• Proceedings of the Korean Society of Coastal and Ocean Engineers Conference
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• 2003.08a
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• pp.359-362
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• 2003

심해 설계파를 추정하는 과정은 일반적으로 극치분포함수에 의한 방법에 의존한다. 극치분포함수를 이용하여 재현기간별 극값을 추출하는 방법은 가용한 자료를 일정기간(보통 1년)으로 구분하고, 구분된 기간에서의 최대값(또는 최소값)을 추출하고, 추출된 자료로부터 적합한 분포를 추정하는 과정으로 분류할 수 있다. (중략)

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.16 no.3
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• pp.130-141
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• 2004

For a coastal or harbor structure design, one of the most important environmental factors is the appropriate design wave condition. Especially, the information of deepwater wave height distribution is essential for reliability design. In this paper, a set of deep water wave data obtained from KORDI(2003) were analyzed for extreme wave heights. These wave data at 67 stations off the Korean coast from 1979 to 1998 were arranged in the 16 directions. The probability distributions considered in this research were the Weibull, the Gumbel, the Log-pearson Type-III, and Lognormal distribution. For each of these distributions, three parameter estimation methods, i.e. the method of moments, maximum likelihood and probability weighted moments, were applied. Chi-square and Kolmogorov-Smirnov goodness-of-fit tests were performed, and the assumed distribution was accepted at the confidence level 95%. Gumbel distribution which best fits to the 67 station was selected as the most probable parent distribution, and optimally estimated parameters and 50 year design wave heights were presented.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.29 no.1
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• pp.245-251
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• 2005

When we design coastal and harbol facilities deepwater design wave and wind speed are the important design parameters. Especially, the analysis of these informations is a vital step for the point of disaster prevention. In this study, we made and an extreme value analysis using a series of deep water significant wave data arranged in the 16 direction and supplied by KORDI real-time wave information system ,and the wind data gained from Wan-Do whether Station 1978-2003. The probability distributions considered in this characteristic analysis were the Weibull, the Gumbel, the Log-Pearson Type III, the Normal, the Lognormal, and the Gamma distribution. The parameter for each distribution was estimated by three methods, i.e. the method of moments, the maximum likelihood, and the method of probability weight moments. Furthermore, probability distributions for the extreme data had been selected by using Chi-square and Kolmogorov-Smirnov test within significant level of 5%, i,e. 95% reliance level. From this study we found that Gumbel distribution is the most proper model for the deep water design wave height off the southwest coast of Korea. However the result shows that the proper distribution made for the selected site is varied in each extreme data set.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.6 no.1
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• pp.88-93
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• 1994

This study is concerned with an analytic derivation of the probability density function applicable for wave heights in finite water depth using two different methods. As the first method of the study, a probability density function is developed by applying a series of polynomials which is orthogonal with respect to Rayleigh probability density function. The newly derived probability density function is compared with the histogram constructed from wave data obtained in finite water depth which indicate strong non-Gaussian characteristics. Although the probability density represents the histogram very well. it has negative density at large values. Although the magnitude of the negative density is small. it negates the use of the distribution function fer estimating extreme values. As the second method of the study, a probability density function of wave height is developed by applying the maximum entropy method. The probability density function thusly derived agrees very well with the wave height distribution in shallow water, and appears to be useful in estimating extreme values and statistical properties of wave heights in finite water depth. However, a functional relationship between the probability distribution and the non-Gaussian characteristics of the data cannot be obtained by applying the maximum entropy method.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.279-279
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• 2020

최근 copula 모형은 여러 확률변수를 갖는 수문현상에 대해 빈도해석을 수행할 경우 결합확률분포형으로 유용하게 사용되고 있다. 하나의 자료를 확률변수로 사용하는 단변량 빈도해석에 비해 여러 수문자료를 동시에 각각 확률변수로 취하여 결합확률분포형을 추정할 수 있는 다변량 빈도해석은 수문자료의 상관성을 고려하면서 확률분포형을 추정할 수 있다는 장점이 있다. Copula 모형 중 Gumbel copula는 extreme-value 확률분포형으로 극치사상에 적합한 확률분포형이다. 본 연구에서는 Gumbel copula를 이용하여 우리나라 기상청 64개 종관기상관측소의 강우자료로부터 극치 강우사상을 추출하고, 이를 이용하여 빈도해석을 수행하였다. 극치 강우사상은 전체 강우사상 중 각 년도별로 최대강우량을 갖는 연최대강우량사상(annual maximum volume event)을 사용하였다. 각 확률변수의 주변분포형으로는 gamma, Gumbel, generalized extreme value, generalized logistic, Weibull 등 5개 확률분포형을 검토하였으며 각각 적합한 주변분포형을 적용하고 copula 모형의 매개변수는 의사최우도법(maximum pseudo-likelihood method)를 사용하여 추정하였다. 또한 추정된 copula 모형은 Cramer-von Mises 함수와 경험적 copula를 이용하여 적합도 검정을 수행하였다. 이를 통해 극치강우사상에 대하여 Gumbel copula 모형의 적용성을 검토하였으며 추정된 결합확률분포형을 이용하여 빈도별 확률강우사상을 2차원 등치선(contour line)형태로 제시하였다.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.27 no.4
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• pp.258-265
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• 2015

