• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.194-194
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• 2012

본 연구에서는 자연하천에서 Rhodamine-WT를 이용하여 추적자 실험을 수행하고 이를 바탕으로 오염사고대응예측모델에 대한 검증을 실시하고자 하였다. 최근 국내에서는 다양한 형태와 규모의 수질오염사고가 매년 수십건 이상 발생하고 있으며, 따라서 수치모형 기반의 수질오염 사고 대응 예측시스템에 대한 높은 신뢰성이 요구되고 있다. 수질사고에 노출되어 있는 지표수를 각종 용수로서 안전하게 공급하기 위해서는 정확한 수질예측이 필수적이며, 이를 위해서 수십 년간 연구되어 온 수질모델을 오염사고 대응예측시스템에 적합하도록 정확성과 신뢰성을 갖추기 위한 연구가 진행되어야 한다. 수치 모형을 이용한 물질의 이송 및 확산 모의에서는 오염물질 도달시간과 확산 농도를 결정하는 것이 가장 중요한 요소이므로 이송 및 확산 모의에 대한 검증 기법 개발 및 적용이 필요하다. 본 연구에서는 낙동강수계 지류하천인 감천에서 추적자 실험을 4회 실시하여 측정한 수리량과 농도 실측치를 이용하여 분산계수를 종 횡분산계수 산정이 가능한 2차원 유관추적법을 적용하여 산정하였다. 각 단면에 유속은 ADV-3차원 유속계인 Flow-Tracker를 사용하여 도섭으로 측정하였으며 하천의 흐름 방향의 직각으로 측선을 설치하고 펌프를 이용하여 채수를 한 다음 Rhodamie WT의 농도를 측정하였으며 측선의 위치 보정은 GPS를 통하여 보정하였다. 측정 자료를 이용하여 2차원 유관추적법으로 분산계수를 산정한 결과 각각의 측선에 따라서 다소 차이가 발생하였으며, 일부 구간에서는 이론식으로 계산한 분산계수와 근사한 값이 나타났다. 이는 사주가 매우 발달하고 만곡이 많은 실험구간의 특성상 Elder와 Fischer 식으로 계산한 값과 차이가 발생할 가능성이 높은 구간이기 때문인 것으로 판단된다. 또한 하폭에 대한 수심비가 커질수록 분산계수도 증가하고 평균유속에 대한 전단유속에 비에 비례하는 것으로 나타났다.

• Water for future
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• v.23 no.3
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• pp.319-328
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• 1990

The Parameter Identification of 2-demensional estuarine model was carried out using new output ADI-FDM numerical semi-implicit schem transformed in boundary fitted(BF) - coordinate. The hydrodynamic equations which is coupled with the transport equations were used as basic equations in the model. Thompson's equations were used to transform governing equations into rectangular plane equations and his elliptic grid generation scheme was used to generate curvilinear grid system. in BF - coordinates. The parameters to be identified are friction coefficient and disperse coefficient embedded in the governing equations. The numerical output scheme is tidally averaged salinity model in BF - coordinates. The algorithm to optimize norm of error between observations and calculations is the influence coefficinet algorithm associated with least square criterion. The lumped model is conssidered in identification. This paper was concetrated on checking whether the new output scheme might be useful to identify parameters in estuarine salinity model or not. The proposed method was tested through experimental application with hypothetical simple model. The result of the test shows that the proposed method can be used for parameter identification in estuarine model.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.91-91
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• 2022

