• Journal of Korea Water Resources Association
• /
• v.53 no.2
• /
• pp.121-129
• /
• 2020

The increase of impervious surface and development along the river due to urbanization not only causes an increase in the number of associated flood risk factors but also exacerbates flood damage, leading to difficulties in flood management. Flood control measures should be prioritized based on various geographical information in urban areas. In this study, a probabilistic flood hazard assessment was applied to flood-prone areas near an urban river. Flood hazard maps were alternatively considered and used to describe the expected inundation areas for a given set of predictors such as elevation, slope, runoff curve number, and distance to river. This study proposes a Bayesian logistic regression-based flood risk model that aims to provide a probabilistic risk metric such as population-at-risk (PAR). Finally, the logistic regression model demonstrates the probabilistic flood hazard maps for the entire area.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.39 no.1
• /
• pp.155-163
• /
• 2019

The knowledge about drought propagation is very important in accurate estimation of hydrological drought characteristics and efficient development of early warning system. This study investigated a probabilistic relationship of drought propagation based on Bayesian network model for historic period and for future projection under climate change scenario RCP 8.5 over the Han River basin. The results revealed that the propagation rate and lag time have increasing and decreasing trends from the historic period of 1967-2013 to the future periods of 2014-2053 and 2054-2100 under climate change, respectively. The probabilistic results of Bayesian model revealed that the probability of occurrence of lag time varied spatially and decreased when the intensity of meteorological drought changed from moderate to severe and extreme condition during 1967-2013. The values of probability increased in the first future period of 2014-2053 in several sub-basins and slight decreased in the second period of 2054-2100. The proposed probabilistic results will be useful for the decision makers to develop related policies with an appropriate insight toward the future drought status.

• Earthquakes and Structures
• /
• v.8 no.4
• /
• pp.935-956
• /
• 2015

Structural Health Monitoring (SHM) of steel towers has become a hot research topic. From the literature, it is impractical and impossible to develop a "general" method that can detect all kinds of damages for all types of structures. A practical method should make use of the characteristics of the type of structures and the kind of damages. This paper reports a feasibility study on the use of measured modal parameters for the detection of damaged braces of tower structures following the Bayesian probabilistic approach. A substructure-based structural model-updating scheme, which groups different parts of the target structure systematically and is specially designed for tower structures, is developed to identify the stiffness distributions of the target structure under the undamaged and possibly damaged conditions. By comparing the identified stiffness distributions, the damage locations and the corresponding damage extents can be detected. By following the Bayesian theory, the probability model of the uncertain parameters is derived. The most probable model of the steel tower can be obtained by maximizing the probability density function (PDF) of the model parameters. Experimental case studies were employed to verify the proposed method. The contributions of this paper are not only on the proposal of the substructure-based Bayesian model updating method but also on the verification of the proposed methodology through measured data from a scale model of transmission tower under laboratory conditions.

• Structural Engineering and Mechanics
• /
• v.25 no.3
• /
• pp.331-345
• /
• 2007

Uncertainties enter a complex analysis from a variety of sources: variability, lack of data, human errors, model simplification and lack of understanding of the underlying physics. However, for many important engineering applications insufficient data are available to justify the choice of a particular probability density function (PDF). Sometimes the only data available are in the form of interval estimates which represent, often conflicting, expert opinion. In this paper we demonstrate that Bayesian estimation techniques can successfully be used in applications where only vague interval measurements are available. The proposed approach is intended to fit within a probabilistic framework, which is established and widely accepted. To circumvent the problem of selecting a specific PDF when only little or vague data are available, a hierarchical model of a continuous family of PDF's is used. The classical Bayesian estimation methods are expanded to make use of imprecise interval data. Each of the expert opinions (interval data) are interpreted as random interval samples of a parent PDF. Consequently, a partial conflict between experts is automatically accounted for through the likelihood function.

• Journal of Korea Water Resources Association
• /
• v.43 no.7
• /
• pp.645-655
• /
• 2010

In this study, occurrences of relative probabilistic changing points between Chukwooki rainfall data (CWK) and modern rain gage data (MRG) were analyzed using Barry and Hartigan (BH) Bayesian changing points estimation method which estimated the changing points by calculation of change probabilities at each point. Since any natural phenomenon cannot be simulated identically and perfectly, a statistical method which can not consider the sequential order has its limitation on prediction of a specific time of occurrence. In this respect, Homogeneity analysis between CWK and MRG was performed through the occurrence investigation of relative probabilistic changing points for four rainfall characteristics of data sets using BH bayesian model which estimate the change point by calculating the relative probabilities in each data points. The results show that statistical characteristics of CWK are not different significantly from MRG, even though considered that there may be little quantitative difference CWK and MRG caused from limitation of measurement accuracy of CWK.

