• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2018년도 학술발표회
• /
• pp.155-155
• /
• 2018

Uncertainty in flood forecasting using a coupled meteorological and hydrological model is arisen from various sources, especially the uncertainty comes from the inaccuracy of Quantitative Precipitation Forecasts (QPFs). In order to improve the capability of flood forecast, the uncertainty estimation and mitigation are required to perform. This study is conducted to investigate and reduce such uncertainty. First, ensemble QPFs are generated by using Monte - Carlo simulation, then each ensemble member is forced as input for a hydrological model to obtain ensemble streamflow prediction. Likelihood measures are evaluated to identify feasible member. These members are retained to define upper and lower limits of the uncertainty interval and assess the uncertainty. To mitigate the uncertainty for very short lead time, a blending method, which merges the ensemble QPFs with radar-based rainfall prediction considering both qualitative and quantitative skills, is proposed. Finally, blending bias ratios, which are estimated from previous time step, are used to update the members over total lead time. The proposed method is verified for the two flood events in 2013 and 2016 in the Yeonguol and Soyang watersheds that are located in the Han River basin, South Korea. The uncertainty in flood forecasting using a coupled Local Data Assimilation and Prediction System (LDAPS) and Sejong University Rainfall - Runoff (SURR) model is investigated and then mitigated by blending the generated ensemble LDAPS members with radar-based rainfall prediction that uses McGill algorithm for precipitation nowcasting by Lagrangian extrapolation (MAPLE). The results show that the uncertainty of flood forecasting using the coupled model increases when the lead time is longer. The mitigation method indicates its effectiveness for mitigating the uncertainty with the increases of the percentage of feasible member (POFM) and the ratio of the number of observations that fall into the uncertainty interval (p-factor).

• 대한안전경영과학회지
• /
• 제9권1호
• /
• pp.145-156
• /
• 2007

This paper is to propose model classification and evaluation of measurement uncertainty. In order to obtain type A and B uncertainty, variety of measurement mathematical models are illustrated by example. The four steps to evaluate expanded uncertainty are indicated as following; First, to get type A standard uncertainty, measurement mathematical models of single, double, multiple, design of experiment and serial autocorrelation are shown. Second, to solve type B standard uncertainty measurement mathematical models of empirical probability distributions and multivariate are presented. Third, type A and B combined uncertainty, considering sensitivity coefficient, linearity and correlation are discussed. Lastly, expanded uncertainty, considering degree of freedom for type A, B uncertainty and coverage factor are presented with uncertainty budget. SPC control chart to control expanded uncertainty is shown.

• 동력기계공학회지
• /
• 제4권3호
• /
• pp.72-80
• /
• 2000

In order to design a stable robust controller, nominal model, and the upper bound about the uncertainty which is the error of the model are needed. The problem to estimate the nominal model of controlled system and the upper bound of uncertainty at the same time is called robust identification. When the nominal model of controlled system and the upper bound of uncertainty in relation to robust identification are given, the evaluation of the validity of the model and the upper bound makes it possible to distinguish whether there is a model which explains observation data including disturbance among the model set. This paper suggests a method to identity the uncertainty which removes disturbance and expounds observation data by giving a probable postulation and plural data set to disturbance. It also examines the suggested method through a numerical computation simulation and validates its effectiveness.

• Nuclear Engineering and Technology
• /
• 제54권3호
• /
• pp.790-802
• /
• 2022

Large-scale reactor simulation often requires the use of Monte Carlo calculation techniques to estimate important reactor parameters. One drawback of these Monte Carlo calculation techniques is they inevitably result in some uncertainty in calculated quantities. The present study includes parametric uncertainty quantification (UQ) and sensitivity analysis (SA) on the Advanced Test Reactor Critical (ATRC) facility housed at Idaho National Laboratory (INL) and addresses some complications due to Monte Carlo uncertainty when performing these analyses. This approach for UQ/SA includes consideration of Monte Carlo code uncertainty in computed sensitivities, consideration of uncertainty from directly measured parameters and a comparison of results obtained from brute-force Monte Carlo UQ versus UQ obtained from a surrogate model. These methodologies are applied to the uncertainty and sensitivity of k_eff for two sets of uncertain parameters involving fuel plate geometry and fuel plate composition. Results indicate that the less computationally-expensive method for uncertainty quantification involving a linear surrogate model provides accurate estimations for k_eff uncertainty and the Monte Carlo uncertainty in calculated k_eff values can have a large effect on computed linear model parameters for parameters with low influence on k_eff.

