• Proceedings of the Korea Water Resources Association Conference
• /
• 2009.05a
• /
• pp.1321-1326
• /
• 2009

강우는 수자원 확보 측면에서 근원이 되는 요소이다. 그러므로 정확한 확률강우량 산정은 미래의 가용 수자원량을 예측하는데 있어 중요한 사항중 하나이며 무엇보다 신중한 결정이 요구된다. 또한 하천의 범람에 의한 침수를 예방하는 수공구조물 등의 설계에 있어서는 신뢰할 수 있는 확률강우량 산정이 선행되어야 한다. 본 연구에서는 최근 우리나라 극치강우확률분포로서 많은 연구가 이루어지고 있는 GEV 분포(GEV-O)를 기반으로 위치 매개변수에 시간의 함수를 고려한 개선된 GEV 분포(GEV-A)를 이용하여 서울지점에 적용함으로서 GEV-O 분포에 의한 확률강우량과 GEV-A 분포로 산정된 확률강우량을 비교 검토하였다. 먼저 임의의 난수 발생을 통해 최우도추정법과 확률가중모멘트법으로 매개변수를 추정한 GEV-O 분포와 최우도추정법으로 매개변수를 추정한 GEV-A 분포의 상대평균제곱근오차 (R-RMSE)를 계산하여 비교함으로서 GEV-A 분포의 효율성을 판단하였다. 사례연구는 1961년부터 2008년까지 서울강우관측소에서 측정된 연최대 1일 강우량으로 하였으며 $X^2$-검정, PPCC-검정으로 적합도 검정을 실시하였다. 강우빈도분석 결과 GEV-A 분포가 GEV-O 분포로 산정된 결과 보다 대체로 재현기간 200년 이상일 경우, 과다 산정되는 경향을 보였다. 추후 개선된 GEV 분포를 서울 인근 지점에 적용함으로서 지역빈도해석(Regional Frequency Analysis)을 실행하기 위한 연구가 진행되어야 할 것이다. 또한 확률홍수량 산정 등에도 개선된 GEV 분포를 이용함으로서 보다 정확하고 신뢰성 있는 확률수문량을 예측하여야 할 것이다.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.28 no.2
• /
• pp.177-185
• /
• 2015

City-based Lifeline is expected to cause significant social and economic loss accompanied the secondary damage such as paralysis of urban functions and a large fire as well as the collapse caused by earthquake. Earthquake Disaster Response System of Korea is being operated with preparation, calculates the probability of failure of the facility through Seismic Fragility Model and evaluates the degree of earthquake disaster. In this paper, the time history analysis of buried gas pipeline in city-based lifeline was performed with consideration for ground characteristics and also seismic fragility model was developed by maximum likelihood estimation method. Analysis model was selected as the high-pressure pipe and the normal-pressure pipe buried in the city of Seoul, Korea's representative, modeling of soil was used for Winkler foundation model. Also, method to apply developed fragility model at GIS is presented.

• Journal of the Earthquake Engineering Society of Korea
• /
• v.12 no.3
• /
• pp.37-44
• /
• 2008

This study investigates the influence of various soil properties on the seismic performance of a three-span FCM bridge. Piers that are vulnerable to seismic vibration are identified through numerical study of plastic hinges possibly occurring at the top and bottom of the piers. The fragility curve is obtained as a lognormal distribution function with respect to peak ground acceleration(PGA). The median and logarithmic standard deviation, which are two parameters of a lognormal distribution function, are estimated using the maximum likelihood method. In order to consider the different soil properties of each support, an equivalent spring based on the Korean Standard Specifications for Highway Bridges(KSSHB) is adopted in this study. For seismic fragility analysis, the rotational ductility demands of bridge piers are used as a damage index of the structure.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.11 no.5
• /
• pp.107-113
• /
• 2007

This study presents fragility curves for the wind fragility analysis of a six-span extradosed bridge. The loads and corresponding load combinations are calculated using domestic design codes. Random variables are utilized to considering the uncertainties of the input variables for wind loads. The fragility curve is represented as a log-normal distribution function, in which two parameters are estimated by the maximum likelihood method. The results show that the extradosed bridge is safe to suffer static wind forces.

• Journal of Korea Water Resources Association
• /
• v.43 no.10
• /
• pp.893-909
• /
• 2010

The purpose of this study is to consider the interaction between surface water and groundwater in basin scale by developing TANK_GS model. The soil moisture structure of tank model with 3 tanks is improved to simulate the appropriate stream-aquifer interactions. Maximum likelihood method is applied to calibrate parameters with variance functions to deal with heteroscedasticity of residuals. The parameters of improved TANK_GS model and variance function are simultaneously estimated by Simulated Annealing method, a global optimization technique. The results of TANK-GE are compared to those of the SWMM-GE model which had been developed to consider the stream-aquifer interactions. The new TANK_GS model and SWMM-GE model are applied to Gapcheon basin, which belongs to Geum River basin. TANK_GS model showed better model performance compared to the original TANK model and characterized the relationship of stream-aquifer interactions as satisfactorily as the SWMM-GE model. The sustainable groundwater yield can be estimated for the regional water resources planning using the TANK_GS model

• Journal of Korean Society of Forest Science
• /
• v.100 no.1
• /
• pp.70-78
• /
• 2011

In this study, we develop a fire stochastic simulation model by season based on the historical fire data in Korea. The model is utilized to generate sequences of fire events that are consistent with Korean fire history. We employ a three-stage approach. First, a random draw from a Bernoulli distribution is used to determine if any fire occurs for each day of a simulated fire season. Second, if a fire does occur, a random draw from a geometric multiplicity distribution determines their number. Last, ignition times for each fire are randomly drawn from a Poisson distribution. This specific distributional forms are chosen after analysis of Korean historical fire data. Maximum Likelihood Estimation (MLE) is used to estimate the primary parameters of the stochastic models. Fire sequences generated with the model appear to follow historical patterns with respect to diurnal distribution and total number of fires per year. We expect that the results of this study will assist a fire manager for planning fire suppression policies and suppression resource allocations.

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