• Journal of Environmental Science International
• /
• v.17 no.6
• /
• pp.657-669
• /
• 2008

Lately extreme weather event is occurring because of the global warming. Especially disaster due to the extreme heat are increasing but the definition and the standard of the extreme heat is obscure until now. So this study established the extreme heat standard by using the number of daily deaths. As a result, considering the climate of the megalopolis using daily maximum heat index and daily maximum temperature was the best for the standard of the extreme heat. And it showed that extreme heat lasted for 2 days affects the death toll the most. The regional incidence of the extreme heat is highest at August and July, September and June is following.

• Wind and Structures
• /
• v.26 no.3
• /
• pp.129-146
• /
• 2018

Extreme wind speed analysis has been carried out conventionally by assuming the extreme series data is stationary. However, time-varying trends of the extreme wind speed series could be detected at many surface meteorological stations in China. Two main reasons, exposure change and climate change, were provided to explain the temporal trends of daily maximum wind speed and annual maximum wind speed series data, recorded at Hangzhou (China) meteorological station. After making a correction on wind speed series for time varying exposure, it is necessary to perform non-stationary statistical modeling on the corrected extreme wind speed data series in addition to the classical extreme value analysis. The generalized extreme value (GEV) distribution with time-dependent location and scale parameters was selected as a non-stationary model to describe the corrected extreme wind speed series. The obtained non-stationary extreme value models were then used to estimate the non-stationary extreme wind speed quantiles with various mean recurrence intervals (MRIs) considering changing climate, and compared to the corresponding stationary ones with various MRIs for the Hangzhou area in China. The results indicate that the non-stationary property or dependence of extreme wind speed data should be carefully evaluated and reflected in the determination of design wind speeds.

• Wind and Structures
• /
• v.31 no.3
• /
• pp.197-207
• /
• 2020

Knowledge on the design value of extreme wind pressure coefficients (EWPC) of a specific zone of buildings is essential for the wind-resistant capacity of claddings. This paper presents a method to estimate the representative EWPC introducing the multivariate extreme value model. The spatial correlations of the extreme wind pressures at different locations can be consider through the multivariate extreme value. The moving average method is also adopted in this method, so that the measured point pressure can be converted to wind pressure of an area. The proposed method is applied to wind tunnel test results of a large flat roof building. Comparison with existing methods shows that it can give a good estimation for all target zones with different sizes.

• Wind and Structures
• /
• v.19 no.2
• /
• pp.169-184
• /
• 2014

This paper presents a method of estimation of extreme wind. Assuming the extreme wind follows the Gumbel distribution, it is modeled through fitting an exponential function to the numbers of storms over different thresholds. The comparison between the estimated results with the Improved Method of Independent Storms (IMIS) shows that the proposed method gives reliable estimation of extreme wind. The proposed method also shows its advantage on the insensitiveness of estimated results to the precision of the data. The volume of extreme storms used in the estimation leads to more than 5% differences in the estimated wind speed with 50-year return period. The annual rate of independent storms is not a significant factor to the estimation.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.26 no.1
• /
• pp.87-92
• /
• 2016

In this paper, optimization technique such as particle swarm optimization was used to optimize the parameters of fuzzy Extreme Learning Machine. While the learning speed of conventional neural networks is very slow, that of Extreme Learning Machine is very fast. Fuzzy Extreme Learning Machine is composed of the Extreme Learning Machine with very fast learning speed and fuzzy logic which can represent the linguistic information of the field experts. The general sigmoid function is used for the activation function of Extreme Learning Machine. However, the activation function of Fuzzy Extreme Learning Machine is the membership function which is defined in the procedure of fuzzy C-Means clustering algorithm. We optimize the parameters of the membership functions by using optimization technique such as Particle Swarm Optimization. In order to validate the classification capability of the proposed classifier, we make several experiments with the various machine learning datas.

