• Nuclear Engineering and Technology
• /
• 제35권1호
• /
• pp.64-79
• /
• 2003

In the risk and reliability analysis of complex technological systems, the primary concern of formal uncertainty analysis is to understand why uncertainties arise, and to evaluate how they impact the results of the analysis. In recent times, many of the uncertainty analyses have focused on parameters of the risk and reliability analysis models, whose values are uncertain in an aleatory or an epistemic way. As the field of parametric uncertainty analysis matures, however, more attention is being paid to the explicit treatment of uncertainties that are addressed in the predictive model itself as well as the accuracy of the predictive model. The essential steps for evaluating impacts of these model uncertainties in the presence of parameter uncertainties are to determine rigorously various sources of uncertainties to be addressed in an underlying model itself and in turn model parameters, based on our state-of-knowledge and relevant evidence. Answering clearly the question of how to characterize and treat explicitly the forgoing different sources of uncertainty is particularly important for practical aspects such as risk and reliability optimization of systems as well as more transparent risk information and decision-making under various uncertainties. The main purpose of this paper is to provide practical guidance for quantitatively treating various model uncertainties that would often be encountered in the risk and reliability modeling process of complex technological systems.

• 한국환경과학회지
• /
• 제20권3호
• /
• 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.

• Journal of the Korean Data and Information Science Society
• /
• 제27권1호
• /
• pp.9-18
• /
• 2016

모수불확실성을 반영하는 손실모형으로는 Heckman과 Meyers가 제안한 모형이 주로 인용되고 있다. 이 모형은 모수 자체가 어떤 확률분포를 따른다는 가정을 하고 있으며 IAA, Swiss Solvency Test, EU Solvency II 등에서 참고하고 있다. 반면 베이지안 기법을 이용한 연구는 모수에 대한 선험적 정보 즉, 사전분포를 이용하여 모수불확실성을 반영한다. 그러나 현실에서는 두 가지 방법을 동시에 고려해야 하는 상황이 빈번히 발생한다. 이에 본 연구는 Heckman-Meyers의 모형과 베이지안 접근법을 동시에 고려한 베이지안 H-M CRM모형을 제안하고 그 특성을 분석하였다.

• 한국경영과학회:학술대회논문집
• /
• 대한산업공학회/한국경영과학회 1990년도 춘계공동학술대회논문집; 한국과학기술원; 28 Apr. 1990
• /
• pp.272-288
• /
• 1990

The uncertainty quantification process in probabilistic Risk Assessment usually involves a specification of the uncertainty in the input data and the propagation of this uncertainty to the final risk results. The distributional sensitivity analysis is to study the impact of the various assumptions made during the quantification of input parameter uncertainties on the final output uncertainty. The uncertainty importance of input parameters, in this case, should reflect the degree of changes in the whole output distribution and not just in a point estimate value. A measure of the uncertainty importance is proposed in the present paper. The measure is called the distributional sensitivity measure(DSM) and explicitly derived from the definition of the Kullback's discrimination information. The DSM is applied to three typical discrimination information. The DSM is applied to three typical cases of input distributional changes: 1) Uncertainty is completely eliminated, 2) Uncertainty range is increased by a factor of 10, and 3) Type of distribution is changed. For all three cases of application, the DSM-based importance ranking agrees very well with the observed changes of output distribution while other statistical parameters are shown to be insensitive.

• Nuclear Engineering and Technology
• /
• 제54권8호
• /
• pp.2941-2959
• /
• 2022

The fire brigade non-suppression probability model is a major factor that should be considered in evaluating fire-induced risk through fire probabilistic risk assessment (PRA), and also uncertainty is a critical consideration in support of risk-informed performance-based (RIPB) fire protection decision-making. This study developed an optimal integrated probabilistic fire brigade non-suppression model considering uncertainty of parameters based on the Bayesian Markov Chain Monte Carlo (MCMC) approach on electrical fire which is one of the most risk significant contributors. The result shows that the log-normal probability model with a location parameter (µ) of 2.063 and a scale parameter (σ) of 1.879 is best fitting to the actual fire experience data. It gives optimal model adequacy performance with Bayesian information criterion (BIC) of -1601.766, residual sum of squares (RSS) of 2.51E-04, and mean squared error (MSE) of 2.08E-06. This optimal log-normal model shows the better performance of the model adequacy than the exponential probability model suggested in the current fire PRA methodology, with a decrease of 17.3% in BIC, 85.3% in RSS, and 85.3% in MSE. The outcomes of this study are expected to contribute to the improvement and securement of fire PRA realism in the support of decision-making for RIPB fire protection programs.

