• Journal of the Korean Solar Energy Society
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• v.37 no.6
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• pp.79-91
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• 2017

The meteorological data of 1 hour interval are required to write a typical weather data for building energy simulation. However, many meterological data are missing and the interpolation method to recover the missing data is required. Especially, lots of meterological data are replicated by linear interpolation method because the changes are not significant. While, the wind velocity fluctuates with the time or locations, so linear interpolation method is not appropriate in interpolation of the wind velocity data. In this study, three interpolation methods, using surrounding wind velocity data, Inverse Distance Weighting (IDW), Revised Inverse Distance Weighting (IDW-r), were analyzed considering the characteristics of wind velocity. The Revised Inverse Distance Weighting method, proposed in this study, showed the highest reliability in restoration of the wind velocity data among the analyzed methods.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.2
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• pp.124-132
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• 2022

This study was aimed to visualize the spatial distribution of PM₁₀ data measured at non-uniformly distributed observation sites in South Korea. Different spatial interpolation methods were applied to irregularly distributed PM₁₀ observation data from January, 2019, when the concentration was the highest and in July, 2019, when the concentration was the lowest. Four interpolation methods with different parameters were used: Inverse Distance Weighted (IDW), Ordinary Kriging (OK), radial base function, and scattered interpolation. Six cases were cross-validated and the normalized root-mean-square error for each case was compared. The results showed that IDW using smoothing-related factors was the most appropriate method, while the OK method was least appropriate. Our results are expected to help users select the proper spatial interpolation method for PM₁₀ data analysis with comparative reliability and effectiveness.

• Journal of The Korean Society of Agricultural Engineers
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• v.53 no.5
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• pp.1-7
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• 2011

The objective of this study is to decide a spatial interpolation method on temperature data for the suitability analysis of garlic cultivation. In Korea, garlic is the second most cultivated condiment vegetable after red pepper. Nowadays warm-temperate garlic faces potential shift of its arable area according to warmer temperature in the Korean Peninsula, and the change can be drawn with the precise temperature map derived from interpolation on point-measured data. To find the preferable interpolation method in cases of germination and vegetative period of the garlic, different approaches were tested as follows: Inverse Distance Weighted (IDW), Spline, Ordinary Kriging (OK), and Universal Kriging (UK). As a result, IDW and UK show the lowest root mean square errors as for the germination and vegetative seasons, respectively. However, statistically significant difference was not revealed among the applied methods regarding the germinating period. Eventually this will contribute to mapping the suitable lands for the cultivation of warm-temperate garlic reasonably.

• Spatial Information Research
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• v.14 no.2 s.37
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• pp.223-234
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• 2006

In order to examine spatio-temporal characteristics of coastal water quality, we applied GIS spatial analysis to the water quality data collected from observation points located on Korean coastal area during 1997$\sim$2004. The water quality parameters measured included: chlorophyll-a, pH, DO, COD, SS, dissolved inorganic nitrogen, dissolved inorganic phosphorous, salinity, temperature. The water quality data used in this paper was obtained only at selected sites even though they are potentially available at any location in a continuous surface. Thus, it is necessary to estimate the values at unsampled locations so as to analyze spatial distribution patterns of coastal water quality, Owing to this reason, we applied IDW(inverse distance weighted) interpolation method to water quality data and evaluated the usefulness of IDW method. After IDW interfolation method was applied, we divided the Korean coastal area into 46 sections and examined spatio-temporal patterns of each section using GIS visualization technique. As a result of evaluation, we can blow that IDW interpolation and GIS are useful for understanding spatial and temporal distribution characteristics of coastal water quality data which is collected from a wide area far many years.

• Journal of The Korean Society of Agricultural Engineers
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• v.53 no.6
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• pp.23-29
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• 2011

This study was conducted to identify the effect of lapse rate application according to elevation on the estimation of large scale watershed rainfall. For the Han river basin (26,018 $km^2$), the 11 years (2000-2010) daily rainfall data from 108 AWS (Automatic Weather Station) were collected. Especially, the 11 heavy rain and typhoon events from 2004 to 2009 were selected for trend analysis. The elevation effect by IDW (Inverse Distance Weights) interpolation showed the change up to +62.7 % for 1,200~1,600m elevation band. The effect based on 19 subbasins of WAMIS (Water Resources Management Information System) water resources unit map, the changes of IDW and Thiessen were -8.0 % (Downstream of Han river)~ +19.7 % (Upstream of Namhan river) and -5.7 %~+15.9 % respectively. It showed the increase trend as the elevation increases. For the 11 years rainfall data analysis, the lapse rate effect of IDW and Thiessen showed increase of 9.7 %~15.5 % and 6.6 %~9.6 % respectively.

