• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.1001-1012
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• 2016

It is well known that air pollutants exert a bad influence on human health. According to the United Nations Environment Program, 4.3 million people die from carbon monoxide and particulate matter annually from all over the world. Carbon monoxide is a toxic gas that is the most dangerous of the gas consisting of carbon and oxygen. In this paper, we used 1 hour, 6 hours, 12 hours, and 24 hours average carbon monoxide concentration data collected between 2004 and 2013 in Daegu Gyeongbuk area. Parameters of the generalized extreme value distribution were estimated by maximum likelihood estimation and L-moments estimation. An evalution of goodness of fitness also was performed. Since the number of samples were small, L-moment estimation turned out to be suitable for parameter estimation. We also calculated 5 year, 10 year, 20 year, and 40 year return level.

• Journal of Korea Water Resources Association
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• v.43 no.4
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• pp.337-351
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• 2010

There is a growing dissatisfaction with use of conventional statistical methods for the prediction of extreme events. Conventional methodology for modeling extreme event consists of adopting an asymptotic model to describe stochastic variation. However asymptotically motivated models remain the centerpiece of our modeling strategy, since without such an asymptotic basis, models have no rational for extrapolation beyond the level of observed data. Also, this asymptotic models ignored or overestimate the uncertainty and finally decrease the reliability of uncertainty. Therefore this article provide the research example of the extreme rainfall event and the methodology to reduce the uncertainty. In this study, the Bayesian MCMC (Bayesian Markov Chain Monte Carlo) and the MLE (Maximum Likelihood Estimation) methods using a quadratic approximation are applied to perform the at-site rainfall frequency analysis. Especially, the GEV distribution and Gumbel distribution which frequently used distribution in the fields of rainfall frequency distribution are used and compared. Also, the results of two distribution are analyzed and compared in the aspect of uncertainty.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.464-464
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• 2012

수문분야에 있어서 빈도해석의 목적은 특정 재현기간에 대한 발생 가능한 수문량의 규모를 파악하는데 있으며, 빈도해석의 정확도는 적합한 확률분포모형의 선택과 매개변수 추정방법에 의존하게 된다. 일반적으로 각 확률분포모형의 특성을 대표하는 매개변수를 추정하기 위해서는 모멘트 방법, 확률가중 모멘트 방법, 최대우도법 등을 이용하게 된다. 모멘트 방법에 의한 매개변수 추정은 해를 구하기 위한 과정이 단순한 반면, 비대칭형의 왜곡된 분포를 갖는 자료들에 대해서는 부정확한 결과를 나타내게 된다. 확률가중 모멘트 방법은 표본의 크기가 작거나 왜곡된 자료일 경우에도 비교적 안정적인 결과를 제공하는 반면, 확률 가중치가 정수로만 제한되는 단점을 갖고 있다. 그리고 대수 우도함수를 이용하여 매개변수를 추정하게 되는 최우도법은 가장 효율적인 매개변수 추정치를 얻을 수 있는 것으로 알려져 있으나, 비선형 연립방정식으로 표현되는 해를 구하기 위해서는 Newton-Raphson 방법을 사용하는 등 절차가 복잡하며, 때로는 수렴이 되지 않아 해룰 구하지 못하는 경우가 발생되게 된다. 이에 반해, 최근의 Genetic Algorithm, Ant Colony Optimization 및 Simulated Annealing과 같은 Meta-Heuristic Algorithm들은 복잡합 공학적 최적화 문제 있어서 효율적인 대안으로 주목받고 있으며, Hassanzadeh et al.(2011)에 의해 수문학적 빈도해석을 위한 매개변수 추정에 있어서도 그 적용성이 검증된바 있다. 본 연구의 목적은 연 최대강수 자료의 빈도해석에 적용되는 확률분포모형들의 매개변수 추정을 위해 Meta-Heuristic Algorithm을 적용하고자 함에 있다. 따라서 본 연구에서는 매개변수 추정을 위한 방법으로 Genetic Algorithm 및 Harmony Search를 적용하였고, 그 결과를 최우도법에 의한 결과와 비교하였다. GEV 분포를 이용하여 Simulation Test를 수행한 결과 Genetic Algorithm을 이용하여 추정된 매개변수들은 최우도법에 의한 결과들과 비교적 유사한 분포를 나타내었으나 과도한 계산시간이 요구되는 것으로 나타났다. 하지만 Harmony Search를 이용하여 추정된 매개변수들은 최우도법에 의한 결과들과 유사한 분포를 나타내었을 뿐만 아니라 계산시간 또한 매우 짧은 것으로 나타났다. 또한 국내 74개소의 강우관측소 자료와 Gamma, Log-normal, GEV 및 Gumbel 분포를 이용한 실증연구에 있어서도 Harmony Search를 이용한 매개변수 추정은 효율적인 매개 변수 추정치를 제공하는 것으로 나타났다.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.16-16
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• 2011