하천 합류부에서 수체의 흐름은 매우 역동적으로 변화하며 합류부의 복잡한 3차원 흐름과 난류 구조는 2차류(secondary currents)의 강도변화, 전단층(shear layer)의 뒤틀림 그리고 재순환구역(recirculation zone)의 발생 등 합류부에서의 독특한 특징을 형성한다. 이러한 특징들의 변화는 수체의 흐름구조 뿐만 아니라 하천으로 유입된 오염물의 거동에도 영향을 준다. 기존의 합류부 연구들은 주로 본류와 지류의 합류각이나 유량비에 차이를 두어 합류부의 특징 변화를 모의하였다. 하지만 실제 자연하천에서 홍수방지를 위한 수심확보, 건축자재의 골재수집 등 다양한 목적으로 수행되는 본류의 준설작업으로 인해 발생하는 본류와 지류의 하상면 단차 또한 합류부의 특성에 영향을 미치는 주요한 인자 중 하나이다. 단차가 커짐에 따라 증가하는 지류수체의 낙차는 이차류의 강화를 야기하며 이는 합류부에서의 유속구조를 변화시켜 흐름을 가속시키거나 지체시키며 오염물의 혼합에 영향을 미친다. 본 연구에서는 3차원 수치모의를 통해 90도로 합류되는 수로에서의 흐름구조와 오염물의 혼합에 단차비와 유량비가 미치는 영향을 모의하였다. 유동장 해석을 위해 3차원 RANS (Reynolds-averaged Navier-Stoke) 방정식을 사용하였으며 난류해석은 k-𝜔 SST 모델을 이용하였다. 본류의 경우 11.4m의 수로 연장을 갖고, 하폭은 0.3m이며 수심은 단차의 크기에 따라 변화한다. 지류의 경우는 수로연장 1m, 하폭 및 수로깊이는 0.1m이다. 수치결과의 검증을 위해 이주하(2013)이 수행한 실내 합류수로의 실험결과를 이용하였다. 모의결과를 통해 파악한 합류부의 흐름특성을 이용하여 적절한 2차원 분산계수를 산정한다. 자연하천에서 오염물의 혼합거동을 효과적으로 모의하기 위해 수심 평균된 2차원 이송-분산모형을 이용하는데 이때 적절한 분산계수의 산정이 필수적이다. 본 연구에서는 합류 후 흐름방향에 따라 분산특성이 상이한 구간을 구분하여 분산계수를 산정하였으며 이를 통해 오염물의 거동을 정확하게 모의하였다.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1033-1043
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• 2011

In this paper, we use a nested family of models of Generalized Autoregressive Conditional Heteroscedasticity(GARCH) to verify asymmetric conditional heteroscedasticity in the KOSPI and Won-Dollar exchange rate. This study starts from an investigation of whether time series data have asymmetric features not explained by standard GARCH models. First, we use kernel density plot to show the non-normality and asymmetry in data as well as to capture asymmetric conditional heteroscedasticity. Later, we use three representative asymmetric heteroscedastic models, EGARCH(Exponential Garch), GJR-GARCH(Glosten, Jagannathan and Runkle), APARCH(Asymmetric Power Arch) that are improved from standard GARCH models to give a better explanation of asymmetry. Thereby we highlight the fact that volatility tends to respond asymmetrically according to positive and/or negative values of past changes referred to as the leverage effect. Furthermore, it is verified that how the direction of asymmetry is different depending on characteristics of time series data. For the KOSPI and Korean won-US dollar exchange rate, asymmetric heteroscedastic model analysis successfully reveal the leverage effect. We obtained predictive values of conditional volatility and its prediction standard errors by using moving block bootstrap.

• Journal of Wetlands Research
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• v.13 no.3
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• pp.385-394
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• 2011

In this study, optimal parameters of diffusion-analogy GIUH were calculated by separating channel and hillslope from drainage structures in the basin. Parameters of the model were composed of channel and hillslope, each velocity($u_c$, $u_h$) and diffusion coefficient($D_c$, $D_h$). Tanbu subwatershed in Bocheong river basin as a target basin was classified as 4th rivers by Strahler's ordering scheme. The optimization technique was applied to the SCE-UA, the estimated optimal parameters are as follows. $u_c$ : 0.589 m/s, $u_h$ : 0.021 m/s, $D_c$ : $34.469m^2/s$, $D_h$ : $0.1333m^2/s$. As a verification for the estimated parameters, the error of average peak flow was about 11 % and the error of peaktime was 0.3 hr. By examining the variability of parameters, the channel diffusion coefficient didn't have significant effect on hydrological response function. by considering these results, the model is expected to be simplified in the future.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.3
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• pp.125-140
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• 2017

