• Journal of Korea Water Resources Association
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• v.33 no.1
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• pp.15-30
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• 2000

This study is to develop the applied method of reliability analysis to present risk - initial water level relationship in the small reservoir. To determine the reliability, the grasping of uncertainty sources is prerequisited and performance function is formulated. Reliability analysis method is a statistical method and the basic procedure of risk evaluation for overtopping of reservoir is as follows. 1. Define the risk criterion and performance function for the overtopping. 2. Determine the uncertainties of all the variables in the performance function. 3. Perform the risk analysis with suitable risk calculation method. Reliability analysis method such as Monte Carlo simulation(MCS) method and mean value first order second moment(MVFOSM) method are used to calculate the risk for reservoir. Finally, risk - initial water level relationship is established according to return period and it is useful for reservoir operation and safety assessment.ssment.

• Journal of Korean Society of Transportation
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• v.32 no.6
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• pp.694-702
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• 2014

Since Greenhouse Gas Inventory & Research Center (GIR) of Korea was founded in 2010, the annual greenhouse gas inventory reports, one of the collections of GIR's major affairs, have been published from 2012. In the reports many items related to greenhouse gas emission quantities are included, but among them uncertainty values are replaced to basic values which IPCC guideline suggests. Even though IPCC guideline suggests the equations of each Tier level in details, the guideline recommends developing nation's own methodology on uncertainty which is closely related to statistical problems such as the estimation of a probability density function or Monte carlo methods. In the road transportation sector the emissions have been calculated by Tier 1 but the uncertainties have not been reported. This study introduce a bootstrap technique and Monte carlo method to estimates annual emission quantity and uncertainty, given activity data and emission factors such as annual traveled distances, fuel efficiencies and emission coefficients.

• Journal of Korean Society on Water Environment
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• v.36 no.5
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• pp.373-384
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• 2020

Hydrological models are based on a combination of parameters that describe the hydrological characteristics and processes within a watershed. For this reason, the model performance and accuracy are highly dependent on the parameters. However, model uncertainties caused by parameters with stochastic characteristics need to be considered. As a follow-up to the study conducted by Choi et al (2020), who developed a relatively simple semi-distributed hydrological model, we propose a tool to estimate the posterior distribution of model parameters using the Metropolis-Hastings algorithm, a type of Markov-Chain Monte Carlo technique, and analyze the uncertainty of model parameters and simulated stream flow. In addition, the uncertainty caused by the parameters of each version is investigated using the lumped and semi-distributed versions of the applied model to the Hapcheon Dam watershed. The results suggest that the uncertainty of the semi-distributed model parameters was relatively higher than that of the lumped model parameters because the spatial variability of input data such as geomorphological and hydrometeorological parameters was inherent to the posterior distribution of the semi-distributed model parameters. Meanwhile, no significant difference existed between the two models in terms of uncertainty of the simulation outputs. The statistical goodness of fit of the simulated stream flows against the observed stream flows showed satisfactory reliability in both the semi-distributed and the lumped models, but the seasonality of the stream flow was reproduced relatively better by the distributed model.

• Journal of Korea Water Resources Association
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• v.44 no.3
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• pp.209-220
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• 2011

Input uncertainty is one of the major sources of uncertainty in hydrologic modeling. In this paper, first, three alternate rainfall inputs generated by different interpolation schemes were used to see the impact on a distributed watershed model. Later, the residuals of precipitation interpolations were tested as a source of ensemble streamflow generation in two river basins in the U.S. Using the Monte Carlo parameter search, the relationship between input and parameter uncertainty was also categorized to see sensitivity of the parameters to input differences. This analysis is useful not only to find the parameters that need more attention but also to transfer parameters calibrated for station measurement to the simulation using different inputs such as downscaled data from weather generator outputs. Input ensembles that preserves local statistical characteristics are used to generate streamflow ensembles hindcast, and showed that the ensemble sets are capturing the observed steamflow properly. This procedure is especially important to consider input uncertainties in the simulation of streamflow forecast.

• Journal of the Korea institute for structural maintenance and inspection
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• v.11 no.4
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• pp.138-146
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• 2007

Geotechnical performance at the soft ground is strongly dependent on the properties of the soil beneath and adjacent to the structure of interest. These soil properties can be described using deterministic and/or probabilistic models. Deterministic models typically use a single discrete descriptor for the parameter of interest. Probabilistic models describe parameters by using discrete statistical descriptors or probability distribution density functions. The consolidation process depends on several uncertain parameters including the coefficients of consolidation and coefficients of permeability in vertical and horizontal directions. The implication of this uncertain parameter in the design of prefabricated vertical drains for soil improvement is discussed. A sensitivity analysis of the degree of consolidation and calculation of settlements to these uncertain parameters is presented for clayey deposits.