• Journal of the Korean Geotechnical Society
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• v.38 no.3
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• pp.35-42
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• 2022

The N-value from the Standard Penetration Test (SPT), which is one of the representative in-situ test, is an important index that provides basic geological information and the depth of the bearing layer for the design of geotechnical structures. In the aspect of time and cost-effectiveness, there is a need to carry out a representative sampling test. However, the various variability and uncertainty are existing in the soil layer, so it is difficult to grasp the characteristics of the entire field from the limited test results. Thus the spatial interpolation techniques such as Kriging and IDW (inverse distance weighted) have been used for predicting unknown point from existing data. Recently, in order to increase the accuracy of interpolation results, studies that combine the geotechnics and deep learning method have been conducted. In this study, based on the SPT results of about 22,000 holes of ground survey, a comparative study was conducted to predict the depth of the bearing layer using deep learning methods and IDW. The average error among the prediction results of the bearing layer of each analysis model was 3.01 m for IDW, 3.22 m and 2.46 m for fully connected network and PointNet, respectively. The standard deviation was 3.99 for IDW, 3.95 and 3.54 for fully connected network and PointNet. As a result, the point net deep learing algorithm showed improved results compared to IDW and other deep learning method.

• Spatial Information Research
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• v.14 no.1 s.36
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• pp.29-41
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• 2006

Geostatistical data are obtained only at selected sites even though they are potentially available at any location In a continuous surface. Therefore it is necessary to estimate the unknown values at unsampled locations based on observations. In this study we compared the accuracy of 5 spatial interpolation methods: local trend surface, IDW, RBF, ordinary kriging, universal kriging. These interpolation methods were applied to annual rainfall data. As the results of validation tests, universal kriging with gaussian variogram model showed the best accuracy in comparison with other interpolation methods. In the case of kriging, the predicted values were more accurate and within a more narrow range than other methods. In contrast with kriging, local trend surface analysis, IDW and RBF showed the wide range of predicted values and abrupt changes between neighbors.

• Journal of the Korean Geographical Society
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• v.44 no.2
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• pp.105-121
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• 2009

The accurate official map of air temperature does not exist for the Hawaiian Islands due to the limited number of weather stations on the rugged volcanic landscape. To alleviate the major problem of temperature mapping, satellite-measured land surface temperature (LST) data were used as an additional source of sample points. The Moderate Resolution Imaging Spectroradiometer (MODIS) system provides hypertemperal LST data, and LST pixel values that were frequently observed (${\ge}$14 days during a 32-day composite period) had a strong, consistent correlation with air temperature. Systematic grid points with a spacing of 5km, 10km, and 20km were generated, and LST-derived air temperature estimates were extracted for each of the grid points and used as input to inverse distance weighted (IDW) and cokriging methods. Combining temperature data and digital elevation model (DEM), cokriging significantly improved interpolation accuracy compared to IDW. Although a cokriging method is useful when a primary variable is cross-correlated with elevation, interpolation accuracy was sensitively influenced by the seasonal variations of weather conditions. Since the spatial variations of local air temperature are more variable in the wet season than in the dry season, prediction errors were larger during the wet season than the dry season.

• Journal of Environmental Impact Assessment
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• v.27 no.5
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• pp.447-474
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• 2018

The purpose of this study is to build a new dataset of spatially interpolated climate data of South Korea by performing various geo-statistical interpolation techniques for comparison with the LDAPS grid data of KMA. Among 595 observation data in 2017, 80 % of the total points and remaining 117 points were used for spatial mapping and quantification,respectively. IDW, cokriging, and kriging were performed via the ArcGIS10.3.1 software and Python3.6.4, and each result was then divided into three clusters and four watersheds for statistical verification. As a result, cokriging produced the most suitable grid climate data for instantaneous temperature. For 1-hr accumulated precipitation, IDW was most suitable for expressing local rainfall effects.

• Journal of Korea Water Resources Association
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• v.45 no.9
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• pp.943-957
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• 2012

In this study, a technique based on Fractal Theory with Erosion Model was developed to interpolate the river morphology data at the border area between river bed and river side where both surface and under water surveyings can not be committed easily. Three dimensional river morphology data along the Ara River was generated by the developed technique. The Ara River is an artificially constructed waterway for vessels between the Han River and West Sea of Korea. The result was compared with the survey data by RMSE of 0.384, while the IDW interpolation result has RMSE of 0.802. Consequently, the developed river morphology data interpolation technique using Erosion Model based Fractal Theory is conceived to be superior to the IDW which has been generally used in generating the river morphology data.