• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.100-100
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• 2018

본 연구에서는 집중호우에 대한 레이더기반 초단기 강우예측 시스템의 정확도를 향상시키기 위해 초기장 개선 연구를 수행하였다. 집중호우에 적합한 강우를 추정하기 위해 층운형, 대류형, 열대형의 Z-R관계식과 반사도 조건에 따라 층운형과 적운형을 구분하여 Z-R 관계식을 적용하였으며, 이를 초단기 강우예측 시스템의 초기장으로 활용하였다. 또한 2016년 10월 5일 태풍 차바(Chaba)에 의한 집중호우 사례에 대해 지상관측 강우자료와 레이더 추정 및 예측 강우자료와의 비교를 통해 정확도를 정성적 정량적으로 평가하였다. 레이더 강우추정에 대한 분석 결과, 복합형 타입의 Z-R 관계식의 상관계수와 평균제곱근오차가 비슬산레이더의 경우 각각 0.8207, 9.22 mm/hr, 면봉산 레이더의 경우 각각 0.8001, 10.53 mm/hr로 가장 좋은 성능을 보였다. 강우 예측에 대한 분석 결과, 집중호우 사례에 대해 강우강도 공간분포 및 이동 패턴은 평균적으로 잘 모의하였으며, 초단기 강우예측 결과의 평균적으로 POD는 0.97이상, FAR는 0.21 이하로 다소 정확하게 예측하는 것으로 분석되었다. 정량적 평가 결과, 비슬산 레이더의 경우 상관계수가 예측시간 60분까지 0.545이상, 면봉산 레이더의 경우 0.379 이상으로 비교적 좋은 상관성을 보였으며, Z-R관계식 유형에 따른 차이는 작은 것으로 확인되었다. 평균제곱근오차의 경우 열대형과 복합형의 Z-R관계식이 높은 정확도를 가지는 것으로 확인되었다. 본 연구 결과, 초기장 정확도의 개선을 통한 레이더 기반 초단기 강우예측 모형의 정확도 개선 가능성을 확인할 수 있었으며, 향후 지속적인 사례연구 및 검증을 통하여 강우추정 및 강우예측 알고리즘 개선의 노력이 필요하다.

• Journal of the Earthquake Engineering Society of Korea
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• v.1 no.2
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• pp.1-15
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• 1997

This paper presents substructural identification methods for the assessment of local damages in complex and large structural systems. For this purpose, an auto-regressive and moving average with stochastic input (ARMAX) model is derived for a substructure to process the measurement data impaired by noises. Using the substructural methods, the number of unknown parameters for each identification can be significantly reduced, hence the convergence and accuracy of estimation can be improved. Secondly, the damage index is defined as the ratio of the current stiffness to the baseline value at each element for the damage assessment. The indirect estimation method was performed using the estimated results from the identification of the system matrices from the substructural identification. To demonstrate the proposed techniques, several simulation and experimental example analyses are carried out for structural models of a 2-span truss structure, a 3-span continuous beam model and 3-story building model. The results indicate that the present substructural identification method and damage estimation methods are effective and efficient for local damage estimation of complex structures.

• The Korean Journal of Applied Statistics
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• v.17 no.1
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• pp.105-117
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• 2004

In this paper, a new method is developed for estimating the mean of a population which has a linear trend. This method involves drawing a sample by the modified systematic sampling, and then estimating the population mean with an adjusted estimator, not with the sample mean itself. We use the method of least squares in determining the adjusted estimator. The proposed method is shown to be more and more efficient as the linear trend becomes stronger. It turns out to be relatively efficient as compared with the conventional methods if $\sigma$$^2$the variance of the random error term in the infinite superpopulation model, is not very large.

• Journal of the Korea Computer Industry Society
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• v.5 no.5
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• pp.755-760
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• 2004

In this paper, an advanced successive elimination algorithm was proposed using mean absolute difference of neighboring search points. By using mean absolute difference of neighboring search points, the search point in motion estimation can be eliminated effeciently without matching evaluation that requires very intensive computations. By using adaptive MAD calculation algorithm, the candidate matching block can be eliminated early. So, the number of the proposed algrorithm was verified by experimental results.

