• Communications for Statistical Applications and Methods
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• v.13 no.2
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• pp.369-378
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• 2006

This Study focuses on the binary forecast of occurrence of heavy snow in Honam area based on the MOS(model output statistic) method. For our study daily amount of snow cover at 17 stations during the cold season (November to March) in 2001 to 2005 and Corresponding 45 RDAPS outputs are used. Logistic regression model and neural networks are applied to predict the probability of occurrence of Heavy snow. Based on the distribution of estimated probabilities, optimal thresholds are determined via true shill score. According to the results of comparison the logistic regression model is recommended.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.2
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• pp.37-46
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• 2012

The change of rainfall pattern and hydrologic factors due to climate change increases the occurrence probability of agricultural reservoir water shortage. Water supply assessment of reservoir is usually performed current reservoir level compared to historical water levels or the simulation of reservoir operation based on the water budget analysis. Since each reservoir has the native property for watershed, irrigation district and irrigation water requirement, it is necessary to improve the assessment methods of agricultural reservoir water capability about water resources system. This study proposed a practical methods that water supply vulnerability assessment for an agricultural reservoir based on a concept of probabilistic reliability. The vulnerability assessment of water supply is calculated from probability distribution of water demand condition and water supply condition that influences on water resources management and reservoir operations. The water supply vulnerability indices are estimated to evaluate the performance of water supply on agricultural reservoir system, and thus it is recommended a more objective method to evaluate water supply reliability.

• Journal of the Korea Society for Simulation
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• v.8 no.3
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• pp.77-89
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• 1999

Simulating rare events, such as probability of cell loss in ATM networks, machine failure in highly reliable systems, requires huge simulation efforts due to the low chance of occurrence. Importance Sampling (IS) has been applied to accelerate the occurrence of rare events. However, it has a drawback of effective biasing scheme to make the estimator of IS unbiased. Adaptive Importance Sampling (AIS) employs an estimated sampling distribution of IS to the system of interest during the course of simulation. We propose Nonparametric Adaptive Importance Sampling (NAIS) technique which is nonparametrical version of AIS. We test NAIS to estimate a probability of rare event in M/M/1 queueing model. Comparing with classical Monte Carlo simulation, the computational efficiency and variance reductions gained via NAIS are substantial. A possible extension of NAIS regarding with random number generation is also discussed.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.6
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• pp.405-413
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• 2009

Tagging system is the system that allows internet users to assign new meta-data which is called tag to article, photo, video and etc. for facilitating searching and browsing of web contents. Tag cloud, a visual interface is widely used for browsing tag space. Tag cloud selects the tags with the highest frequency and presents them alphabetically with font size reflecting their popularity. However the conventional tag selection method includes known weaknesses. So, we propose a novel tag selection method Freshness, which helps to find fresh web contents. Freshness is the mean value of Kullback-Leibler divergences between each consecutive change of tag co-occurrence probability distribution. We collected tag data from three web sites, Allblog, Eolin and Technorati and constructed the system, 'Fresh Tag Cloud' which collects tag data and creates our tag cloud. Comparing the experimental results between Fresh Tag Cloud and the conventional one with data from Allblog, our one shows 87.5% less overlapping average, which means Fresh Tag Cloud outperforms the conventional tag cloud.

• Journal of Korea Water Resources Association
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• v.51 no.5
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• pp.417-424
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• 2018

This study analyzed the effect of irrigation reservoirs, antecedent soil moisture conditions (AMC) and Huff time distribution on peak discharge using Monte Carlo simulation. The peak discharge was estimated for four different cases in combination of irrigation reservoir capacity, AMC, and Huff time distribution. Applying 100% reservoir capacity or AMC-III, the peak discharges corresponding return periods of 50~300 years were overestimated by 25~30% compared to those of cases that considered the probability of occurrence for individual condition. Applying the 3rd quantile huff distribution, the peak discharges were overestimated by 5% over the peak discharge that considered the probability of occurrence. The overall results indicated that the effect on the peak flood of Huff distribution was less than AMC and reservoir storage.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.3
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• pp.509-521
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• 1994

This study is an effort to develop computer simulation model that produce precipitation patterns from stochastic model. A stochastic model is formulated for the process of daily precipitation with considering the sequences of wet and dry days and the precipitation amounts on wet days. This study consists of 2 papers and the process of precipitation occurrence is modelled by an alternate renewal process (ARP) in paper (I). In the ARP model for the precipitation occurrence, four discrete distributions, used to fit the wet and dry spells, were as follows; truncated binomial distribution (TBD), truncated Poisson distribution (TPD), truncated negative binomial distribution (TNBD), logarithmic series distribution (LSD). In companion paper (II) the process of occurrence is developed by Markov chain. The amounts of precipitation, given that precipitation has occurred, are described by a Gamma. Pearson Type-III, Extremal Type-III, and 3 parameter Weibull distribution. Daily precipitation series model consists of two models, A-Wand A-G model, by combining the process of precipitation occurrence and a continuous probability distribution on the precipitation of wet days. To evaluate the performance of the simulation model, output from the model was compared with historical data of 7 stations in the Nakdong and Seomjin river basin. The results of paper (1) show that it is possible to design a model for the synthetic generation of IX)int precipitation patterns.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.4
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• pp.24-34
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• 2014