• 한국방재학회:학술대회논문집
• /
• 한국방재학회 2008년도 정기총회 및 학술발표대회
• /
• pp.723-726
• /
• 2008

It is not always easy to estimate the parameters in hydrologic models due to insufficient hydrologic data when hydraulic structures are designed or water resources plan are established, uncertainty analysis, therefore, are inevitably needed to examine reliability for the estimated results. With regard to this point, this study applies a Bayesian Markov Chain Monte Carlo scheme to the NWS-PC rainfall-runoff model that has been widely used, and a case study is performed in Soyang Dam watershed in Korea. The NWS-PC model is calibrated against observed daily runoff, and thirteen parameters in the model are optimized as well as posterior distributions associated with each parameter are derived. The Bayesian Markov Chain Monte Carlo shows a improved result in terms of statistical performance measures and graphical examination. The patterns of runoff can be influenced by various factors and the Bayesian approaches are capable of translating the uncertainties into parameter uncertainties. One could provide against an expected runoff event by utilizing information driven by Bayesian methods. Therefore, the rainfall-runoff analysis coupled with the uncertainty analysis can give us an insight in evaluating flood risk and dam size in a reasonable way.

• 한국농공학회논문집
• /
• 제56권2호
• /
• pp.11-23
• /
• 2014

The impact and adaption on agricultural water resources considering climate change is significant for reservoirs. The change in rainfall patterns and hydrologic factors due to climate change increases the uncertainty of agricultural water supply and demand. The quantitative evaluation method of uncertainty based on agricultural water resource management under future climate conditions is a major concern. Therefore, it is necessary to improve the vulnerability management technique for agricultural water supply based on a probabilistic and stochastic risk evaluation theory. The objective of this study was to analyse the uncertainty of water resources under future climate change using probability distribution function of water supply in agricultural reservoir and demand in irrigation district. The uncertainty of future water resources in agricultural reservoirs was estimated using the time-specific analysis of histograms and probability distributions parameter, for example the location and the scale parameter. According to the uncertainty analysis, the future agricultural water supply and demand in reservoir tends to increase the uncertainty by the low consistency of the results. Thus, it is recommended to prepare a resonable decision making on water supply strategies in terms of using climate change scenarios that reflect different future development conditions.

• Earthquakes and Structures
• /
• 제6권1호
• /
• pp.19-43
• /
• 2014

A risk assessment framework for evaluating building structures is implemented in this study. This framework allows considering sources of uncertainty both on structural capacity and seismic demand. In particular randomness on seismic load, incident angle, material properties, floor mass and structural damping are considered; in addition the choice of fibre modelling versus plastic hinge model is also considered as a source of uncertainty. The main objective of this work is to study the contribution of these sources of uncertainty on the fragilities of steel and steel-reinforced concrete composite 3D building structures. The fragility curves are expressed in the form of a two-parameter lognormal distribution where vertical statistics in conjunction with metaheuristic optimization are implemented for calculating the two parameters.

• 한국환경과학회지
• /
• 제20권3호
• /
• pp.329-340
• /
• 2011

The probability concepts mainly used for rainfall or flood frequency analysis in water resources planning are the frequentist viewpoint that defines the probability as the limit of relative frequency, and the unknown parameters in probability model are considered as fixed constant numbers. Thus the probability is objective and the parameters have fixed values so that it is very difficult to specify probabilistically the uncertianty of these parameters. This study constructs the uncertainty evaluation model using Bayesian MCMC and Metropolis -Hastings algorithm for the uncertainty quantification of parameters of probability distribution in rainfall frequency analysis, and then from the application of Bayesian MCMC and Metropolis- Hastings algorithm, the statistical properties and uncertainty intervals of parameters of probability distribution can be quantified in the estimation of probability rainfall so that the basis for the framework configuration can be provided that can specify the uncertainty and risk in flood risk assessment and decision-making process.

• Nuclear Engineering and Technology
• /
• 제19권4호
• /
• pp.249-265
• /
• 1987

고준위 방사성폐기물 처분장에 대한 확률론적 위험도 평가를 위해 지금까지 많은 방법들이 제안되어 왔다. 이 계는 많은 불확실성을 갖는 입력 변수들을 갖고 있어서 이 입력변수들에 대해 계산된 위험도 역시 많은 불착실성을 갖는다. 본 논문에서는 이러한 점들을 조직적으로 분석하기 위하여 여러가지 불확실도 및 민감도 분석 방법들이 개발되었고 고준위 폐기물 처분장의 위험도 평가에 적용되었다. 본 논문을 통해 개발된 통계 패키지 SPUSA는 통계적 열여유도 분석, 방사선원 불확실도 분석등 등의 분야에도 사용될 수 있다.