• Spatial Information Research
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• v.18 no.5
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• pp.107-117
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• 2010

Taking the East-Hwasung area as the case, this study first builds gridded population data based on the municipal population survey raw data, and then measures, by way of GIS tools, the major urban spatial variables that are thought to influence the composition of the regional population. For the purpose of comparison, the urban models based on the Genetic Algorithm technique and the regression technique are constructed using the same input variables. The findings indicate that the GA output performed better in differentiating the effective variables among the pilot model variables, and predicted as much consistent and meaningful coefficient estimates for the explanatory variables as the regression models. The study results indicate that GA technique could be a very useful and supplementary research tool in understanding the urban phenomena.

• Journal of Environmental Science International
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• v.22 no.8
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• pp.953-963
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• 2013

Reliable long-term streamflow forecasting is invaluable for water resource planning and management which allocates water supply according to the demand of water users. Forecasting of seasonal inflow to Andong dam is performed and assessed using statistical methods based on hydrometeorological data. Predictors which is used to forecast seasonal inflow to Andong dam are selected from southern oscillation index, sea surface temperature, and 500 hPa geopotential height data in northern hemisphere. Predictors are selected by the following procedure. Primary predictors sets are obtained, and then final predictors are determined from the sets. The primary predictor sets for each season are identified using cross correlation and mutual information. The final predictors are identified using partial cross correlation and partial mutual information. In each season, there are three selected predictors. The values are determined using bootstrapping technique considering a specific significance level for predictor selection. Seasonal inflow forecasting is performed by multiple linear regression analysis using the selected predictors for each season, and the results of forecast using cross validation are assessed. Multiple linear regression analysis is performed using SAS. The results of multiple linear regression analysis are assessed by mean squared error and mean absolute error. And contingency table is established and assessed by Heidke skill score. The assessment reveals that the forecasts by multiple linear regression analysis are better than the reference forecasts.

• Spatial Information Research
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• v.22 no.1
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• pp.55-63
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• 2014

Near surface air temperature data which are one of the essential factors in hydrology, meteorology and climatology, have drawn a substantial amount of attention from various academic domains and societies. Meteorological observations, however, have high spatio-temporal constraints with the limits in the number and distribution over the earth surface. To overcome such limits, many studies have sought to estimate the near surface air temperature from satellite image data at a regional or continental scale with simple regression methods. Alternatively, we applied various Kriging methods such as ordinary Kriging, universal Kriging, Cokriging, Regression Kriging in search of an optimal estimation method based on near surface air temperature data observed from automatic weather stations (AWS) in South Korea throughout 2010 (365 days) and MODIS land surface temperature (LST) data (MOD11A1, 365 images). Due to high spatial heterogeneity, auxiliary data have been also analyzed such as land cover, DEM (digital elevation model) to consider factors that can affect near surface air temperature. Prior to the main estimation, we calculated root mean square error (RMSE) of temperature differences from the 365-days LST and AWS data by season and landcover. The results show that the coefficient of variation (CV) of RMSE by season is 0.86, but the equivalent value of CV by landcover is 0.00746. Seasonal differences between LST and AWS data were greater than that those by landcover. Seasonal RMSE was the lowest in winter (3.72). The results from a linear regression analysis for examining the relationship among AWS, LST, and auxiliary data show that the coefficient of determination was the highest in winter (0.818) but the lowest in summer (0.078), thereby indicating a significant level of seasonal variation. Based on these results, we utilized a variety of Kriging techniques to estimate the surface temperature. The results of cross-validation in each Kriging model show that the measure of model accuracy was 1.71, 1.71, 1.848, and 1.630 for universal Kriging, ordinary Kriging, cokriging, and regression Kriging, respectively. The estimates from regression Kriging thus proved to be the most accurate among the Kriging methods compared.

• Journal of the Korean earth science society
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• v.38 no.4
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• pp.269-282
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• 2017

In this study, the surface broadband emissivity ($3.0-14.0{\mu}m$) was calculated using the multiple linear regression model with narrow bands (channels 29, 30, and 31) emissivity data of the Moderate Resolution Imaging Spectroradiometer (MODIS) on Earth Observing System Terra satellite. The 307 types of spectral emissivity data (123 soil types, 32 vegetation types, 19 types of water bodies, 43 manmade materials, and 90 rock) with MODIS University of California Santa Barbara emissivity library and Advanced Spaceborne Thermal Emission & Reflection Radiometer spectral library were used as the spectral emissivity data for the derivation and verification of the multiple linear regression model. The derived determination coefficient ($R^2$) of multiple linear regression model had a high value of 0.95 (p<0.001) and the root mean square error between these model calculated and theoretical broadband emissivities was 0.0070. The surface broadband emissivity from our multiple linear regression model was comparable with that by Wang et al. (2005). The root mean square error between surface broadband emissivities calculated by models in this study and by Wang et al. (2005) during January was 0.0054 in Asia, Africa, and Oceania regions. The minimum and maximum differences of surface broadband emissivities between two model results were 0.0027 and 0.0067 respectively. The similar statistical results were also derived for August. The surface broadband emissivities by our multiple linear regression model could thus be acceptable. However, the various regression models according to different land covers need be applied for the more accurate calculation of the surface broadband emissivities.

