• Proceedings of the Korea Water Resources Association Conference
• /
• 2004.05b
• /
• pp.479-483
• /
• 2004

A new calculation method of expected overtopping probability of rubble mound breakwater considering real tide occurrence has been proposed. A calculation method of expected overtopping probability of rubble mound breakwater was proposed by Kweon and Suh (2003). In their calculation, the fluctuation of tidal elevation was expressed by the sinusoidal change that yields the uniform distribution of occurrence frequency. However, the realistic distribution of tidal elevation should influence on the overtopping chance. In this study, the occurrence frequency of tidal elevation obtained from the real sea is included. The tidal elevation used in this study is collected from the east coastal part of Korean peninsular. Analyzing the annual data of the tidal fluctuation measured hourly during 355 days, the distribution of occurrence frequency is formulated utilizing by the normal distribution with one peak. Among the calculation procedures of annual maximum wave height, wave height-period joint distribution, wave run-up height and occurrence frequency of tide, only the annual maximum wave height is again chosen randomly from normal distribution to consider the uncertainty. The others are treated by utilizing the distribution function or relationship itself, It is found that the inclusion of the variability of tidal elevation has great influence on the computation of the expected overtopping probability of rubble mound breakwater. The bigger standard deviation of occurrence frequency is, the lower the overtopping probability of rubble mound breakwater is.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.6 no.4
• /
• pp.242-249
• /
• 2004

This study was conducted to develop a forest fire occurrence model using meteorological characteristics for the practical purpose of forecasting forest fire danger. Forest fire in South Korea is highly influenced by humidity, wind speed, and temperature. To effectively forecast forest fire occurrence, we need to develop a forest fire danger rating model using weather factors associated with forest fire. Forest fore occurrence patterns were investigated statistically to develop a forest fire danger rating index using time series weather data sets collected from 8 meteorological observation centers. The data sets were for 5 years from 1997 through 2001. Development of the forest fire occurrence probability model used a logistic regression function with forest fire occurrence data and meteorological variables. An eight-province probability model by was developed. The meteorological variables that emerged as affective to forest fire occurrence are effective humidity, wind speed, and temperature. A forest fire occurrence danger rating index of through 10 was developed as a function of daily weather index (DWI).

• The Bulletin of The Korean Astronomical Society
• /
• v.39 no.1
• /
• pp.76.1-76.1
• /
• 2014

We investigate the solar flare and CME occurrence rate and probability depending on sunspot class and its area change. These CMEs are front-side, partial and full halo CMEs associated with X-ray flares. For this we use the Solar Region Summary(SRS) from NOAA, NGDC flare catalog, and SOHO/LASCO CME catalog for 16 years (from January 1996 to December 2011). We classify each sunspot class into two sub-groups: "Large" and "Small". In addition, for each class, we classify it into three sub-groups according to sunspot class area change: "Decrease", "Steady", and "Increase". In terms of sunspot class area, the solar flare and CME occurrence probabilities noticeably increase at compact and large sunspot groups (e.g., 'Fkc'). In terms of sunspot area change, solar flare and CME occurrence probabilities for the "Increase" sub-groups are noticeably higher than those for the other sub-groups. For example, in case of the (M+X)-class flares of 'Dkc' class, the flare occurrence probability of the "Increase" sub-group is three times higher than that of the "Steady" sub-group. In case of the 'Eai' class, the CME occurrence probability of the "Increase" sub-groups is five time higher than that of the "Steady" sub-group. Our results demonstrate statistically that magnetic flux and its emergence enhance solar flare and CME occurrence, especially for compact and large sunspot groups.

• Journal of Internet Computing and Services
• /
• v.19 no.6
• /
• pp.101-112
• /
• 2018

A lot of manpower and budgets are being used to prevent fires, and only a small portion of the data generated during this process is used for disaster prevention activities. This study develops a prediction model of fire occurrence probability based on data mining in order to more actively use these data for disaster prevention activities. For this purpose, variables for predicting fire occurrence probability of various buildings were selected and data of construction administrative system, national fire information system, and Korea Fire Insurance Association were collected and integrated data set was constructed. After appropriate data cleansing and preprocessing, various data mining methodologies such as artificial neural network, decision trees, SVM, and Naive Bayesian were used to develop a prediction model of the fire occurrence probability of buildings. The most accurate model among the derived models is Linear SVM model which shows 68.42% as experimental data and 63.54% as verification data and it is the best model to predict fire occurrence probability of buildings. As this study develops the prediction model which uses only the set values of the specific ranges, future studies may explore more opportunites to use various setting values not shown in this study.