In order to manage essential parts that are required for the repairable parts services performed at the military maintenance depots, the United States Air Force developed the Repairability Forecasting Model (RFM). In the RFM, if the requirements of the parts are assumed to follow the normal probability distribution after applying means from the past data to the replacement rate and lead times, the chance of the AWP (Awaiting Parts) occurring is 50%. In this study, to counter the uncertainties of requirements and lead times from the RFM, the safety level concept is considered. To obtain the safety level for requirements, the binomial probability distribution is applied, while the safety level for lead time is obtained by applying the normal probability distribution. After adding this concept, the improved RFM is renamed as the ARFM (Advanced RFM), and by conducting the numerical stimulation, the effectiveness of the ARFM, minimizing the occurrence of the AWP, is shown by increasing the efficiency of the maintenance process and the operating rate of the weapon system.

• Journal of Korea Water Resources Association
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• v.32 no.6
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• pp.627-636
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• 1999

In 60 years when the double $CO_2$concentration is anticipated the average annual rainfall depth is expected to be increased by 5 10% due to global warming. However, in the water resources area the frequency change of meteorological extremes such as droughts and floods attracts more interests than the increase of annual rainfall amount. Even though recent frequent occurrences of this kind of meteorological extremes are assumed as an indirect proof of global warming, the prediction of its overall tendency has not yet been made. Thus, in this research we propose a possible methodology to be used for its prediction. The methodology proposed is based on the frequency distribution of daily rainfall be Todorovie and Woolhiser(1975), and Katz(1977), where the input parameters are modified to consider the change of monthly or annual rainfall depth and, thus, to result in the change of frequency distribution. We adopt two values(10mm, 50mm) as thresholds and investigate the change of occurrence probability due to the change monthly and annual rainfall depth. these changes do not directly indicate the changes of occurrence probability of floods and droughts, but it may still be a very useful information for their prediction. Finally, the changes of occurrence probability were found to be greater when considering the monthly rainfall rather than the annual rainfall, and those in rainy season than those in dry season.

• Journal of Korean Society for Atmospheric Environment
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• v.21 no.3
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• pp.303-313
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• 2005

In this study, meteorological characteristics concerning the occurrence of fog are analyzed using 4-years $(2000\~2003)$ data at Chuncheon and the probability of prediction is investigated. From the analysis of meteorological characteristics, the fog at Chuncheon occurred before sunrise time and disappeared after that time and lasted for $2\~4$ hours. When fog occurred, on the whole, wind direction was blew the northerly and wind speed was below 2.1m/s. Especially, about $42\%$ of foggy day fell on the calm $(0\~0.2\;ms^{-1})$ conditions. The difference between air temperature and dew point temperature near the surface were mainly less than $2^{\circ}C$. For the lack of water surface temperature, the water surface temperature was calculated by using Water Quality River Reservoir System (WQRRS) and then it was used as the surface boundary condition of MM5. The numerical experiment was carried out for 2 days from 1300 LST on 14 October 2003 to 1300 LST on 16 October 2003 and fog was simulated at dawn on 15 and 16 October 2003. Simulated air temperature and dew point temperature indicate the similar tendency to observation and the simulated difference between air temperature and dew point temperature has also the similar tendency within $2^{\circ}C$. Thus, the occurrence of fog is well simulated in the terms of the difference between air temperature and dew point temperature. Horizontal distribution of the difference between air temperature and dew point temperature from the numerical experiment indicates occurrence, dissipation and lasting time of fog at Chuncheon. In Chuncheon, there is close correlation between the frequency of fog day and outflow from Soyang reservoir and high frequency of occurrence due to the difference between air and cold outlet water temperature.

• Journal of Korean Society for Atmospheric Environment
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• v.4 no.2
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• pp.67-71
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• 1988

To estimate the probability of short term concentration of air pollution using long term arithmetic average concentration, the procedure was developed and added to Texas Climatological Model version 2. In the procedure, such statistical characteristics that frequency distribution of short term concentration may be approximated by a lognormal distribution, were applied. This procedure is capable of estimating not only highest concentration for a variety of averaging times but also concentrations for arbitrary occurrence frequency. Evaluation of the procedure with the results of short term concentrations calculated by Texas Episodic Model version 8 using the meteorological data and emission data in Seoul shows that the procedure estimates concentrations fairly well for wide range of percentiles.