• Proceedings of the Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.127-127
• /
• 2021

In order to estimate parameter uncertainty of hydrological models, the consideration of the likelihood functions which provide reliable parameters of model is necessary. In this study, the Bayesian Markov Chain Monte Carlo (MCMC) method with informal likelihood functions is used to analyze the uncertainty of parameters of the SURR model for estimating the hourly streamflow of Gunnam station of Imjin basin, Korea. Three events were used to calibrate and one event was used to validate the posterior distributions of parameters. Moreover, the performance of four informal likelihood functions (Nash-Sutcliffe efficiency, Normalized absolute error, Index of agreement, and Chiew-McMahon efficiency) on uncertainty of parameter is assessed. The indicators used to assess the uncertainty of the streamflow simulation were P-factor (percentage of observed streamflow included in the uncertainty interval) and R-factor (the average width of the uncertainty interval). The results showed that the sensitivities of parameters strongly depend on the likelihood functions and vary for different likelihood functions. The uncertainty bounds illustrated the slight differences from various likelihood functions. This study confirms the importance of the likelihood function selection in the application of Bayesian MCMC to the uncertainty assessment of the SURR model.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2009.05a
• /
• pp.2215-2219
• /
• 2009

For model calibration in rainfall-runoff modeling, streamflow data at a specific outlet is obviously required but is not sufficient to identify parameters of a model since numerous parameter combinations can result in very similar model performance measures (i.e. objective functions) and indistinguishable simulated hydrographs. This phenomenon has been called 'equifinality' due to inherent parameter uncertainty involved in rainfall-runoff modeling. This study aims to investigate catchment responses in time and space to various uncertain parameter sets in distributed rainfall-runoff modeling. Seven plausible (or behavioral) parameter sets, which guarantee identically-good model performances, were sampled using deterministic and stochastic optimization methods entitled SCE and SCEM, respectively. Then, we applied them to a computational tracer method linked with a distributed rainfall-runoff model in order to trace and visualize potential origins of streamflow at a catchment outlet. The results showed that all hydrograph simulations based on the plausible parameter sets were performed equally well while internal catchment responses to them showed totally different aspects; different parameter values led to different distributions with respect to the streamflow origins in space and time despite identical simulated hydrographs. Additional information provided by the computational tracer method may be utilized as a complementary constraint for filtering out non-physical parameter set(s) (or reducing parameter uncertainty) in distributed rainfall-runoff modeling.

• Computational Structural Engineering : An International Journal
• /
• v.1 no.2
• /
• pp.81-87
• /
• 2001

A framework for reliability analysis of structural components and systems under conditions of statistical and model uncertainty is presented. The Bayesian parameter estimation method is used to derive the posterior distribution of model parameters reflecting epistemic uncertainties. Point, predictive and bound estimates of reliability accounting for parameter uncertainties are derived. The bounds estimates explicitly reflect the effect of epistemic uncertainties on the reliability measure. These developments are enhance-ments of second-moment uncertainty analysis methods developed by A. H-S. Ang and others three decades ago.

• 제어로봇시스템학회:학술대회논문집
• /
• 1995.10a
• /
• pp.348-351
• /
• 1995

This note proposes a robust LQR method for systems with structured real parameter uncertainty based on Riccati equation approach. Emphasis is on the reduction of design conservatism in the sense of quadratic performance by utilizing the uncertainty structure. The class of uncertainty treated includes all the form of additive real parameter uncertainty, which has the multiple rank structure. To handle the structure of uncertainty, the scaling matrix with block diagonal structure is introduced. By changing the scaling matrix, all the possible set of uncertainty structures can be represented. Modified algebraic Riccati equation (MARE) is newly proposed to obtain a robust feedback control law, which makes the quadratic cost finite for an arbitrary scaling matrix. The remaining design freedom, that is, the scaling matrix is used for minimizing the upper bound of the quadratic cost for all possible set of uncertainties within the given bounds. A design example is shown to demonstrate the simplicity and the effectiveness of proposed method.

• Journal of Environmental Science International
• /
• v.20 no.3
• /
• pp.321-327
• /
• 2011

Bootstrap methods is the computer-based resampling method that estimates the standard errors and confidence intervals of summary statistics using the plug-in principle for assessing the accuracy or uncertainty of statistical estimates, and the BCa method among the Bootstrap methods is known much superior to other Bootstrap methods in respect of the standards of statistical validation. Therefore this study suggests the method of the representation and treatment of uncertainty in flood risk assessment and water resources planning from the construction and application of rainfall frequency analysis model considersing the uncertainty based on the nonparametric BCa method among the Bootstrap methods for the assessement of the estimation of probability rainfall and the effect of uncertainty considering the uncertainty of the parameter estimation of probability in the rainfall frequency analysis that is the most fundamental in flood risk assessement and water resources planning.