• Journal of Korea Water Resources Association
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• v.56 no.8
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• pp.509-520
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• 2023

Various drought indices are widely used for assessing drought conditions which are affected by many factors such as precipitation, soil moisture, and runoff. The values of drought indices varies depending on hydro-meteorological data and calculation formulas, and the judgment of the drought condition may also vary. This study selected four calculation components such as precipitation data length, accumulation period, probability distribution function, and parameter estimation method as the sources of uncertainty in the calculation of standardized precipitation index (SPI), and evaluated their contributions to the uncertainty using root mean square error (RMSE) and linear mixed model (LMM). The RMSE estimated the overall errors in the SPI calculation, and the LMM was used to quantify the uncertainty contribution of each factor. The results showed that as the accumulation period increased and the data period extended, the RMSEs decreased. The comparison of relative uncertainty using LMM indicated that the sample size had the greatest impact on the SPI calculation. In addition, as sample size increased, the relative uncertainty related to the sample size used for SPI calculation decreased and the relative uncertainty associated with accumulation period and parameter estimation increased. In conclusion, to reduce the uncertainty in the SPI calculation, it is essential to collect long-term data first, followed by the appropriate selection of probability distribution models and parameter estimation methods that represent well the data characteristics.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.9
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• pp.5585-5593
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• 2014

This study tested the hypothesis that the significance of the heterogeneous dispersion parameter in safety performance function (SPF) used to estimate the expected crashes is affected by the endogenous heterogeneous prior distributions, and analyzed the impacts of the mis-specified dispersion parameter on the evaluation results for traffic safety countermeasures. In particular, this study simulated the Poisson means based on the heterogeneous dispersion parameters and estimated the SPFs using both the negative binomial (NB) model and the heterogeneous negative binomial (HNB) model for analyzing the impacts of the model mis-specification on the mean and dispersion functions in SPF. In addition, this study analyzed the characteristics of errors in the crash reduction factors (CRFs) obtained when the two models are used to estimate the posterior means and variances, which are essentially estimated through the estimated hyper-parameters in the heterogeneous prior distributions. The simulation study results showed that a mis-estimation on the heterogeneous dispersion parameters through the NB model does not affect the coefficient of the mean functions, but the variances of the prior distribution are seriously mis-estimated when the NB model is used to develop SPFs without considering the heterogeneity in dispersion. Consequently, when the NB model is used erroneously to estimate the prior distributions with heterogeneous dispersion parameters, the mis-estimated posterior mean can produce large errors in CRFs up to 120%.

• Korean Journal of Agricultural and Forest Meteorology
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• v.3 no.2
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• pp.96-104
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• 2001

Usage of ecosystem models has been extended to landscape scales for understanding the effects of environmental factors on natural and agro-ecosystems and for serving as their management decision tools. Accurate prediction of spatial variation in daily temperature is required for most ecosystem models to be applied to landscape scales. There are relatively few empirical evaluations of landscape-scale temperature prediction techniques in mountainous terrain such as Korean Peninsula. We derived a periodic function of seasonal lapse rate fluctuation from analysis of elevation effects on daily temperatures. Observed daily maximum and minimum temperature data at 63 standard stations in 1999 were regressed to the latitude, longitude, distance from the nearest coastline and altitude of the stations, and the optimum models with $r^2$ of 0.65 and above were selected. Partial regression coefficients for the altitude variable were plotted against day of year, and a numerical formula was determined for simulating the seasonal trend of daily lapse rate, i.e., partial regression coefficients. The formula in conjunction with an inverse distance weighted interpolation scheme was applied to predict daily temperatures at 267 sites, where observation data are available, on randomly selected dates for winter, spring and summer in 2000. The estimation errors were smaller and more consistent than the inverse distance weighting plus mean annual lapse rate scheme. We conclude that this method is simple and accurate enough to be used as an operational temperature interpolation scheme at landscape scale in Korea and should be applicable to elsewhere.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.701-705
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• 2006

