• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.507-514
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• 2018

TSI (TPI-Slope Index) which is the combination of TPI (Topographic Position Index) and slope was newly proposed for landslide and applied to a landslide susceptibility model. To do this, we first compared the TPIs with various scale factors and found that TPI350 was the best fit for the study area. TPI350 was combined with slope to create TSI. TSI was evaluated using logistic regression. The evaluation showed that TSI can be used as a landslide factor. Then a logistic regression model was developed to assess the landslide susceptibility by adding other topographic factors, geological factors, and forestial factors. For this, landslide-related factors that can be extracted from DEM (Digital Elevation Model), soil map, and forest type map were collected. We checked these factors and excluded those that were highly correlated with other factors or not significant. After these processes, 8 factors of TSI, elevation, slope length, slope aspect, effective soil depth, tree age, tree density, and tree type were selected to be entered into the regression analysis as independent variables. Three models through three variable selection methods of forward selection, backward elimination, and enter method were built and evaluated. Selected variables in the three models were slightly different, but in common, effective soil depth, tree density, and TSI was most significant.

• Journal of Korea Water Resources Association
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• v.55 no.4
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• pp.257-266
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• 2022

Inundation damage is increasing every year due to localized heavy rain and an increase of rainfall exceeding the design frequency. Accordingly, the importance of hydraulic structures for flood control and defense is also increasing. The hydraulic structures are designed according to its purpose and performance, and the amount of flood is an important calculation factor. However, in Korea, design rainfall is used as input data for hydrological analysis for the design of hydraulic structures due to the lack of sufficient data and the lack of reliability of observation data. Accurate probability rainfall and its temporal distribution are important factors to estimate the design rainfall. In practice, the regression equation of temporal distribution for the design rainfall is calculated using the cumulative rainfall percentage of Huff's quartile method. In addition, the 6^th order polynomial regression equation which shows high overall accuracy, is uniformly used. In this study, the optimized regression equation of temporal distribution is derived using the variable selection method according to the principle of parsimony in statistical modeling. The derived regression equation of temporal distribution is verified through the significance test. As a result of this study, it is most appropriate to derive the regression equation of temporal distribution using the stepwise selection method, which has the advantages of both forward selection and backward elimination.

• Journal of agricultural medicine and community health
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• v.39 no.3
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• pp.176-186
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• 2014

Objectives: As a sequel to the former analysis of the quality of life (QoL) among young-old and old-old in Korea, this research was aimed to identify factors related to the quality of life and the gender difference after controlling for the related factors among Korean elderly. Methods: Selected elderly data of 1,339 subjects from the 5th Korea National Health and Nutrition Examination Survey conducted in 2010 was analyzed. In this survey, QoL was measured using Euro Quality of Life (EQ-5D) instrument. Data were analyzed using complex survey data analysis on IBM-SPSS 20.0. The related factors were identified using general linear models with backward elimination. The gender difference was tested also using general linear models. Results: The distributions of educational level, family income level, and presence of cohabitant were different between male and female elderly in both young-old and old-old age group. So were the health behaviors and perceived health, and experience of stress, depression, and suicidal thoughts. QoL and its subscales- mobility, self care, daily living, pain and discomfort, and anxiety and depression- were consistently better among male elderly regardless of age group. Among the variables considered, education, family income level, presence of cohabitant, perceived health, age group and BMI were found to be related to the QoL at p=.05, and presence of chronic diseases at p=.10. The difference in QoL between male and female elderly after controlling for the variables was statistically significant. Conclusion: Improving QoL is particularly important for the elderly. In order to improve QoL of the elderly, age- and gender- differences need to be considered when developing services and programs for the elderly.

