• The Korean Journal of Applied Statistics
• /
• v.21 no.6
• /
• pp.1093-1107
• /
• 2008

In this study, we considered linear regression model with various spatial dependency structures in order to make more reliable prediction of precipitation in South Korea. The prediction approaches are based on semi-variogram models fitted by least-squares estimation method and restricted maximum likelihood estimation method. We validated some candidate models from the two different estimation methods in terms of cross-validation and comparison between predicted values and observed values measured at different locations.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.18 no.3
• /
• pp.74-81
• /
• 2019

Herein, we performed a comparative study on approximate multi-objective design optimization, to realize a structural design to improve the weight and vibration performances of the knuckle - a car suspension component - considering various load conditions and vibration characteristics. In the approximate multi-objective optimization process, a regression meta-model was generated using the response surfaces method (RSM), while Kriging and back-propagation neural network (BPN) methods were applied for interpolation meta-modeling. The Pareto solutions, multi-objective optimal solutions, were derived using the non-dominated sorting genetic algorithm (NSGA-II). In terms of the knuckle design considered in this study, the characteristics and influence of the meta-model on multi-objective optimization were reviewed through a comparison of the approximate optimization results with the meta-models and the actual optimization.

• Journal of Urban Science
• /
• v.9 no.2
• /
• pp.31-44
• /
• 2020

This study aims to analyze the relations among greenbelt, air pollution empirically in order to assess the environmental effects of the greenbelt in the Seoul metropolitan area, objectively. For this purpose, this study conducts an empirical analysis of impacts of greenbelt on urban air pollution using a multiple-regression model. The major findings are summarized as follows. As a result of an empirical analysis of the impacts of greenbelt on air pollution, it is found that the characteristics of the city have impacts on air pollution concentration. It is found that the population and employment are the causes of increases in CO and NO2 concentrations, and the number of employees in the manufacturers has impacts on increases of O3 and SO2, while power plants have impacts on PM10, CO and NO2. Intersections have impacts on O3 and SO2, while the areas of the roads have impacts on CO and NO2. In addition, as for the spatial distribution of air pollutants, it is found that CO and NO2 concentrations are relatively higher in the center of the Seoul metropolitan area, while PM10, O3 and SO2 concentrations are relatively higher in the suburbs. It is found that air pollution concentration is low in greenbelt zone. In the greenbelt zone, PM10, CO and SO2 concentrations are low.

• The Korean Journal of Applied Statistics
• /
• v.23 no.2
• /
• pp.345-356
• /
• 2010

In this paper, we introduce the linear regression model taking the parametric spatial association structure into account and employ it to five-year averaged wind speed data measured at 460 meteorological monitoring stations in South Korea. From the prediction map obtained by the model with spatial association parameters, we can see that inland area has smaller wind speed than coastal regions. When comparing the spatial linear regression model with classical one by using one-leave-out cross-validation, the former outperforms the latter in terms of similarity between the observations and the corresponding predictions and coverage rate of 95% prediction intervals.

• Journal of Korea Water Resources Association
• /
• v.42 no.6
• /
• pp.465-480
• /
• 2009

In this study, single and multiple linear regression model were used to derive the relationship between precipitation and altitude, latitude and longitude in Jejudo. The single linear regression analysis was focused on whether orographic effect was existed in Jejudo by annual average precipitation, and the multiple linear regression analysis on whether orographic effect was applied to each duration and return period of quantile from regional frequency analysis by index flood method. As results of the regression analysis, it shows the relationship between altitude and precipitation strongly form a linear relationship as the length of duration and return period increase. The multiple linear regression precipitation estimates(which used altitude, latitude, and longitude information) were found to be more reasonable than estimates obtained using altitude only or altitude-latitude and altitude-longitude. Especially, as results of spatial distribution analysis by kriging method using GIS, it also provides realistic estimates for precipitation that the precipitation was occurred the southeast region as real climate of Jejudo. However, the accuracy of regression model was decrease which derived a short duration of precipitation or estimated high region precipitation even had long duration. Consequently the other factor caused orographic effect would be needed to estimate precipitation to improve accuracy.

• Korean Journal of Soil Science and Fertilizer
• /
• v.43 no.6
• /
• pp.968-974
• /
• 2010

We assessed the feasibility of discrete wavelet transform (DWT) applied for the spectral processing to enhance the estimation performance quality of soil organic matters using visible-near infrared spectra and mapped their distribution via block Kriging model. Continuum-removal and $1^{st}$ derivative transform as well as Haar and Daubechies DWT were used to enhance spectral variation in terms of soil organic matter contents and those spectra were put into the PLSR (Partial Least Squares Regression) model. Estimation results using raw reflectance and transformed spectra showed similar quality with $R^2$ > 0.6 and RPD> 1.5. These values mean the approximation prediction on soil organic matter contents. The poor performance of estimation using DWT spectra might be caused by coarser approximation of DWT which not enough to express spectral variation based on soil organic matter contents. The distribution maps of soil organic matter were drawn via a spatial information model, Kriging. Organic contents of soil samples made Gaussian distribution centered at around 20 g $kg^{-1}$ and the values in the map were distributed with similar patterns. The estimated organic matter contents had similar distribution to the measured values even though some parts of estimated value map showed slightly higher. If the estimation quality is improved more, estimation model and mapping using spectroscopy may be applied in global soil mapping, soil classification, and remote sensing data analysis as a rapid and cost-effective method.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.36 no.6
• /
• pp.541-549
• /
• 2008

