• Journal of Wetlands Research
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• v.6 no.3
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• pp.119-126
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• 2004

In recent, the critical rainfall duration concept is widely used but we do not have understandable criteria yet. However, the critical rainfall duration is usually calculated considering concentration time, runoff model using effective rainfall, and unit hydrograph for the estimation of design flood. This study is to derive the regression equations between the critical rainfall duration and hydrologic components such as the basin area, slope, length, CN, and so on. We use a GIS tool which is called the ArcView for the estimation of hydrologic components and the HEC-1 module which is provided in WMS model is used for the runoff computation. As the results, the basin area, basin slope, and basin length had a great influence on the estimations of peak runoff and critical rainfall duration. We also investigated the sensitivities for the peak runoff and critical duration of rainfall from the correlation analysis for the involved components in the runoff estimation.

• International Journal of Highway Engineering
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• v.12 no.1
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• pp.21-27
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• 2010

Management of driver hours of service (HOS) for commercial vehicle operators has been a continual safety challenge. One of the more critical issues to government and motor carriers is fatigue and fatigue-related accidents. To reduce truck drivers’fatigue-related accident risk in other countries, the government issued the HOS regulations. However, korea government does not have any HOS regulations. The objective of this research gives the clues that korea should have the HOS regulation to reduce truck drivers’fatigue-related accident risk. This study examines the HOS regulation over other countries and conducts relative accident risk analysis using the real data from 3 freight companies. The data set includes 231 accident involved drivers and 462 non-accident drivers. Therefore, the size of the total data set is 693 drivers. One of the most important aspects of early studies of safety and HOS was the need to characterize continuous driving by using the notion of "survival". Subsequent research used a data replication scheme and logistic regression to capture the survival effect. This study uses time-dependent logistic regression. The test of significance between parameters indicates that the first three hours are almost the same risk. In the 10th hour of driving, the risk was more than 2.2times that in the baseline first hour. In conclusion, as driving time goes on, the crash risk increases.

• Journal of Korea Water Resources Association
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• v.54 no.9
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• pp.731-745
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• 2021

In this study, the monthly temperature of the Han River basin was predicted by statistical multiple regression models that use global climate indices and weather data of the target region as predictors. The optimal predictors were selected through teleconnection analysis between the monthly temperature and the preceding patterns of each climate index, and forecast models capable of predicting up to 12 months in advance were constructed by combining the selected predictors and cross-validating the past period. Fore each target month, 1000 optimized models were derived and forecast ranges were presented. As a result of analyzing the predictability of monthly temperature from January 1992 to December 2020, PBIAS was -1.4 to -0.7%, RSR was 0.15 to 0.16, NSE was 0.98, and r was 0.99, indicating a high goodness-of-fit. The probability of each monthly observation being included in the forecast range was about 64.4% on average, and by month, the predictability was relatively high in September, December, February, and January, and low in April, August, and March. The predicted range and median were in good agreement with the observations, except for some periods when temperature was dramatically lower or higher than in normal years. The quantitative temperature forecast information derived from this study will be useful not only for forecasting changes in temperature in the future period (1 to 12 months in advance), but also in predicting changes in the hydro-ecological environment, including evapotranspiration highly correlated with temperature.

• The Korean Journal of Applied Statistics
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• v.36 no.1
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• pp.49-62
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• 2023

Solar energy, which is rapidly increasing in proportion, is being continuously developed and invested. As the installation of new and renewable energy policy green new deal and home solar panels increases, the supply of solar energy in Korea is gradually expanding, and research on accurate demand prediction of power generation is actively underway. In addition, the importance of solar radiation prediction was identified in that solar radiation prediction is acting as a factor that most influences power generation demand prediction. In addition, this study can confirm the biggest difference in that it attempted to predict solar radiation using medium-term forecast weather data not used in previous studies. In this paper, we combined the multi-linear regression model, KNN, random fores, and SVR model and the clustering technique, K-means, to predict solar radiation by hour, by calculating the probability density function for each cluster. Before using medium-term forecast data, mean absolute error (MAE) and root mean squared error (RMSE) were used as indicators to compare model prediction results. The data were converted into daily data according to the medium-term forecast data format from March 1, 2017 to February 28, 2022. As a result of comparing the predictive performance of the model, the method showed the best performance by predicting daily solar radiation with random forest, classifying dates with similar climate factors, and calculating the probability density function of solar radiation by cluster. In addition, when the prediction results were checked after fitting the model to the medium-term forecast data using this methodology, it was confirmed that the prediction error increased by date. This seems to be due to a prediction error in the mid-term forecast weather data. In future studies, among the weather factors that can be used in the mid-term forecast data, studies that add exogenous variables such as precipitation or apply time series clustering techniques should be conducted.

• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.269-278
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• 2011

There are many researches that have considered the distribution functions and appropriate covariates corresponding to the scores in order to improve the accuracy of a diagnostic test, including the ROC curve that is represented with the relations of the sensitivity and the specificity. The ROC analysis was used by the regression model including some covariates under the assumptions that its distribution function is known or estimable. In this work, we consider a general situation that both the distribution function and the elects of covariates are unknown. For the ROC analysis, the mixtures of normal distributions are used to estimate the distribution function fitted to the credit evaluation data that is consisted of the score random variable and two sub-populations of parameters. The AUC measure is explored to compare with the nonparametric and empirical ROC curve. We conclude that the method using normal mixtures is fitted to the classical one better than other methods.