• Smart Structures and Systems
• /
• v.32 no.1
• /
• pp.61-81
• /
• 2023

This study presents an ensemble learning based Bayesian model updating approach for structural damage diagnosis. In the developed framework, the structure is initially decomposed into a set of substructures. The autoregressive moving average (ARMAX) model is established first for structural damage localization based structural motion equation. The wavelet packet decomposition is utilized to extract the damage-sensitive node energy in different frequency bands for constructing structural surrogate models. Four methods, including Kriging predictor (KRG), radial basis function neural network (RBFNN), support vector regression (SVR), and multivariate adaptive regression splines (MARS), are selected as candidate structural surrogate models. These models are then resampled by bootstrapping and combined to obtain an ensemble model by probabilistic ensemble. Meanwhile, the maximum entropy principal is adopted to search for new design points for sample space updating, yielding a more robust ensemble model. Through the iterations, a framework of surrogate ensemble learning based model updating with high model construction efficiency and accuracy is proposed. The specificities of the method are discussed and investigated in a case study.

• Korean Journal of Cognitive Science
• /
• v.27 no.4
• /
• pp.541-572
• /
• 2016

Causal reasoning is actively studied not only by psychologists but, in recent years, also by cognitive scientists taking the Bayesian approach. This paper seeks to provide an overview of the recent trends in causal reasoning research with a focus on the power probabilistic contrast theory of causality, a major psychological theory on causal inference. The power probabilistic contrast theory (PPCT) assumes that a cause is a power that initiates or inhibits the result. This power is purported be understood through statistical correlation under certain conditions. The paper examines the supporting empirical evidence in the development of PPCT. Also, introduced are the theoretical dispute between the PPCT and the model based on Bayesian approach, and the current developments and implications of research on causal invariance hypothesis, which states that cause operates identically regardless of the context. Recent studies have produced experimental results that cannot be readily explained by existing empirical approach. Therefore, these results call for serious examination of the power theory of causality by researchers in neighboring fields such as philosophy, statistics, and artificial intelligence.

• International Journal of Computer Science & Network Security
• /
• v.23 no.4
• /
• pp.85-94
• /
• 2023

Image halftoning is a technique for varying grayscale images into two-tone binary images. Unfortunately, the static representation of an image-half toning, wherever each pixel intensity is combined by its local neighbors only, causes missing subjective problem. Also, the existing noise causes an instability criterion. In this paper an image half-toning is represented as a dynamical system for recognizing the global representation. Also, noise is reduced based on a probabilistic model. Since image half-toning is considered as 2-D matrix with a full connected pass, this structure is recognized by the dynamical system of Cellular Neural Networks (CNNs) which is defined by its template. Bayesian Rough Sets is used in exploiting the ideal CNNs construction that synthesis its dynamic. Also, Bayesian rough sets contribute to enhance the quality of the halftone image by removing noise and discovering the effective parameters in the CNNs template. The novelty of this method lies in finding a probabilistic based technique to discover the term of CNNs template and define new learning rules for CNNs internal work. A numerical experiment is conducted on image half-toning corrupted by Gaussian noise.

• 한국지구물리탐사학회:학술대회논문집
• /
• 2003.11a
• /
• pp.340-343
• /
• 2003

This study presents a practical procedure for the Bayesian inversion of geophysical data by Markov chain Monte Carlo (MCMC) sampling and geostatistics. We have applied geostatistical techniques for the acquisition of prior model information, and then the MCMC method was adopted to infer the characteristics of the marginal distributions of model parameters. For the Bayesian inversion of dipole-dipole array resistivity data, we have used the indicator kriging and simulation techniques to generate cumulative density functions from Schlumberger array resistivity data and well logging data, and obtained prior information by cokriging and simulations from covariogram models. The indicator approach makes it possible to incorporate non-parametric information into the probabilistic density function. We have also adopted the MCMC approach, based on Gibbs sampling, to examine the characteristics of a posteriori probability density function and the marginal distribution of each parameter. This approach provides an effective way to treat Bayesian inversion of geophysical data and reduce the non-uniqueness by incorporating various prior information.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.36 no.4
• /
• pp.273-282
• /
• 2023

Probabilistic safety assessment (PSA) has been widely used to evaluate the seismic risk of nuclear power plants (NPPs). However, studies on seismic PSA for process plants, such as gas plants, oil refineries, and chemical plants, have been scarce. This is because the major disasters to which these process plants are vulnerable include explosions, fires, and release (or dispersion) of toxic chemicals. However, seismic PSA is essential for the plants located in regions with significant earthquake risks. Seismic PSA entails probabilistic seismic hazard analysis (PSHA), event tree analysis (ETA), fault tree analysis (FTA), and fragility analysis for the structures and essential equipment items. Among those analyses, ETA can depict the accident sequence for core damage, which is the worst disaster and top event concerning NPPs. However, there is no general top event with regard to process plants. Therefore, PSA cannot be directly applied to process plants. Moreover, there is a paucity of studies on developing fragility curves for various equipment. This paper introduces PSA for gas plants based on FTA, which is then transformed into Bayesian network, that is, a probabilistic graph model that can aid risk-informed decision-making. Finally, the proposed method is applied to a gas plant, and several decision-making cases are demonstrated.