• Journal of Environmental Science International
• /
• v.2 no.3
• /
• pp.235-243
• /
• 1993

Extreme environments and freak wave characteristics in the coastal waters of Korean Peninsula are analyzed using the observed wave data. Freak wave has been intensely emphasized as an important environmental force parameter in several recent research works. However, the mechanism and occurrence probability of freak wave are not clarified. The aims of this study we: to summarize the distribution of extreme environment for wind waves, and to find occurrence probability of freak wave in the coastal waters of Korean Peninsula. These extreme sea conditions are discussed by applying extreme value analysis method, and the statistic characteristics are summarized which can be used to the design and analysis of coastal structures. The mechanism and the occurrence probability of freak wave are also discussed in detail using wave parameters in considered with wave deformation in the coastal waters. Key Words : extreme wave, freak wave, extreme analysis, design wave, probability density.

• Journal of Korean Society of Water and Wastewater
• /
• v.34 no.4
• /
• pp.251-258
• /
• 2020

Sewer deterioration models are needed to forecast the remaining life expectancy of sewer networks by assessing their conditions. In this study, the serious defect (or condition state 3) occurrence probability, at which sewer rehabilitation program should be implemented, was evaluated using four probability distribution functions such as normal, lognormal, exponential, and Weibull distribution. A sample of 252 km of CCTV-inspected sewer pipe data in city Z was collected in the first place. Then the effective data (284 sewer sections of 8.15 km) with reliable information were extracted and classified into 3 groups considering the sub-catchment area, sewer material, and sewer pipe size. Anderson-Darling test was conducted to select the most fitted probability distribution of sewer defect occurrence as Weibull distribution. The shape parameters (β) and scale parameters (η) of Weibull distribution were estimated from the data set of 3 classified groups, including standard errors, 95% confidence intervals, and log-likelihood values. The plot of probability density function and cumulative distribution function were obtained using the estimated parameter values, which could be used to indicate the quantitative level of risk on occurrence of CS3. It was estimated that sewer data group 1, group 2, and group 3 has CS3 occurrence probability exceeding 50% at 13th-year, 11th-year, and 16th-year after the installation, respectively. For every data groups, the time exceeding the CS3 occurrence probability of 90% was also predicted to be 27th- to 30th-year after the installation.

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

• IEMEK Journal of Embedded Systems and Applications
• /
• v.10 no.5
• /
• pp.265-272
• /
• 2015

This paper presents an improved feature extraction method to achieve a good performance in the detection of splicing forged images. Strong edges caused by the image splicing destroy the statistical dependencies between parent and child subbands in the wavelet domain. We analyze the co-occurrence probability matrix of parent and child subbands in the wavelet domain, and calculate the statistical moments from two-dimensional characteristic function of the co-occurrence matrix. The extracted features are used as the input of SVM classifier. Experimental results show that the proposed method obtains a good performance with a small number of features compared to the existing methods.

• The Bulletin of The Korean Astronomical Society
• /
• v.36 no.1
• /
• pp.40.2-40.2
• /
• 2011

We investigated solar flare occurrence probability depending on sunspot group classification and its area change. For this study, we used the McIntosh sunspot group classification and then selected most flare-productive six sunspot groups : DKI, DKC, EKI, EKC, FKI and FKC. For each group, we classified it into three sub-groups according to the sunspot group area change : increase, steady and decrease. For sunspot data, we used the NOAA's active region information for 19 years (from 1992.01 to 2010.12). As a result, we found that the probabilities of the all "increase" sub-groups is noticeably higher than those of other sub-groups. In case of FKC McIntosh sunspot group, for example, the M-class flare occurrence probability of the "increase" sub-group is 65% while the "decrease" and "steady" sub-groups are 50% and 44%, respectively. In summary, when sunspot group area increases, the probability of solar flares noticeably increases. This is statistical evidence that magnetic flux emergence is an very important mechanism for triggering solar flares.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.7 no.4
• /
• pp.57-64
• /
• 2004

In order to decrease the area damaged by forest fires and to prevent the occurrence of forest fires, the forest fire danger rating system was developed to estimate forest fire risk by means of weather, topography, and forest type. Forest fires occurrence prediction needs to improve continually. Logistic regression and spatial analysis was used in developing the forest fire occurrence probability model. The forest fire danger index in accordance to the probability of forest fire occurrence was used in the classification of forest fire occurrence risk regions.