• Nuclear Engineering and Technology
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• v.51 no.8
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• pp.2018-2025
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• 2019

This study systematically compared two hybrid deterministic/Monte Carlo transport codes, ADVANTG/MCNP and MAVRIC, in solving a difficult shielding problem for a real-world spent fuel storage cask. Both hybrid codes were developed based on the consistent adjoint driven importance sampling (CADIS) methodology but with different implementations. The dose rate distributions on the cask surface were of primary interest and their predicted results were compared with each other and with a straightforward MCNP calculation as a baseline case. Forward-Weighted CADIS was applied for optimization toward uniform statistical uncertainties for all tallies on the cask surface. Both ADVANTG/MCNP and MAVRIC achieved substantial improvements in overall computational efficiencies, especially for gamma-ray transport. Compared with the continuous-energy ADVANTG/MCNP calculations, the coarse-group MAVRIC calculations underestimated the neutron dose rates on the cask's side surface by an approximate factor of two and slightly overestimated the dose rates on the cask's top and side surfaces for fuel gamma and hardware gamma sources because of the impact of multigroup approximation. The fine-group MAVRIC calculations improved to a certain extent and the addition of continuous-energy treatment to the Monte Carlo code in the latest MAVRIC sequence greatly reduced these discrepancies. For the two continuous-energy calculations of ADVANTG/MCNP and MAVRIC, a remaining difference of approximately 30% between the neutron dose rates on the cask's side surface resulted from inconsistent use of thermal scattering treatment of hydrogen in concrete.

• YAKHAK HOEJI
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• v.52 no.6
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• pp.480-487
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• 2008

We described an estimation of measurement uncertainty in quantitative analysis of 11-nor-9-carboxy-${\Delta}^9$-tetrahydrocannabinol (THCCOOH), the major metabolite of ${\Delta}^9$-tetrahydrocannabinol, in urine sample by solid-phase extraction (SPE) and GC/MS detection. The analytical results were compared and the different contributions to the uncertainty were evaluated. Inter-day and inter-person validation were performed using statistical analysis of several indicative factors. Measurement uncertainty associated with target analyte in real forensic samples was estimated using quality control (QC) data. Traceability of measurement was established through traceable standards, calibrated volumetric glassware and volume measuring device. The major factors of contribution to combined standard uncertainty, were calibration linearity, inter-day repeatability and inter-person reproducibility, while those associated with preparation of analytical standards and sampling volume were not so important considering the degree of contribution. Relative combined standard uncertainties associated with the described method was 12.05% for THCCOOH.

• Journal of Digital Convergence
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• v.11 no.10
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• pp.387-394
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• 2013

Data mining is the process of discovering hidden, non-trivial patterns in large amounts of data records in order to be used very effectively for analysis and forecasting. Because hundreds of variables give rise to a high level of redundancy and dimensionality with time complexity, they are more likely to have spurious relationships, and even the weakest relationships will be highly significant by any statistical test. Hence cluster analysis is a main task of data mining and is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. In this paper system implementation is of great significance, which defines a new definition based on information-theoretic entropy and analyse the analogue behaviors of objects at hand so as to address the measurement of uncertainties in the classification of categorical data. The sources were taken from a survey aimed to identify of job guidance from students in high school pyeongtaek. we show how variable precision information-entropy based rough set can be used to group student in each section. It is proved that the proposed method has the more exact classification than the conventional in attributes more than 10 and that is more effective in job guidance for students.

• Journal of Korea Water Resources Association
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• v.41 no.3
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• pp.251-264
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• 2008

In this study, a nonhomogeneous markov model which is able to simulate hourly rainfall series is developed for estimating reliable hydrologic variables. The proposed approach is applied to simulate hourly rainfall series in Korea. The simulated rainfall is used to estimate the design rainfall and flood in the watershed, and compared to observations in terms of reproducing underlying distributions of the data to assure model's validation. The model shows that the simulated rainfall series reproduce a similar statistical attribute with observations, and expecially maximum value is gradually increased as number of simulation increase. Therefore, with the proposed approach, the non-homogeneous markov model can be used to estimate variables for the purpose of design of hydraulic structures and analyze uncertainties associated with rainfall input in the hydrologic models.

• Journal of the Korean Geotechnical Society
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• v.28 no.12
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• pp.65-76
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• 2012

In this study, probabilistic analysis of seepage through a two-layered soil foundation was performed. The hydraulic conductivity of soil shows significant spatial variations in different layers because of stratification; further, it varies on a smaller scale within each individual layer. Therefore, the deterministic seepage analysis method was extended to develop a probabilistic approach that accounts for the uncertainties and spatial variation of the hydraulic conductivity in a layered soil profile. Two-dimensional random fields were generated on the basis of the Karhunen-Lo$\grave{e}$ve expansion in a manner consistent with a specified marginal distribution function and an autocorrelation function for each layer. A Monte Carlo simulation was then used to determine the statistical response based on the random fields. A series of analyses were performed to verify the application potential of the proposed method and to study the effects of uncertainty due to the spatial heterogeneity on the seepage behavior of two-layered soil foundation beneath water retaining structure. The results showed that the probabilistic framework can be used to efficiently consider the various flow patterns caused by the spatial variability of the hydraulic conductivity in seepage assessment for a layered soil foundation.