• 근관절건강학회지
• /
• 제5권1호
• /
• pp.7-25
• /
• 1998

Rheumatoid arthritis, unlike other chronic diseases, causes the patients to experience uncertainty in their daily lives and thus to feel threat on their emotional comfort because of inconsistent and unpredictable symptoms such as pain. Therefore, a theoretical framework is needed for explanation of uncertainty in patients having rheumatoid arthritis. A hypothetical model was constructed on the basis of Mishel's Uncertainty Theory and other literature review. The model included 9 theoretical concepts and 19 paths. Subjects of the study constituted 330 partients who visited outpatient clinics of two university hospitals and one general hospital in Seoul. Self report questionnaires were used to measure the variables affecting uncertainty. Reliability coefficients of these instruments were found Cronbach's Alpha=$.70{\sim}.94$. In data analysis, SAS program and PC-LISREL 8.03 computer program were utilized for descriptive statistics and covariance structure analysis. The results of covariance structure analysis for model fitness were as follows : 1) Hypothetical model showed a good fit to the empirical data : Chi-square($X^2$)=41.81 (df=11, P=.000), Goodness of Fit Index=.974, Root Mean Square Residual=.049, Normed Fit Index=.928, Non Normed Fit Index=.814. 2) For the validity and the parcimony of model, a modified model was constructed by appending 2 paths and deleting 5 paths according to the criteria of statistical significance and meaningfulness. 3) The results of hypothesis testing were as follows : (1) Educational level, event familiarity and severity of illness had a direct effect on uncertainty : Event congruency had both direct and indirect effect on uncertainty : Credible authority and symptom consistency had a nonsignificant direct effect on uncertainty, (2) Illness duration, symptom consistency, and event congruency had a direct effect on severity of illness ; Credible authority had a both direct and indirect effect on severity of illness ; Event congruency had the greatest effect on severity of illness, and event familiarity had a nonsignificant direct effect on severity of illness.

• Management Science and Financial Engineering
• /
• 제13권1호
• /
• pp.35-53
• /
• 2007

We consider a(worst-case) robust optimization version of the Economic Order Quantity(EOQ) model. Order setup costs and inventory carrying costs are assumed to have uncertainty in their values, and the uncertainty description of the two parameters is supposed to be given by an ellipsoidal representation. A genetic algorithm combined with Monte Carlo simulation is proposed to approximate the ellipsoidal representation. The objective function of the model under ellipsoidal uncertainty description is derived, and the resulting problem is solved by another genetic algorithm. Computational test results are presented to show the performance of the proposed method.

• Asia pacific journal of information systems
• /
• 제16권3호
• /
• pp.95-116
• /
• 2006

Although the study of post-adoption has increased in recent years, few studies have focused on the moderating effect of uncertainty avoidance on the relationship between post-expectation and behavior. The purpose of this study is to examine the moderating effect of uncertainty avoidance in the mobile Internet domains. This study proposed a post-adoption model based on prior continuance model. This theoretical model was verified empirically by conducting web surveys and multi-group analysis. Based on the survey data, we classified users into those with high uncertainty avoidance and those with low uncertainty avoidance. The results indicate that post expectations have significant impacts on satisfaction and continuance intention. The results also show that the impacts of intrinsic motivational factors of mobile Internet services on satisfaction and continuance intention are stronger for users with high uncertainty avoidance. On the other hand, the impacts of extrinsic motivational factors on satisfaction and continuance intention are stronger for users with low uncertainty avoidance, with a few exceptions. This paper ends with theoretical and managerial implications of the study results, as well as limitations and future research directions.

• Nuclear Engineering and Technology
• /
• 제55권7호
• /
• 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.

• Nuclear Engineering and Technology
• /
• 제46권5호
• /
• pp.619-632
• /
• 2014

The Best Estimate Plus Uncertainty (BEPU) method has been widely used to evaluate the uncertainty of a best-estimate thermal hydraulic system code against a figure of merit. This uncertainty is typically evaluated based on the physical model's uncertainties determined by expert judgment. This paper introduces the application of data assimilation methodology to determine the uncertainty bands of the physical models, e.g., the mean value and standard deviation of the parameters, based upon the statistical approach rather than expert judgment. Data assimilation suggests a mathematical methodology for the best estimate bias and the uncertainties of the physical models which optimize the system response following the calibration of model parameters and responses. The mathematical approaches include deterministic and probabilistic methods of data assimilation to solve both linear and nonlinear problems with the a posteriori distribution of parameters derived based on Bayes' theorem. The inverse problem was solved analytically to obtain the mean value and standard deviation of the parameters assuming Gaussian distributions for the parameters and responses, and a sampling method was utilized to illustrate the non-Gaussian a posteriori distributions of parameters. SPACE is used to demonstrate the data assimilation method by determining the bias and the uncertainty bands of the physical models employing Bennett's heated tube test data and Becker's post critical heat flux experimental data. Based on the results of the data assimilation process, the major sources of the modeling uncertainties were identified for further model development.

• 한국기후변화학회지
• /
• 제7권4호
• /
• pp.407-419
• /
• 2016

The low flow is the necessary and important index to establish national water planning, however there are lots of uncertainty in the low flow estimation. Therefore, the objectives of this study are to assess the climate change uncertainty and the effects of hydrological models on low flow estimation. The 5 RCMs (HadGEM3-RA, RegCM4, MM5, WRF, and RSM), 5 statistical post-processing methods and 2 hydrological models were applied for evaluation. The study area were selected as Chungju dam and Soyang river dam basin, and the 30 days minimum flow is used for the low flow evaluation. The results of the uncertainty analysis showed that the hydrological model was the largest source of uncertainty about 41.5% in the low flow projection. The uncertainty of hydrological model is higher than the other steps (RCM, statistical post-processing). Also, VIC model is more sensitive for climate change compared to SWAT model. Therefore, the hydrological model should be thoroughly reviewed for the climate change impact assessment on low flow.