• Journal of Technology Innovation
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• v.18 no.1
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• pp.99-121
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• 2010

The goal of this paper is to analyze the effects of various firm characteristics on the probability for a firm to receive government’s financial supports for R&D. In the empirical analysis, a Probit model is estimated for the 2008 Korea Innovation Survey data. The main contribution of the paper is to investigate the distribution of R&D supports at the national level, instead of the program level. Especially, it is the first academic effort to evaluate the effects of regional and industrial variables. The results show that: (1) firm size and export increase the probability of receiving government’s R&D support; (2) variables measuring firms’ innovative ability, such as official designation as innovative firm, running R&D institute, number of R&D personnel, also have significantly positive effects; (3) firms in the chemical and automobile industries are more likely to receive R&D supports; and (4) firms in Teakyoung and Bukyoung regions are more likely to receive R&D supports.

• Journal of the Korean Geotechnical Society
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• v.35 no.6
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• pp.17-26
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• 2019

In this study, the statistical characteristics of the resistance bias factors were analyzed using a high-quality field load test database, and the total resistance bias factors were estimated considering the soil uncertainty and construction errors for the application of the limit state design of aggregate pier foundation. The MLR model by Bong and Kim (2017), which has a higher prediction performance than the previous models was used for estimating the resistance bias factors, and its suitability was evaluated. The chi-square goodness of fit test was performed to estimate the probability distribution of the resistance bias factors, and the normal distribution was found to be most suitable. The total variability in the nominal resistance was estimated including the uncertainty of undrained shear strength and construction errors that can occur during the aggregate pier construction. Finally, the probability distribution of the total resistance bias factors is shown to follow a log-normal distribution. The parameters of the probability distribution according to the coefficient of variation of total resistance bias factors were estimated by Monte Carlo simulation, and their regression equations were proposed for simple application.

• Korean Journal of Social Welfare
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• v.67 no.2
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• pp.137-159
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• 2015

This longitudinal study examined the influence of substandard housing conditions and housing affordability on physical and mental health. Using data from the Korea Welfare Panel Study, this study followed 8,583 adults who continued to participate in the survey from 2009 to 2013. Multivariate analyses involved linear and logistic regression models with the hybrid method that incorporates both fixed and random effects. Results show that substandard housing conditions and excess housing cost burden had significant adverse effects on adults' mental health (e.g., depressive symptoms). About one fourth of the entire sample and one third of those in poverty reported having lived in substandard housing conditions. Additionally, nearly one fourth of those in poverty reported having experienced excess housing cost burden, which is 4 times greater than that of the entire sample. Our findings show that a substantial proportion of individuals, particularly among the poor, have a difficulty in accessing to decent, affordable housing, and that housing assistance may have additional benefits of improving the mental health of individuals with housing issues.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.4
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• pp.89-101
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• 2017

Crime occurs differently based on not only place locations and building uses but also the characteristics of the people who use the place and the spatial structures of the buildings and locations. Therefore, if spatial big data, which contain spatial and regional properties, can be utilized, proper crime prevention measures can be enacted. Recently, with the advent of big data and the revolutionary intelligent information era, predictive policing has emerged as a new paradigm for police activities. Based on 7420 actual crime incidents occurring over three years in a typical provincial city, "J city," this study identified the areas in which crimes occurred and predicted risky areas. Spatial regression analysis was performed using spatial big data about only physical and environmental variables. Based on the results, using the street width, average number of building floors, building coverage ratio, the type of use of the first floor (Type II neighborhood living facility, commercial facility, pleasure use, or residential use), this study established a Crime Incident Prediction Model (CIPM) based on Bayesian probability theory. As a result, it was found that the model was suitable for crime prediction because the overlap analysis with the actual crime areas and the receiver operating characteristic curve (Roc curve), which evaluated the accuracy of the model, showed an area under the curve (AUC) value of 0.8. It was also found that a block where the commercial and entertainment facilities were concentrated, a block where the number of building floors is high, and a block where the commercial, entertainment, residential facilities are mixed are high-risk areas. This study provides a meaningful step forward to the development of a crime prediction model, unlike previous studies that explored the spatial distribution of crime and the factors influencing crime occurrence.

• Journal of Technology Innovation
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• v.17 no.2
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• pp.55-80
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• 2009

Technology innovation activity plays a pivotal role in constructing the entrance barrier for other firms and making process improvement and new product. and these activities give a profit increase and growth to firms. Thus, technology innovation activity can reduce the default risk of firms. However, technology innovation activity can also increase the firm's default risk because technology innovation activity requires too much investment of the firm's resources and has the uncertainty on success. The purpose of this study is to examine the effect of technology innovation activity on the default risk of firms. This study's sample consists of manufacturing firms listed on the Korea Securities Market and The Kosdaq Market from January 1,2000 to December 31, 2008. This study makes use of R&D intensity as an proxy variable of technology innovation activity. The default probability which proxies the default risk of firms is measured by the Merton's(l974) debt pricing model. The main empirical results are as follows. First, from the empirical results, it is found that technology innovation activity has a negative and significant effect on the default risk of firms independent of the Korea Securities Market and Kosdaq Market. In other words, technology innovation activity reduces the default risk of firms. Second, technology innovation activity reduces the default risk of firms independent of firm size, firm age, and credit score. Third, the results of robust analysis also show that technology innovation activity is the important factor which decreases the default risk of firms. These results imply that a manager must show continuous interest and investment in technology innovation activity of one's firm. And a policymaker also need design an economic policy to promote the technology innovation activity of firms.