• Journal of the Korean Institute of Landscape Architecture
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• v.44 no.3
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• pp.1-12
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• 2016

This study aimed to interpret the fundamental cause of road-kill occurrences and analyzed spatial characteristics of the road-kill locations from Route 44 in Seorak National Park, Korea. Logistic regression analysis was utilized for backward elimination on variables. Seorak National Park Service has constructed GIS-data of 81 road-kill occurrences from 2008 to 2013 and these data were assigned as dependent variables in this study. Considered as independent variables from previous studies and field surveys, vegetation age-class, distance to streams, coverage of fences and retaining walls, and distance to building sites were assigned as road-kill impact factors. The coverage of fences and retaining walls(-1.0135) was shown as the most influential factor whereas vegetation age-class(0.0001) was the least influential among all of the significant factor estimates. Accordingly, the rate of road-kill occurrence can increase as the distance to building sites and stream becomes closer and vegetation age-class becomes higher. The predictive accuracy of road-kill occurrence was shown to be 72.2% as a result of analysis, assuming as partial causes of road-kill occurrences reflecting spatial characteristics. This study can be regarded as beneficial to provide objective basis for spatial decision making including road-kill occurrence mitigation policies and plans in the future.

• Journal of Dental Rehabilitation and Applied Science
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• v.38 no.2
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• pp.97-109
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• 2022

Purpose: This study aims to investigate the risk indicators contributing to implant failure, and analyze the relationship between risk indicators and marginal bone loss (MBL) through long-term follow-up over 3 years. Materials and Methods: From 2003 to 2017, patients' medical charts with a history of dental implant surgery at Chonnam National University Dental Hospital were reviewed retrospectively. The patient's demographic variables, and clinical variables were recorded. Periapical radiographs were used to evaluated the changes in MBL around implants. And we analyzed implant survival rates. Multiple regression analysis with backward elimination was conducted to correlate the patient's clinical variables and implant failure and Pearson correlation analysis was performed to the correlated between implant long-term survival rates and MBL and initial stability. Results: In multiple regression analysis, there was a statistically significant negative correlation between abutment connection type (β = -.189, P < .05), with or without SPT (β = -.163, P < .05), diabetes (β = -.164, P < .05), osteoporosis (β = -.211, P < .05) and MBL. Anticoagulant medication influenced the long-term success rate of implants. PTV values at the second implant surgery showed a statistically significant negative correlation with long-term implant survival (P < .05). Conclusion: For the long-term success of the implant, the appropriate abutment connection type must be selected and the periodic SPT is recommended. Systemic diseases such as diabetes and osteoporosis and anticoagulant medication should be considered. Furthermore, since high PTV at the second implant surgery correlated with the long-term survival rates of the implant, initial stability should be carefully considered before undergoing the prosthetic procedure.

• Korean Journal of Soil Science and Fertilizer
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• v.23 no.2
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• pp.87-93
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• 1990

A statistical model to predict soil temperature from the ambient meteorological factors including mean, maximum and minimum air temperatures, precipitation, wind speed and snow depth combined with Fourier time series expansion was developed with the data measured at the Suwon Meteorolical Service from 1979 to 1988. The stepwise elimination technique was used for statistical analysis. For the yearly oscillation model for soil temperature with 8 terms of Fourier expansion, the mean square error was decreased with soil depth showing 2.30 for the surface temperature, and 1.34-0.42 for 5 to 500-cm soil temperatures. The $r^2$ ranged from 0.913 to 0.988. The number of lag days of air temperature by remainder analysis was 0 day for the soil surface temperature, -1 day for 5 to 30-cm soil temperature, and -2 days for 50-cm soil temperature. The number of lag days for precipitaion, snow depth and wind speed was -1 day for the 0 to 10-cm soil temperatures, and -2 to -3 days for the 30 to 50-cm soil teperatures. For the statistical soil temperature prediction model combined with the yearly oscillation terms and meteorological factors as remainder terms considering the lag days obtained above, the mean square error was 1.64 for the soil surfac temperature, and ranged 1.34-0.42 for 5 to 500cm soil temperatures. The model test with 1978 data independent to model development resulted in good agreement with $r^2$ ranged 0.976 to 0.996. The magnitudes of coeffcicients implied that the soil depth where daily meteorological variables night affect soil temperature was 30 to 50 cm. In the models, solar radiation was not included as a independent variable ; however, in a seperated analysis on relationship between the difference(${\Delta}Tmxs$) of the maximum soil temperature and the maximum air temperature and solar radiation(Rs ; $J\;m^{-2}$) under a corn canopy showed linear relationship as $${\Delta}Tmxs=0.902+1.924{\times}10^{-3}$$ Rs for leaf area index lower than 2 $${\Delta}Tmxs=0.274+8.881{\times}10^{-4}$$ Rs for leaf area index higher than 2.