Statistics of extreme rainfall play a vital role in engineering practice from the perspective of mitigation and protection of infrastructure and human life from flooding. While flood frequency assessments, based on river flood flow data are preferred, the analysis of rainfall data is often more convenient due to the finer spatial nature of rainfall recording networks, often with longer records, and potentially more easily transferable from site to site. The rainfall frequency analysis as a design tool has developed over the years in New Zealand from Seelye's daily rainfall frequency maps in 1947 to Thompson's web based tool in 2010. This paper will present a history of the development of New Zealand rainfall frequency analysis methods, and the details of the latest method, so that comparisons may in future be made with the development of Korean methods. One of the main findings in the development of methods was new knowledge on the distribution of New Zealand rainfall extremes. The High Intensity Rainfall Design System (HIRDS.V3) method (Thompson, 2011) is based upon a regional rainfall frequency analysis with the following assumptions: $\bullet$ An "index flood" rainfall regional frequency method, using the median annual maximum rainfall as the indexing variable. $\bullet$ A regional dimensionless growth curve based on the Generalised Extreme Value (GEV), and using goodness of fit test for the GEV, Gumbel (EV1), and Generalised Logistic (GLO) distributions. $\bullet$ Mapping of median annual maximum rainfall and parameters of the regional growth curves, using thin-plate smoothing splines, a $2km\times2km$ grid, L moments statistics, 10 durations from 10 minutes to 72 hours, and a maximum Average Recurrence Interval of 100 years.

• Journal of Ocean Engineering and Technology
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• v.33 no.3
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• pp.222-228
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• 2019

An extreme value analysis (EVA) is essential to obtain a design value for highly nonlinear variables such as long-term environmental data for wind and waves, and slamming or sloshing impact pressures. According to the extreme value theory (EVT), the extreme value distribution is derived by multiplying the initial cumulative distribution functions for independent and identically distributed (IID) random variables. However, in the position mooring of DNVGL, the sampled global maxima of the mooring line tension are assumed to be IID stochastic variables without checking their independence. The ITTC Recommended Procedures and Guidelines for Sloshing Model Tests never deal with the independence of the sampling data. Hence, a design value estimated without the IID check would be under- or over-estimated because of considering observations far away from a Weibull or generalized Pareto distribution (GPD) as outliers. In this study, the IID sampling data are first checked in an EVA. With no IID random variables, an automatic resampling scheme is recommended using the block maxima approach for a generalized extreme value (GEV) distribution and peaks-over-threshold (POT) approach for a GPD. A partial autocorrelation function (PACF) is used to check the IID variables. In this study, only one 5 h sample of sloshing test results was used for a feasibility study of the resampling IID variables approach. Based on this study, the resampling IID variables may reduce the number of outliers, and the statistically more appropriate design value could be achieved with independent samples.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.365-365
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• 2022

강우에 따른 유역 내 유출량은 수문순환에서 중요한 요소 중 하나이며, 과거부터 강우-유출 모델링을 위한 여러 물리적 수문모형들이 개발되어왔다. 또한 최근 딥러닝 기술을 기반으로한 강우-유출 모델링 접근 방식이 유효함을 입증하는 여러 연구가 수행됨에 따라 딥러닝을 기반으로한 유출량 모의 연구도 활발히 진행되고 있다. 따라서 본 연구에서는 물리적 수문모형인 SWAT(Soil Water Assessment Tool)과 딥러닝 기법 중 하나인 LSTM(Long Short-Term Memory)을 사용하여 연구대상지 유출량을 모의했으며, 두 모형에 의해 모의 된 유출량의 극값을 비교 분석했다. 연구대상지로는 영산강 유역을 선정했으며, 영산강 유역의 과거 기간의 기후 변수 모의를 위해 CMIP(Coupled Model Intercomparison Project)6 GCM(General Circulation Model)을 사용했다. GCM을 사용하여 모의 된 기후 변수들은 영산강 유역 내 기상관측소의 과거 기간 관측 값을 기반으로 분위사상법을 사용하여 편이보정 됐다. GCM에 의해 모의 된 기후 변수 및 SWAT, LSTM에 의해 모의 된 유출량은 각각 영산강 유역 내 기상관측소 및 수위관측소의 관측 값을 기반으로 재현성을 평가했다. SWAT 및 LSTM을 사용하여 모의 된 유출량의 극값은 GEV(General Extreme Value) 분포를 사용하여 추정하였다. 결과적으로 GCM의 기후 변수 모의 성능은 과거 기간 관측 값과 비교했을 때 편이보정 후에서 상당히 향상되었다. 유출량 모의 결과의 경우 과거 기간 유출량의 관측 값과 비교했을 때 LSTM의 모의 유출량이 SWAT보다 과거 기간 유출량을 보다 근접하게 모의했으며, 극값 모의 성능의 경우 또한 LSTM이 SWAT보다 높은 성능을 보였다.

