• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.140-140
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• 2021

Urban flood management is a crucial and challenging task, particularly in developed cities. Therefore, accurate prediction of urban flooding under heavy precipitation is critically important to address such a challenge. In recent years, machine learning techniques have received considerable attention for their strong learning ability and suitability for modeling complex and nonlinear hydrological processes. Moreover, a survey of the published literature finds that hybrid computational intelligent methods using nature-inspired algorithms have been increasingly employed to predict or simulate the streamflow with high reliability. The present study is aimed to propose a novel approach, an ensemble tree, Bayesian Additive Regression Trees (BART) model incorporating a nature-inspired algorithm to predict hourly multi-step ahead streamflow. For this reason, a hybrid intelligent model was developed, namely GA-BART, containing BART model integrating with Genetic algorithm (GA). The Jungrang urban basin located in Seoul, South Korea, was selected as a case study for the purpose. A database was established based on 39 heavy rainfall events during 2003 and 2020 that collected from the rain gauges and monitoring stations system in the basin. For the goal of this study, the different step ahead models will be developed based in the methods, including 1-hour, 2-hour, 3-hour, 4-hour, 5-hour, and 6-hour step ahead streamflow predictions. In addition, the comparison of the hybrid BART model with a baseline model such as super vector regression models is examined in this study. It is expected that the hybrid BART model has a robust performance and can be an optional choice in streamflow forecasting for urban basins.

• 대한안전경영과학회지
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• 제13권1호
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• pp.91-98
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• 2011

We develop a model to predict traffic accidents in Korea. In contrast to the classical approach that mainly uses regression analysis, Bayesian approach is adopted. A dependent model that incorporates the data from different kinds of accidents is introduced. The rate of severe accident can be updated even with no data of the same kind. The data of minor accident that can be obtained frequently is efficiently used to predict the severe accident.

• 한국경영과학회지
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• 제16권1호
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• pp.113-119
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• 1991

The author is currently assistant professor of Management Science at Korea Advanced Institute of Science and Technology, following a few years as assistant professor of Industrial Engineering at Kyung Hee University, Korea. He received his doctorate from the department of Industrial Engineering and Operations Research, University of California, Berkeley. His research interests are time series and forecasting modelling, Bayesian forecasting and the related software development. He is now teaching time series analysis and econometrics at the graduate level.

• 한국항만경제학회지
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• 제30권2호
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• pp.77-92
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• 2014

본 연구는 2000~2013년까지의 월별 시계열 자료를 이용하여 실물 금융변수와 해운경기간의 동태적 상관관계를 분석한다. 특히, 2008년 글로벌위기 이후 운임지수의 지속적인 하락국면에서 실물 금융변수가 얼마만큼의 영향을 미쳤는가를 중심으로 분석하였다. 모형의 적합성과 예측력 비교를 위해 기존의 일반적인 VAR 모형과 베이지안 VAR를 비교하였으며, VAR 모형 설정에 있어 외생성을 보다 객관적으로 도출하기 위해 DAG(Directed Acyclic Graph)를 활용하여 충격반응분석을 실시하고 각각의 모형에 대한 예측력을 비교하였다. 분석결과 BDI에 대한 금융 실물 부문의 영향에 대하여 베이지안 VAR 모형의 충격반응분석 결과는 일반적인 VAR 모형보다 명확하게 드러났으며, 두 모형 간의 예측력을 검정한 결과 베이지안 VAR 모형의 예측력이 매우 우월한 것으로 나타났다.

• 한국지형학회지
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• 제24권2호
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• pp.91-101
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• 2017

Quantitative forecasting methods based on spatial data and geographic information system have been used in predicting the landslide location. This study compared the simulated results of logistic, Bayesian, and maximum entropy models to understand the uncertainties of each model and identify the main factors that influence landslide. The study area is Boeun gun where 388 landslides occurred in the year of 1998. The verification results showed that the AUC of the three models was 0.84. However, the landslide susceptibility distribution of Maxent model was different from those of the other two models. With the same landslide occurrence data, the result of high susceptible area in Maxent model is smaller than Logistic or Bayesian. Maxent model, however, proved to be more efficient in predicting landslide than the other two models. In Maxent's simulations, the responsible factors for landslide susceptibility are timber age class, land cover, timber diameter, crown closure, and soil drainage. The results suggest that it is necessary to consider the possibility of overestimation when using Logistic or Bayesian model, and forest management around the study area can be an effective way to minimize landslide possibility.