• Korean Journal of Remote Sensing
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• v.33 no.2
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• pp.135-147
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• 2017

We, for the first time, estimated daily and monthly surface nitrogen dioxide ($NO_2$) volume mixing ratio (VMR) using three regression models with $NO_2$ tropospheric vertical column density (OMIT-rop $NO_2$ VCD) data obtained from Ozone Monitoring Instrument (OMI) in Seoul in South Korea at OMI overpass time (13:45 local time). First linear regression model (M1) is a linear regression equation between OMI-Trop $NO_2$ VCD and in situ $NO_2$ VMR, whereas second linear regression model (M2) incorporates boundary layer height (BLH), temperature, and pressure obtained from Atmospheric Infrared Sounder (AIRS) and OMI-Trop $NO_2$ VCD. Last models (M3M & M3D) are a multiple linear regression equations which include OMI-Trop $NO_2$ VCD, BLH and various meteorological data. In this study, we determined three types of regression models for the training period between 2009 and 2011, and the performance of those regression models was evaluated via comparison with the surface $NO_2$ VMR data obtained from in situ measurements (in situ $NO_2$ VMR) in 2012. The monthly mean surface $NO_2$ VMRs estimated by M3M showed good agreements with those of in situ measurements(avg. R = 0.77). In terms of the daily (13:45LT) $NO_2$ estimation, the highest correlations were found between the daily surface $NO_2$ VMRs estimated by M3D and in-situ $NO_2$ VMRs (avg. R = 0.55). The estimated surface $NO_2$ VMRs by three modelstend to be underestimated. We also discussed the performance of these empirical modelsfor surface $NO_2$ VMR estimation with respect to otherstatistical data such asroot mean square error (RMSE), mean bias, mean absolute error (MAE), and percent difference. This present study shows a possibility of estimating surface $NO_2$ VMR using the satellite measurement.

• Journal of ILASS-Korea
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• v.6 no.4
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• pp.22-30
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• 2001

The objective of this study is to develop the new equations for the calculation of higher heating values(HHVs) of reformed diesel fuels by ultrasonic treatment. Therefore, higher heating values of reformed diesel fuels by ultrasonic treatment are determined experimentally and calculated from their viscosity and surface tension measurements. The HHVs of the fuels are supposed to be a function of viscosity(Pa s) and surface tension(N/cm). The equations developed for the samples represent the correlation obtained by means of regression analysis. The HHVs calculated by developing new equations using viscosities showes the differences from the measured values ranging from -0.66 to 1.19 % and the correlation coefficient was -0.9411. The HHVs calculated by developing new equations using surface tensions showed the differences from the measured values ranging from -0.70 to 1.51 % and the correlation coefficient was 0.9999. The viscosity and the surface tension are characteristic properties of ultrasonic reformed diesel fuels for developing new formulae.

• Korean Journal of Child Studies
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• v.31 no.5
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• pp.17-30
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• 2010

The purpose of this study was to investigate the relationship between surface/deep acting in emotion labor, the collectivism and the organizational commitment of teachers in kindergartens and childcare centers. The subjects were 212 teachers employed in kindergartens or childcare centers in Daegu and Gyeongbuk Province. The collected data were analyzed by mean of Pearson's correlation, simple regression, hierarchial regression, by the use of SPSS Win 15.0. The results of our study are as follows. (1) Collectivism was positively associated with deep acting in emotion labor but not with surface acting. (2) Organizational commitment was positively associated with deep acting in emotion labor but not with surface acting. (3) Collectivism operated as a main effect with organizational commitment being observed to increase as collectivism increased. In addition, interaction effects of deep acting in emotion labor and collectivism on organizational commitment were observed, however, no interaction effects were seen in term of surface acting in emotion labor and collectivism on organizational commitment.

• Geomechanics and Engineering
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• v.16 no.6
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• pp.635-642
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• 2018

Braced excavation systems are commonly required to ensure stability in construction of basements for shopping malls, underground transportation and other habitation facilities. For excavations in deposits of soft clays or residual soils, stiff retaining wall systems such as diaphragm walls are commonly adopted to restrain the ground movements and wall deflections in order to prevent damage to surrounding buildings and utilities. The ground surface settlement behind the excavation is closely associated with the magnitude of basal heave and the wall deflections and is also greatly influenced by the possible groundwater drawdown caused by potential wall leakage, flow from beneath the wall, flow from perched water and along the wall interface or poor panel connections due to the less satisfactory quality. This paper numerically investigates the influences of excavation geometries, the system stiffness, the soil properties and the groundwater drawdown on ground surface settlement and develops a simplified maximum surface settlement Logarithm Regression model for the maximum ground surface settlement estimation. The settlements estimated by this model compare favorably with a number of published and instrumented records.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.852-857
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• 2004

A new and efficient method for estimating the statistical moments of a system performance function has been developed. The method consists of two steps: (1) An approximate response surface is generated by a quadratic regression model, and (2) the statistical moments of the regression model are then calculated by experimental design techniques proposed by Seo and $Kwak^{(4)}$. In this approach, the size of experimental region affects the accuracy of the statistical moments. Therefore, the region size should be selected suitably. The D-optimal design and the central composite design are adopted over the selected experimental region for the regression model. Finally, the Pearson system is adopted to decide the distribution type of the system performance function and to analyze structural reliability.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.105-110
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• 2004

This paper deals with the study of feasibility of rotary carbide tools in the machining of aluminium alloy. A rotary tool holder was designed and manufactured for this work. Experiments were performed using Taguchi methods and regression analysis to analyse the influence of various factors and their interactions on the cutting characteristics of rotary carbide tools during machining. The cutting force is influenced the most featly at the inclination angle. The surface roughness is influenced distinctly at depth of cut. It deduced an equation to predict cutting force and surface roughness. Hence, it could be concluded here that the proposed model agrees with the experimental data satisfactorily.