• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.821-825
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• 2005

본 연구에서는 홍수조절 용 저수지의 예비방류 시행을 충분히 효과적으로 시행하고 강우종료 후에도 충분한 이수용량이 확보되도록 실시간 강우자료를 이용한 저수지 유입량 예측모형을 개발하였다. 사전예보(기상청 등)에 의한 총 예상강우량과 선행강우량, 현재 저수지 수위를 입력자료로 저수지 유입 총량과 수위변화량을 계산하여 홍수조절 응 저수지의 초기수위저하 및 하류 하도의 홍수방어를 사전에 대비할 수 있는 자료를 제시하였다. 또한, 유역을 하나의 통합시스템으로 구성하고 실제 강우가 시작되면 매시간 현시간 이후 강우가 중단된다는 가정 하에 현시점까지의 우량주상도를 통합시스템에 적용하여 이후 저수지 유입량을 예측하였다. 무한천 예당저수지에 적용하였으며 통합시스템의 구성은 저수지유역을 10개 소유역으로 분할하고 소유역별 홍수유출량은 Clark의 유역추적법, 하도구간은 Muskingum의 하도홍수추적 방법으로 계산되도록 하였다. 그리고 홍수유출시스템 내에는 강우관측소별 티센가중치에 따라 소유역별 평균강우량이 자동으로 입력되도록 하였으며, 예측정확도를 위해 현시간 이전까지 매시간마다 저수지의 수위변동과 실제 방류량으로부터 실측유입량을 산정하여 모형의 매개변수가 자동 보정되도록 하였다. 1995년 8월 23일$\~$8월 26일과 1999년 8월 2일$\~$8월 4일의 집중호우에 대하여 적용한 결과 모형의 예측정확도는 신뢰수준에 있었으며, 이와 같은 자료는 장수형 등(2005)이 제시한 효율적 저수지 운영관리 시스템과 하나로 통합되어 하류 하도의 통수능력을 고려한 홍수방어능력을 극대화한 예비방류의 시행과 강우종료 후에도 이수용량에는 손실이 없는 저수지의 관리방안의 지침이 되는데 효율적이라 판단되었다. 방법을 개발하여 개선시킬 필요성이 있다.>$4.3\%$로 가장 근접한 결과를 나타내었으며, 총 유출량에서도 각각 $7.8\%,\;13.2\%$의 오차율을 가지는 것으로 분석되어 타 모형에 비해 실유량과의 차가 가장 적은 것으로 모의되었다. 향후 도시유출을 모의하는 데 가장 근사한 유출량을 산정할 수 있는 근거가 될 것이며, 도시재해 저감대책을 수립하는데 기여할 수 있을 것이라 판단된다.로 판단되는 대안들을 제시하는 예비타당성(Prefeasibility) 계획을 수립하였다. 이렇게 제시된 계획은 향후 과학적인 분석(세부평가방법)을 통해 대안을 평가하고 구체적인 타당성(feasibility) 계획을 수립하는데 토대가 될 것이다.{0.11R(mm)}(r^2=0.69)$로 나타났다. 이는 토양의 투수특성에 따라 강우량 증가에 비례하여 점증하는 침투수와 구분되는 현상이었다. 경사와 토양이 같은 조건에서 나지의 경우 역시 $Ro_{B10}(mm)=20.3e^{0.08R(mm)(r^2=0.84)$로 지수적으로 증가하는 경향을 나타내었다. 유거수량은 토성별로 양토를 1.0으로 기준할 때 사양토가 0.86으로 가장 작았고, 식양토 1.09, 식토 1.15로 평가되어 침투수에 비해 토성별 차이가 크게 나타났다. 이는 토성이 세립질일 수록 유거수의 저항이 작기 때문으로 생각된다. 경사에 따라서는 경사도가 증가할수록 증가하였으며 $10\% 경사일 때를 기준으로 $Ro(mm)=Ro_{10}{\times}0.797{\times}e^{-0.021s(\%)}$로 나타났다.천성 승모판 폐쇄 부전등을 초래하는 심각한 선천성 심질환이다. 그러나 진단 즉시 직접 좌

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• Journal of Korean Society of Forest Science
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• v.112 no.3
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• pp.352-362
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• 2023

Harvest time is one of the most important determining factors of seed quality, especially for species that produce seeds over irregular and long-term periods, such as Larix kaempferi. A cone collection plan must be established to increase seed production efficiency and stable mass production. We investigated seed qualities such as seed efficiency, germination rate, and T₅₀ (germination speed), with 7 or 8 cone collection times at a clonal seed orchard of L. kaempferi in Chungju between 2021 and 2022. A multivariate analysis was then performed for the collected data. In early August, decreases in the moisture contents and browning of cones were observed. These were followed by a decrease in germination rate, with a peak at the end of September, but no clear trend was observed. The later the cones were harvested, the better the seed vigor (T₅₀). However, the seed yield and efficiency decreased owing to increases in seed scattering and the number of insect-damaged seeds. As a result, the optimal time of seed harvest for the seed orchard was in early August. To produce uniform seedlings, insect damage must be reduced through timely control and harvest cones in early September. This shows that the degree of browning and moisture content of cones can be used as indicators of the timing of cone collection in L. kaempferi seed orchards.