The fatigue analysis is performed to avoid structural failure in aerospace structures under repeated loads. In this paper, the fatigue life is estimated for the design of tilt rotor UAV. First of all, the fatigue load spectrum for tilt rotor UAV is generated. Fatigue analysis is done for the flaperon joint which may have FCL(fracture critical location). Tilt rotor UAV operates at two modes: helicopter mode such as taking off and landing; fixed wing mode like cruising. To make overall fatigue load spectrum, FELIX is used for helicopter mode and TWIST is used for fixed wing mode. The other hand, the Kriging meta model is used to get S-N regression curve for whole range of material life when S-N test data are analyzed. And then, the second order of S-N curve is accomplished by the least square method. In addition, the coefficient of determination method is used to ensure how accuracy it has. Finally, the fatigue life of flaperon joint is compared with that obtained by MSC. Fatigue.

• Journal of Korean Society of Transportation
• /
• v.35 no.3
• /
• pp.220-233
• /
• 2017

Traffic volumes are fundamental data widely used in various traffic analysis, such as origin-and-destination establishment, total traveled kilometer distance calculation, congestion evaluation, and so on. The low number of links collecting the traffic-volume data in a large urban highway network has weakened the quality of the analyses in practice. This study proposes a method to estimate the traffic volume data on a highway link where no collection device is available by introducing a spatial statistic technique with (1) the traffic-volume data from TOPIS, and National Transport Information Center in the Ministry of Land, Infrastructure, and (2) the navigation data from private navigation. Two different component models were prepared for the interrupted and the uninterrupted flows respectively, due to their different traffic-flow characteristics: the piecewise constant function and the regression kriging. The comparison of the traffic volumes estimated by the proposed method against the ones counted in the field showed that the level of error includes 6.26% in MAPE and 5,410 in RMSE, and thus the prediction error is 20.3% in MAPE.

• The Journal of Engineering Geology
• /
• v.9 no.2
• /
• pp.147-159
• /
• 1999

Geostatistical methods were used for the groundwater flow analysis in a heterogeneous anisotropic aquifer. This study area is located at Sonbul-myeon in Hampyong-gun of Cheonnam Province which is a hydrogeological project area of KORES(Korea Resources Cooperation). Linear regression analysis shows that the topographic elevation and groundwater level of this area have very high correlation. Groundwater-level contour maps produced by ordinary kriging and cokringing have large differences in mountain areas, but small differences in hill and plain areas near the West Sea. Comparing two maps on the basis of an elevation contour map, a groundwater-level contour map using cokriging is more accurate. Analyzing the groundwater flow on two groundwater-level contour maps, the groundwater of study area flows from the high mountain areas to the plain areas near the West Sea. To verify the enffectiveness of geostatistical methods for the groundwater flow analysis in a heterogeneous anisotropic aquifer, the flow directions of groundwater were measured at two groundwater boreholes by a groundwater flowmeter system(model 200 $GeoFlo^{R}$). The measured flow directions of groundwater almost accord with those estimated on two groundwater-level contour maps produced by geostatistical methods.

• Korean Journal of Remote Sensing
• /
• v.36 no.4
• /
• pp.587-607
• /
• 2020

Soil moisture is essential information for meteorological and hydrological analyses. To date, many efforts have been made to achieve the two goals for soil moisture data, i.e., the improvement of accuracy and resolution, which is very challenging. We presented an ensemble downscaling method for quality improvement of gridded soil moisture data in terms of the accuracy and the spatial resolution by the integration of BMA (Bayesian model averaging) and ATPRK (area-to-point regression kriging). In the experiments, the BMA ensemble showed a 22% better accuracy than the data sets from ESA CCI (European Space Agency-Climate Change Initiative), ERA5 (ECMWF Reanalysis 5), and GLDAS (Global Land Data Assimilation System) in terms of RMSE (root mean square error). Also, the ATPRK downscaling could enhance the spatial resolution from 0.25° to 0.05° while preserving the improved accuracy and the spatial pattern of the BMA ensemble, without under- or over-estimation. The quality-improved data sets can contribute to a variety of local and regional applications related to soil moisture, such as agriculture, forest, hydrology, and meteorology. Because the ensemble downscaling method can be applied to the other land surface variables such as temperature, humidity, precipitation, and evapotranspiration, it can be a viable option to complement the accuracy and the spatial resolution of satellite images and numerical models.