강우자료는 수문 모델링 작업에서 가장 기초적인 수문학적 입력자료로 시간과 공간에 따른 변동성이 크므로 규명하기 복잡한 수문현상 중의 하나이다. 산악지역이 많은 우리나라의 지형학적 특성과 태풍, 장마 및 특히, 최근의 게릴라성 집중호우 등으로 인하여 이러한 변동성이 더욱 커지고 있는 실정이다. 장기간 실측된 수문기상 기초 자료가 부족한 우리나라의 실정상 홍수예보 및 수공구조물 설계를 위해 정확한 강우량 자료의 취득이 선행돼야 한다. 따라서 적절한 장소에 수문관측소 설치 및 관리를 통해 양호한 강우량 자료를 획득해야 하지만, 현장 여건상 등의 이유로 미계측 및 결측, 이상자료가 발생하고 있다. 따라서 이러한 미계측 혹은 결측지점의 우량을 추정할 수 있는 방법을 비교, 분석하여 적절한 보정과정을 수행할 필요가 있다. 그간의 연구에서는 미계측 지점 혹은 산악지역에서의 점 강우량 보정방법에 대한 연구가 진행되었지만, 본 연구에서는 '도시홍수재해관리기술연구사업단'에서 운영 중인 도시하천 유역 특히 소배수구역에서의 결측 자료에 대해 여러 추정 방법을 비교, 분석하여 적절한 방안을 찾고자 한다. 이를 위하여 중랑천 유역의 3개 소배수 구역(월계1 배수구역, 군자 배수구역, 어린이대공원 배수구역)에 설치된 3개 우량관측소와 건설교통부 관할 우량관측소 2개소의 우량자료를 사용하였다. 본 연구에서는 결측치 보간을 위하여 널리 이용되고 있는 산술평균법(Arithmetic Average method), 역거리법(Reciprocal Distance Squared method), 거리고도비율법(Ratio of Distance and Elevation method), 인근관측소와의 관계식 이용, 크리깅방법(Simple Kriging method)을 비교, 검토 적용하였다. 중랑천 유역의 소배수구역을 대상으로 연중 발생하는 큰 호우사상에 대해 임의의 강우관측소를 결측지점으로 가정하고 주변의 강우관측소로부터 각각의 방법을 이용해 가중치들을 산정하여 결측지점의 강우량 값을 보정하고자 하였다. 또한 각각의 방법을 이용하여 얻어진 결과에 대해 실측값과 보정값의 오차정도를 평균절대오차법(Mean Absolute Error)과 제곱평균제곱근오차법(Root Mean Squared Error)에 의해 산정하여 보정 방법간의 효율성을 검토하고자 하였다.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.513-522
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• 2014

Using the assumption that the price of a stock follows a geometric Brownian motion with constant volatility, Black and Scholes (BS) derived a formula that gives the price of a European call option on the stock as a function of the stock price, the strike price, the time to maturity, the risk-free interest rate, the dividend rate paid by the stock, and the volatility of the stock's return. However, implied volatilities of BS method tend to depend on the stock prices and the time to maturity in practice. To address this shortcoming, we estimate the implied volatility function as a function of the strike priceand the time to maturity for data consisting of the daily prices for KOSPI200 call options from January 2007 to May 2009 using support vector regression (SVR), the multiple additive regression trees (MART) algorithm, and ordinary least squaress (OLS) regression. In conclusion, use of MART or SVR in the BS pricing model reduced both RMSE and MAE, compared to the OLS-based BS pricing model.

• Survey Research
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• v.12 no.3
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• pp.49-62
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• 2011

In this paper we investigate design-based properties of both the ordinary least square estimator and the weighted least square estimator for regression coefficients in panel regression model. We derive formulas of approximate bias, variance and mean square error for the ordinary least square estimator and approximate variance for the weighted least square estimator after linearization of least square estimators. Also we compare their magnitudes each other numerically through a simulation study. We consider a three years data of Korean Welfare Panel Study as a finite population and take household income as a dependent variable and choose 7 exploratory variables related household as independent variables in panel regression model. Then we calculate approximate bias, variance, mean square error for the ordinary least square estimator and approximate variance for the weighted least square estimator based on several sample sizes from 50 to 1,000 by 50. Through the simulation study we found some tendencies as follows. First, the mean square error of the ordinary least square estimator is getting larger than the variance of the weighted least square estimator as sample sizes increase. Next, the magnitude of mean square error of the ordinary least square estimator is depending on the magnitude of the bias of the estimator, which is large when the bias is large. Finally, with regard to approximate variance, variances of the ordinary least square estimator are smaller than those of the weighted least square estimator in many cases in the simulation.