• Wind and Structures
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• v.38 no.3
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• pp.215-229
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• 2024

Guyed mast structures exhibit characteristics such as high flexibility, low mass, small damping ratio, and large aspect ratio, leading to a complex wind-induced vibration response mechanism. This study analyzed the time- and frequency-domain characteristics of the wind-induced response of a guyed mast structure using measured acceleration response data obtained from the Shenzhen Meteorological Gradient Tower (SZMGT). Firstly, 734 sets of 1-hour acceleration samples measured from 0:00 October 1, 2021, to 0:00 November 1, 2021, were selected to study the vibration shapes of the mast and the characteristics of the generalized extreme value (GEV) distribution. Secondly, six sets of typical samples with different vibration intensities were further selected to explore the Gaussian property and modal parameter characteristics of the mast. Finally, the modal parameters of the SZMGT are identified and the identification results are verified by finite element analysis. The findings revealed that the guyed mast vibration shape exhibits remarkable diversity, which increases nonlinearly along the height in most cases and reaches a maximum at the top of the tower. Moreover, the GEV distribution characteristics of the 734 sets of samples are closer to the Weibull distribution. The probability distribution of the structural wind vibration response under strong wind is in good agreement with the Gaussian distribution. The structural response of the mast under wind loading exhibits multiple modes. As the structural response escalates, the first three orders of modal energy in the tower display a gradual increase in proportion.

• Water for future
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• v.26 no.4
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• pp.73-83
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• 1993

This study focuses on the separation effect analysis of rainfall data for 2-parameter log-normal, 3-parameter log-normal, type-extreme value, 2-parameter gamma, 3-parameter gamma, log-Pearson type-III, and general extreme value distribution functions. Difference in the relationship between the mean and standard deviation of skewness for historical data and relations derived from 7 distribution functions are analyzed suing the Monte Carlo experiment. The results show that rainfall data has the separation effect for 6 distribution functions except 3-parameter gamma distribution function.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.846-851
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• 2006

본 논문은 최근 발생한 집중호우와 이상강우를 고려하여 인천지역에서 사용중인 확률강우강도식에 대한 새로운 확률강우강도식을 제안하였으며, 기상청 자료를 이용하여 지속시간 10분${\sim}$24시간까지의 임의시간 연최대강우량을 산정하였다. 강우지속기간별 확률강우량을 추정하기 위하여 11개의 확률분포형을 적용하였으며 Chi-square 검정방법, Kolmogorov -Smirnov 검정방법, Cramer Von Mises 검정방법으로 적합도 검정과 함께 최근 강우에 대한 경향을 분석하고 실제 발생한 강우 중에서 최대 발생 강우량을 고려하여 적정분포인 GEV 분포를 확률 분포형으로 선정하였다. 확률강우강도식은 최소자승법을 사용하여 Talbot형, Sherman형, Japanese형, 통합형 Ⅰ 및 Ⅱ 형태로 산정하였고, 지역내 하수도 및 하천의 지속시간을 감안하여 확률강우강도식을 결정하였다. 또한 정확성을 고려하여 통합형 Ⅰ을 선택하였고 지속시간에 따른 강우강도식의 확률강우와 관측치를 감안한 강도식을 인천지역의 강우강도식으로 제안하였다.

• Journal of Wetlands Research
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• v.2 no.1
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• pp.49-58
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• 2000

The frequency analysis of annual maximum rainfall data and the derivation of probable rainfall intensity formula at Masan station are performed in this study. Based on the eight different rainfall duration data from 10 minutes to 24 hours, eight types of probability distribution (Gamma, Lognormal, Log-Pearson type III, GEV, Gumbel, Log-Gumbel, Weibull, and Wakeby distributions), three types of parameter estimation scheme (moment, maximum likelihood and probability weighted methods) and three types of goodness-of-fit test (${\chi}^2$, Kolmogorov-Smirnov and Cramer von Mises tests) were considered to find an appropriate probability distribution at Masan station. The Lognormal-2 distribution was selected and the probable rainfall intensity formula was derived by regression analysis. The derived formula can be used for estimating rainfall quantiles of the Masan vicinity areas with convenience and reliability in practice.