• Environmental Engineering Research
• /
• v.16 no.3
• /
• pp.175-183
• /
• 2011

The climate of the $21^{st}$ century is likely to be significantly different from that of the 20th century because of human-induced climate change. An extreme weather event is defined as a climate phenomenon that has not been observed for the past 30 years and that may have occurred by climate change and climate variability. The abnormal climate change can induce natural disasters such as floods, droughts, typhoons, heavy snow, etc. How will the frequency and intensity of extreme weather events be affected by the global warming change in the $21^{st}$ century? This could be a quite interesting matter of concern to the hydrologists who will forecast the extreme weather events for preventing future natural disasters. In this study, we establish the extreme indices and analyze the trend of extreme weather events using extreme indices estimated from the observed data of 66 stations controlled by the Korea Meteorological Administration (KMA) in Korea. These analyses showed that spatially coherent and statistically significant changes in the extreme events of temperature and rainfall have occurred. Under the global climate change, Korea, unlike in the past, is now being affected by extreme weather events such as heavy rain and abnormal temperatures in addition to changes in climate phenomena.

• International Journal of Naval Architecture and Ocean Engineering
• /
• v.6 no.4
• /
• pp.1148-1159
• /
• 2014

Different procedures for estimation of the extreme global wave hydroelastic responses in ships are discussed. Firstly, stochastic procedures for application in detailed numerical studies (CFD) are outlined. The use of the First Order Reliability Method (FORM) to generate critical wave episodes of short duration, less than 1 minute, with prescribed probability content is discussed for use in extreme response predictions including hydroelastic behaviour and slamming load events. The possibility of combining FORM results with Monte Carlo simulations is discussed for faster but still very accurate estimation of extreme responses. Secondly, stochastic procedures using measured time series of responses as input are considered. The Peak-over-Threshold procedure and the Weibull fitting are applied and discussed for the extreme value predictions including possible corrections for clustering effects.

• Wind and Structures
• /
• v.30 no.2
• /
• pp.141-153
• /
• 2020

Probabilistic information regarding directional extreme wind speeds is important for the precise estimation of the design wind loads on structures. A joint probability distribution model of directional extreme typhoon wind speeds is established using Monte Carlo simulation and empirical copula function to fully consider the correlations of extreme typhoon wind speeds among the different directions. With this model, a procedure for estimating directional extreme wind speeds for given return periods, which ensures that the overall risk is distributed uniformly by direction, is established. Taking 5 typhoon-prone cities in China as examples, the directional extreme typhoon wind speeds for given return periods estimated by the present method are compared with those estimated by the method proposed by Cook and Miller (1999). Two types of directional factors are obtained based on Cook and Miller (1999) and the UK standard's drafting committee (Standard B, 1997), and the directional risks for the given overall risks are discussed. The influences of the extreme wind speed correlations in the different directions and the simulated typhoon wind speed sample sizes on the estimated extreme wind speeds for a given return period are also discussed.

• Journal of Ocean Engineering and Technology
• /
• v.34 no.1
• /
• pp.26-36
• /
• 2020

An extreme value analysis of metocean data which include wave, wind, and current data is a prerequisite for the operation and survival of offshore structures. The purpose of this study was to provide information about the return wave, wind, and current values for the Barents Sea using extreme value analysis. Hindcast datasets of the Global Reanalysis of Ocean Waves 2012 (GROW2012) for a waves, winds and currents were obtained from the Oceanweather Inc. The Gumbel distribution, 2 and 3 parameters Weibull distributions and log-normal distribution were used for the extreme value analysis. The least square method was used to estimate the parameters for the extreme value distribution. The return values, including the significant wave height, spectral peak wave period, wind speed and current speed at surface, were calculated and it will be utilized to design offshore structures to be operated in the Barents Sea.

• Wind and Structures
• /
• v.25 no.3
• /
• pp.261-282
• /
• 2017

The probabilistic information of directional extreme wind speeds is important for precisely estimating the design wind loads on structures. A new joint probability distribution model of directional extreme wind speeds is established based on observed wind-speed data using multivariate extreme value theory with the t-Copula function in the present study. At first, the theoretical deficiencies of the Gaussian-Copula and Gumbel-Copula models proposed by previous researchers for the joint probability distribution of directional extreme wind speeds are analysed. Then, the t-Copula model is adopted to solve this deficiency. Next, these three types of Copula models are discussed and evaluated with Spearman's rho, the parametric bootstrap test and the selection criteria based on the empirical Copula. Finally, the extreme wind speeds for a given return period are predicted by the t-Copula model with observed wind-speed records from several areas and the influence of dependence among directional extreme wind speeds on the predicted results is discussed.