accumulation of genomic events. Cancer progression models such as Mutual Hazard Networks (MHNs) describe this dynamic, enabling prediction of temporal event positions and patient-specific risks of acquiring mutations. However, current MHN analyses rely on single most likely models and do not quantify the uncertainty inherent to parameter estimation. Assessing forecast stability is essential before using them to anticipate treatment-relevant mutations, adapt targeted therapies, or prioritize monitoring of patients at elevated progression risk. RESULTS: We address a key prerequisite for the responsible clinical use of cancer progression models by making MHN-derived predictions uncertainty-aware. We present a Bayesian framework for MHN that uses Markov Chain Monte Carlo to sample from the posterior distributions of model parameters and derived predictions. For practical use we implemented the Random-Walk Metropolis, Metropolis-Adjusted Langevin Algorithm (MALA), and simplified manifold MALA samplers as part of the existing mhn Python package. Only MALA and smMALA were successful in sampling from MHN posteriors, with MALA performing best. While most MHN parameters and predictions showed low posterior variance, a small subset displayed greater variability across the posterior distribution. This differentiation cannot be obtained from a single most likely model, emphasizing the need for uncertainty quantification, especially in clinical contexts. As an illustrative example, posterior sampling identified a subgroup of STK11$-$, KRAS$+$ lung adenocarcinoma patients with a high predicted short-term risk-with low variance across posterior samples-to develop an STK11 mutation. This subgroup exhibited poorer survival under immunotherapy, resembling patterns observed in STK11+ patients. AVAILABILITY AND IMPLEMENTATION: Our implementation is part of version 1.2.0 of the mhn package (https://github.com/spang-lab/LearnMHN). All analyses including the code to produce all figures in this article can be found under https://github.com/huy29433/MCMC-sampling-for-MHN (https://doi.org/10.5281/zenodo.21160219).

Humans↗

PubMed1981

Correspondences between biomathematical and causal models for clinical decision making.

Due to incompleteness and other uncertainties, biomathematical models are unsuitable for direct use in clinical decision making. In this research work, we develop a procedure to derive clinical decision-making causal models from mathematical representation. The process involves obtaining the determination ordering for an incompletely specified system of equations. The concept of determination ordering is extended to dynamic systems of equations, in order to derive clinically usable models. The procedure to transform biomathematical models into causal representation has been machine-implemented for fluid flow models of the eye. A case-structured natural language system (CHRONOS) has been developed to accept, process, and store causal as well as biomathematical models. The system obtains the determination ordering for the biomathematical models and stores their causal representation. The system has the capability to compare the causal models. The deductive capabilities of the system can be used by a clinician to consult the diagnostic reasonings of the biomathematical and causal models.

Aqueous Humor↗

PubMed2002

Process identification and model development of contaminant transport in MSWI bottom ash.

In this work we investigate to what extent we are able to predict experimental data on column leaching of heavy metals from municipal solid waste incinerator (MSWI) bottom ash, using the current knowledge on processes controlling aqueous heavy metal concentrations in combination with a multicomponent reactive transport computer model. Heavy metal concentrations were modelled with a surface complexation model for metal sorption to (hydr)oxide minerals in the bottom ash matrix. For transport modelling it was necessary to simplify the sorption modelling approach. Therefore, we determined a minimal set of components and species that still provided an adequate description of the pH dependent heavy metal behaviour. The concentration levels of the heavy metals are generally predicted to within one order of magnitude. Discrepancies between the model and the data are caused by uncertainty in modelling parameters and a still insufficient description of the dynamics of macroelement leaching and pH. In general, the simulated leaching curves show much more abrupt changes than the measurements. This observation might be an indication of non-equilibrium. Processes that have to be taken into account for further model development are the influence of non-equilibrium effects and the facilitated transport of heavy metals by dissolved organic matter.

Forecasting↗