• 한국경영과학회지
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• 제30권3호
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• pp.137-149
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• 2005

This study proposes a framework enhancing the accuracy of estimation for project duration by combining linear Bayesian updating scheme with the learning curve effect. Activities in a particular project might share resources in various forms and might be affected by risk factors such as weather Statistical dependence stemming from such resource or risk sharing might help us learn about the duration of upcoming activities in the Bayesian model. We illustrate, using a Monte Carlo simulation, that for partially repetitive projects a higher degree of statistical dependence among activity duration results in more variation in estimating the project duration in total, although more accurate forecasting Is achievable for the duration of an individual activity.

• 대한산업공학회지
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• 제24권4호
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• pp.485-492
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• 1998

Most forecasting models often fail to produce appropriate forecasts because we build a model based on the assumption of the data being generated from the only one stochastic process. However, in many real problems, the time series data are generated from one stochastic process for a while and then abruptly undergo certain structural changes. In this paper, we assume the basic underlying process is the simple state-space model with random level and deterministic drift but interrupted by three types of exogenous shocks: level shift, drift change, outlier. A Bayesian procedure to detect, estimate and adapt to the structural changes is developed and compared with simple, double and adaptive exponential smoothing using simulated data and the U.S. leading composite index.

• Journal of Information Technology Applications and Management
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• 제26권2호
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• pp.61-73
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• 2019

In 2019, 5G mobile communication technology will be commercialized. From the viewpoint of technological innovation, 5G service can be applied to other industries or developed further. Therefore, it is important to measure the demand of the Internet of things (IoT) because it is predicted to be commercialized widely in the 5G era and its demand hugely effects on the economic value of 5G industry. In this paper, we applied Bayesian method on regression model to find out the demand of 5G IoT service, wearable service in particular. As a result, we confirmed that the Bayesian regression model is closer to the actual value than the existing regression model. These findings can be utilized for predicting future demand of new industries.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2019년도 학술발표회
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• pp.309-309
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• 2019

기후예측모델을 통해 일단위 강수의 예측정보가 제공되고 있지만, 실제 강수량자료와 시공간적 편의로 인해 수문학적 활용은 한계가 있다. 일반적으로 기후모델의 시공간적 해석 규모 및 예측정확성을 고려할 때 계절단위에서 예측정보의 활용이 가장 현실적인 것으로 알려지고 있다. 그러나 수문해석 시 시공간적 해상도가 낮아 직접적인 활용은 어려운 상황이며, 수문해석 모형의 입력자료로 활용 시 편의보정 및 상세화 과정이 일반적으로 요구된다. 본 연구에서는 기후모델로부터 얻은 강우예측결과에 Bayesian 모델 기반의 편의보정-상세화 기법을 개발하여 강우예측정보의 활용성을 개선하고자 한다. 이 과정에서 Bayesian Copula 모델을 이용한 이변량 형태의 예측강수의 검보정 방법을 개발하였으며, 특히 기후모델 이외의 기상 상태변량인 해수면온도(sea surface temperature, SST)를 예측인자로 추가하여 Hybrid 형태의 계절 앙상블 강우예측모델을 개발하고자 한다.

• Journal of the Korean Statistical Society
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• 제7권1호
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• pp.11-16
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• 1978

Bayesian procedures are in vogue to revise the parameter estimates of the forecasting model in the light of actual time series data. In this paper, we study the Bayes forecast for demand and the risk when (a) 'noise' and (b) mean demand rate in a constant process model have moderately non-normal probability distributions.