Until recently, regression based spatial interpolation methods and Kriging based spatial interpolation methods have been largely used to estimate land price or housing price, but less attention has been paid on comparing the performance of these spatial interpolation methods. In this regard, this research applied regression based spatial interpolators and Kriging based spatial interpolators for estimating the land prices in Dalseo-gu, Daegu metropolitan city and evaluated the accuracy of eight spatial interpolators. OLS, SLM, SEM, and GWR were used as regression based spatial interpolators while SK, OK, UK, and CK were employed as Kriging based spatial interpolators. The global accuracy was statistically evaluated by RMSE, adjusted RMSE, and COD. The relative accuracy was visually compared by three-dimensional residual error map and scatterplot. Results from statistical and visual analyses indicate that GWR reflecting the spatial non-stationarity was a relatively more accurate spatial predictor to estimate land prices in the study area than SAR and Kriging based spatial interpolators considering the spatial dependence. The findings from this research will contribute to the secondary research into analyzing the urban spatial structure with land prices.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.117-117
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• 2023

도시 홍수, 하천 범람, 산사태와 같은 폭우와 관련된 재해는 자주 동시에 발생하며, 각 재해는 서로 다른 범위의 시간 스케일에서 강우 변동성에 민감하게 반응한다. 따라서 재해 복합화 모델링에 적합한 확률 강우 모델은 모든 유형의 재해와 관련된 모든 시간 스케일에서 강우 변동성을 잘 재현할 수 있어야 한다. 본 연구에서는 5분에서 10년 사이의 시간 스케일에서 다양한 강우통계특성을 재현할 수 있는 추계학적 강우 생성기를 제안하였다. 이 모델은 우선 Randomized Bartlett-Lewis Rectangular Pulse (RBLRP) 모델을 사용하여 미세 규모의 강우량 시계열을 생성한 후, 연속된 폭풍 사이의 상관관계 구조가 유지되도록 폭풍우의 순서를 섞는다. 마지막으로, 별도의 월별 강우량 모델링 결과에 따라 월 단위로 시계열을 재배열한다. 독일 보훔에서 기록된 69년간의 5분 강우량 데이터를 사용하여 본 모형을 검증한 결과, 평균, 분산, 공분산, 왜곡도 및 강우 간헐성은 5분에서 10년에 이르는 시간 스케일에서 체계적인 편향 없이 잘 재현됨은 물론, 5분에서 3일 사이의 시간 스케일에서의 극한 강수량 값도 잘 재현음을 확인하였다. 아울러, 극한 강우 및 산사태에 큰 영향을 주는 극한 강우 발생 전 과거 7일간의 강수량도 정확히 재현되었다.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.39-55
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• 2019