• Nuclear Engineering and Technology
• /
• v.42 no.6
• /
• pp.690-703
• /
• 2010

Sampling based uncertainty analysis was carried out to quantify uncertainty in predictions of best estimate code RELAP5/MOD3.2 for a thermal hydraulic test (10% hot leg break LOCA) performed in the Large Scale Test Facility (LSTF) as a part of an IAEA coordinated research project. The nodalisation of the test facility was qualified for both steady state and transient level by systematically applying the procedures led by uncertainty methodology based on accuracy extrapolation (UMAE); uncertainty analysis was carried out using the Latin hypercube sampling (LHS) method to evaluate uncertainty for ten input parameters. Sixteen output parameters were selected for uncertainty evaluation and uncertainty band between $5^{th}$ and $95^{th}$ percentile of the output parameters were evaluated. It was observed that the uncertainty band for the primary pressure during two phase blowdown is larger than that of the remaining period. Similarly, a larger uncertainty band is observed relating to accumulator injection flow during reflood phase. Importance analysis was also carried out and standard rank regression coefficients were computed to quantify the effect of each individual input parameter on output parameters. It was observed that the break discharge coefficient is the most important uncertain parameter relating to the prediction of all the primary side parameters and that the steam generator (SG) relief pressure setting is the most important parameter in predicting the SG secondary pressure.

• Journal of Institute of Control, Robotics and Systems
• /
• v.4 no.5
• /
• pp.574-578
• /
• 1998

This paper considers the robust positive real problem for linear systems with linear fractional-type norm-bounded repeated scalar block parameter uncertainty. It is shown that the robust positive real problem can be converted into the standard positive real problem without uncertainty that can be used for the analysis of the given uncertain linear system and the synthesis of a controller that robustly stabilizes and achieves the extended strict positive realness property of the closed-loop transfer function. These results can be also applied to the linear system with general structured uncertainty containing repeated scalar block parameters and are extensions of the previous works that consider only norm-boundedness of the affine unstructured uncertainty.

• Journal of Institute of Control, Robotics and Systems
• /
• v.11 no.11
• /
• pp.920-929
• /
• 2005

This paper presents controller design method for nonlinear radar gimbal system with parameter uncertainty and time delay. In order to consider nonlinearity of gimbal bearing frictional torque, we firstly represent fuzzy model for the nonlinear gimbal system, which is achieved by fuzzy combination of linear models through nonlinear fuzzy membership functions. And secondly we propose a delay dependent fuzzy $H_\infty$ controller design method for the delayed fuzzy model with parameter uncertainty and design radar gimbal controller. The designed controller stabilize gimbal system and guarantee $H_\infty$ performance. A computer simulation is given to illustrate stabilized control performances of the designed controller.

• Nuclear Engineering and Technology
• /
• v.55 no.7
• /
• pp.2395-2406
• /
• 2023

To study the influence of parameter uncertainty in small pressurized water reactor (SPWR) once-through steam generator (OTSG), the nonlinear mathematical model of the SPWR is firstly established. Including the reactor core model, the OTSG model and the pressurizer model. Secondly, a control strategy that both the reactor core coolant average temperature and the secondary-side outlet pressure of the OTSG are constant is adopted. Then, the uncertainty quantification method is established based on Latin hypercube sampling and statistical method. On this basis, the quantitative platform for parameter uncertainty of the OTSG is developed. Finally, taking the uncertainty in primary-side flowrate of the OTSG as an example, the platform application work is carried out under the variable load in SPWR and step disturbance of secondary-side flowrate of the OTSG. The results show that the maximum uncertainty in the critical output parameters is acceptable for SPWR.

• Journal of Korea Water Resources Association
• /
• v.43 no.12
• /
• pp.1011-1027
• /
• 2010

In hydrologic modeling, prediction uncertainty generally stems from various uncertainty sources associated with model structure, data, and parameters, etc. This study aims to assess the parameter uncertainty effect on hydrologic prediction results. For this objective, a distributed rainfall-sediment yield-runoff model, which consists of rainfall-runoff module for simulation of surface and subsurface flows and sediment yield module based on unit stream power theory, was applied to the mesoscale mountainous area (Cheoncheon catchment; 289.9 $km^2$). For parameter uncertainty evaluation, the model was calibrated by a multi-objective optimization algorithm (MOSCEM) with two different objective functions (RMSE and HMLE) and Pareto optimal solutions of each case were then estimated. In Case I, the rainfall-runoff module was calibrated to investigate the effect of parameter uncertainty on hydrograph reproduction whereas in Case II, sediment yield module was calibrated to show the propagation of parameter uncertainty into sedigraph estimation. Additionally, in Case III, all parameters of both modules were simultaneously calibrated in order to take account of prediction uncertainty in rainfall-sediment yield-runoff modeling. The results showed that hydrograph prediction uncertainty of Case I was observed over the low-flow periods while the sedigraph of high-flow periods was sensitive to uncertainty of the sediment yield module parameters in Case II. In Case III, prediction uncertainty ranges of both hydrograph and sedigraph were larger than the other cases. Furthermore, prediction uncertainty in terms of spatial distribution of erosion and deposition drastically varied with the applied model parameters for all cases.