Over the last 20 years, freak waves have attracted many researchers because of their unexpected behaviors and damages on offshore structures and vessels in the ocean and coastal waters. Despite many researches on the causes, mechanisms and occurrence of freak waves, we have not reached consensus on the results of the researches. This paper presents the occurrence probability of freak waves based on the analysis of wave records measured at coastal waters of Donghae harbor in the East Sea. Three freak waves were found which satisfied conditions of m and $H_S{\geq}2.5m$ and $H_m/H_S{\geq}2$. The occurrence probabilities of freak waves were estimated from extreme distributions by Mori, Rayleigh and Ahn, and found to be on the orders of O($10^{-1}$), O($10^{-2}$), and O($10^{-3}$), respectively. The occurrence probabilities of freak waves measured from waves records were estimated between O($10^{-2}$) and O($10^{-3}$), which were located between predictions by Rayleigh and Ahn's extreme probability distributions. However, we need more analysis of wave records obtained from diverse field conditions in order to verify the accuracy of the estimation of occurrence probability of freak waves.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.20 no.5
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• pp.482-490
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• 2008

For a coastal or harbor structure design, one of the most important environmental factors is the appropriate extreme highest tide level condition. Especially, the information of extreme highest tide level distribution is essential for reliability design. In this paper, 23 set of extreme highest tide level data obtained from National Oceanographic Research Institute(NORI) were analyzed for extreme highest tide levels. The probability distributions considered in this research were Generalized Extreme Value(GEV), Gumbel, and Weibull distribution. For each of these distributions, three parameter estimation methods, i.e. the method of moments, maximum likelihood and probability weighted moments, were applied. Chi-square and Kolmogorov-Smirnov goodness-offit tests were performed, and the assumed distribution was accepted at the confidence level 95%. Gumbel distribution which best fits to the 22 tidal station was selected as the most probable parent distribution, and optimally estimated parameters and extreme highest tide level with various return periods were presented. The extreme values of Incheon, Cheju, Yeosu, Pusan, and Mukho, which estimated by Shim et al.(1992) are lower than that of this result.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.21 no.6
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• pp.480-494
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• 2009

Long term extreme wave data are essential for planning and designing coastal structures. Since the availability of the field data for the waters around Korean peninsula is limited to provide a reliable wave statistics, the wave climate information has been generated by means of long-term wave hindcasting using available meteorological data. KORDI(2005) has proposed extreme wave data at 106 stations off the Korean coast from 1979 to 2003. In this paper, extreme data sets of wave(KORDI, 2005) have been analyzed for best-fitting distribution functions, for which the spread parameter proposed by Goda(2004) is evaluated. The calculated values of the spread parameter are in good agreement with the values based on method of moment for parameter estimation. However, the spread parameter of extreme wave data has a representative value ranging from about 1.0 to 2.8 which is larger than some foreign coastal waters, it is necessary to review deep water design wave.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.414-414
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• 2015

최근 지구온난화로 인한 기상변동성 증가로 인해 극한기후현상의 발생빈도가 점차 증가하고 있으며 유역단위의 수자원을 효율적으로 운영하는데 문제점을 해소하고자 다양한 측면에서 체계적인 수자원 운영을 위한 연구가 이루어지고 있다. 수공구조물을 설계하는데 있어서 가장 일반적인 가정 사항은 수문모형에 사용되는 강우의 빈도와 유출의 빈도가 동일하다는 가정에 근거한다. 즉, 유역의 초기함수조건, 강우강도, 강우의 시간적 분포와 관계없이 동일한 빈도로 고려되는 문제점이 있다. 이러한 점에서 비교적 장기간의 자료를 확보하고 있는 계측유역에 대해서 다변량 확률밀도함수를 적용하여 비선형관계를 고려한 수문빈도해석기법을 개발하고자 한다. 본 연구에서는 이변량 분석기법(bivariate analysis) 중 전통적인 이변량 분포에 비해 주변분포형(marginal distribution)을 자유롭게 선택할 수 있는 장점이 있는 추계학적 Copula 모형을 활용하여 댐 및 저수지 상류유역의 강우량과 유입량을 대상으로 이변량 분석을 수행하고자 한다. 최종적으로 비선형 관계에 있는 강수량과 유출량 사이에 이변량 빈도해석 모형을 개발하고 기존 해석방법과의 종합적인 비교를 실시하였다.

• Journal of the Society of Disaster Information
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• v.11 no.4
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• pp.493-505
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• 2015

Based on the typhoon paths landed on the southern coast of Korea, the distribution of typhoon moving directions follow the Beta probability density function and that of pressure drops in typhoon eyes follow the Rayleigh probability density function. Consequently, the extreme typhoon simulation scenarios for six landing positions are determined as most probable one in moving direction and extreme one of Typhoon Maemi level in pressure drop. The variation of storm surges in Masan bay associated with simulated typhoon landing position is analyzed through the numerical experiments in the next paper as the second part.