Recently banks and large financial institutions have introduced lots of Robo-Advisor products. Robo-Advisor is a Robot to produce the optimal asset allocation portfolio for investors by using the financial engineering algorithms without any human intervention. Since the first introduction in Wall Street in 2008, the market size has grown to 60 billion dollars and is expected to expand to 2,000 billion dollars by 2020. Since Robo-Advisor algorithms suggest asset allocation output to investors, mathematical or statistical asset allocation strategies are applied. Mean variance optimization model developed by Markowitz is the typical asset allocation model. The model is a simple but quite intuitive portfolio strategy. For example, assets are allocated in order to minimize the risk on the portfolio while maximizing the expected return on the portfolio using optimization techniques. Despite its theoretical background, both academics and practitioners find that the standard mean variance optimization portfolio is very sensitive to the expected returns calculated by past price data. Corner solutions are often found to be allocated only to a few assets. The Black-Litterman Optimization model overcomes these problems by choosing a neutral Capital Asset Pricing Model equilibrium point. Implied equilibrium returns of each asset are derived from equilibrium market portfolio through reverse optimization. The Black-Litterman model uses a Bayesian approach to combine the subjective views on the price forecast of one or more assets with implied equilibrium returns, resulting a new estimates of risk and expected returns. These new estimates can produce optimal portfolio by the well-known Markowitz mean-variance optimization algorithm. If the investor does not have any views on his asset classes, the Black-Litterman optimization model produce the same portfolio as the market portfolio. What if the subjective views are incorrect? A survey on reports of stocks performance recommended by securities analysts show very poor results. Therefore the incorrect views combined with implied equilibrium returns may produce very poor portfolio output to the Black-Litterman model users. This paper suggests an objective investor views model based on Support Vector Machines(SVM), which have showed good performance results in stock price forecasting. SVM is a discriminative classifier defined by a separating hyper plane. The linear, radial basis and polynomial kernel functions are used to learn the hyper planes. Input variables for the SVM are returns, standard deviations, Stochastics %K and price parity degree for each asset class. SVM output returns expected stock price movements and their probabilities, which are used as input variables in the intelligent views model. The stock price movements are categorized by three phases; down, neutral and up. The expected stock returns make P matrix and their probability results are used in Q matrix. Implied equilibrium returns vector is combined with the intelligent views matrix, resulting the Black-Litterman optimal portfolio. For comparisons, Markowitz mean-variance optimization model and risk parity model are used. The value weighted market portfolio and equal weighted market portfolio are used as benchmark indexes. We collect the 8 KOSPI 200 sector indexes from January 2008 to December 2018 including 132 monthly index values. Training period is from 2008 to 2015 and testing period is from 2016 to 2018. Our suggested intelligent view model combined with implied equilibrium returns produced the optimal Black-Litterman portfolio. The out of sample period portfolio showed better performance compared with the well-known Markowitz mean-variance optimization portfolio, risk parity portfolio and market portfolio. The total return from 3 year-period Black-Litterman portfolio records 6.4%, which is the highest value. The maximum draw down is -20.8%, which is also the lowest value. Sharpe Ratio shows the highest value, 0.17. It measures the return to risk ratio. Overall, our suggested view model shows the possibility of replacing subjective analysts's views with objective view model for practitioners to apply the Robo-Advisor asset allocation algorithms in the real trading fields.

• Management & Information Systems Review
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• v.33 no.1
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• pp.139-155
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• 2014

This study is to examine the linkage of volatility between changes in the stock market of India and other countries through the integration of the world economy. The results were as follows: First, autocorrelation or serial correlation did not exist in the classic RS model, but long-term memory was present in the modified RS model. Second, unit root did not exist in the unit root test for all periods, and the series were a stable explanatory power and a long-term memory with the normal conditions in the ARFIMA model. Third, in the multivariate asymmetric BEKK and VAR model before the financial crisis, it showed that there was a strong influence of the own market of Taiwan and UK in the conditional mean equation, and a strong spillover effect from Japan to India, from Taiwan to China(Korea, US), from US(Japan) to UK in one direction. In the conditional variance equation, GARCH showed a strong spillover effect that indicated the same direction as the result of ARCH coefficient of the market itself. Asymmetric effects in three home markets and between markets existed. Fourth, after the financial crisis, in the conditional mean equation, only the domestic market in Taiwan showed strong influences, and strong spillover effects existed from India to US, from Taiwan to Japan, from Korea to Germany in one direction. In the conditional variance equation, strong spillover effects were the same as the result of the pre-crisis and asymmetric effect in the domestic market in UK was present, and one-way asymmetric effect existed in Germany from Taiwan. Therefore, the results of this study presented the linkage between the volatilities of the stock market of India and other countries through the integration of the world economy, observing and confirming the asymmetric reactions and return(volatility) spillover effects between the stock market of India and other countries.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1171-1180
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• 2010

It is well known that the asymptotic convergence rates of nonparametric regression estimator gets worse as the dimension of covariates gets larger. One possible way to overcome this problem is reducing the dimension of covariates by using single index models. Two coefficient estimation methods in single index models are introduced. One is semiparametric least square estimation method, which tries to find approximate solution by using iterative computation. The other one is weighted average derivative estimation method, which is non-iterative method. Both of these methods offer the parametric convergence rate to normal distribution. However, practical comparison of these two methods has not been done yet. In this article, we compare these methods by examining the variances of estimators in various models.