• 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.

• The Korean Journal of Applied Statistics
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• v.29 no.3
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• pp.549-557
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• 2016

Consecutive missing values are likely to occur in long climate data due to system error or defective equipment. Furthermore, it is difficult to impute missing values. However, these complicated problems can be overcame by imputing missing values with reference time series. Reference time series must be composed of similar time series to time series that include missing values. We performed a simulation to compare three missing imputation methods (the adjusted normal ratio method, the regression method and the IDW method) to complete the missing values of time series. A comparison of the three missing imputation methods for the daily mean temperatures at 14 climatological stations indicated that the IDW method was better thanx others at south seaside stations. We also found the regression method was better than others at most stations (except south seaside stations).

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.4
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• pp.42-54
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• 2012

The main purpose of this study is to suggest the spatial statistical approach suitable for the delimitation of Busan CBD. For this purpose, Getis-Ord $G_i^*$ and both of IDW (Inverse Distance Weight) and FDB(Fixed Distance Band) were applied to delimit the CBD boundary. And then, the results of the CBD boundary were compared and verified with the methodologies of the previous studies. The result of IDW accorded with the previous study relating to the delimitation of the boundary of CBD, and the result of FDB was reflecting the characteristics of the mixed-use residential of a transition zone. As a result of the land use quotient, the mixed land use of residential and commercial was highly specialized in the boundary of FDB. These results will be able to support the understanding of urban spatial structure and the effective CBD management.

• 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 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.

• 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 the Korean association of regional geographers
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• v.16 no.1
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• pp.48-58
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• 2010

Currently, understanding the characters of Rural Industrial Parks' regional distribution is insufficient. Therefore, regional characters of the Rural Industrial Parks all over the country were studied through indications such as sales, worker-sales increase rate, worker increase rates. Portfolio analysis and IDW by using ArcView 3.2 were used as a method of analysis. As a result, most of the Rural Industrial Parks' size and size increase rate showed low figures. Respective regional analysis shows that there is an increase in the scale of the Kyungnam area using portfolio analysis, whereas the scale of Chonbuk is high using IDW. As a result, it shows that there is difference on the scale between Rural Industrial Parks when individual or Associated with peripheral. Therefore, not only do the Rural Industrial Parks need stimulation individually, but adjacent parks need to be supported and managed.

• Atmosphere
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• v.26 no.4
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• pp.697-709
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• 2016

This study estimates and evaluates the extreme value of 30 m-resolution daily maximum and minimum temperatures over South Korea, using inverse distance weighting (IDW), parameter-elevation regression on independent slopes model (PRISM) and generalized extreme value (GEV) method. The three experiments are designed and performed to find the optimal estimation strategy to obtain extreme value. First experiment (EXP1) applies GEV firstly to automated surface observing system (ASOS) to estimate extreme value and then applies IDW to produce high-resolution extreme values. Second experiment (EXP2) is same as EXP1, but using PRISM to make the high-resolution extreme value instead of IDW. Third experiment (EXP3) firstly applies PRISM to ASOS to produce the high-resolution temperature field, and then applies GEV method to make high resolution extreme value data. By comparing these 3 experiments with extreme values obtained from observation data, we find that EXP3 shows the best performance to estimate extreme values of maximum and minimum temperatures, followed by EXP1 and EXP2. It is revealed that EXP1 and EXP2 have a limitation to estimate the extreme value at each grid point correctly because the extreme values of these experiments with 30 m-resolution are calculated from only 60 extreme values obtained from ASOS. On the other hand, the extreme value of EXP3 is similar to observation compared to others, since EXP3 produces 30m-resolution daily temperature through PRISM, and then applies GEV to that result at each grid point. This result indicates that the quality of statistically produced high-resolution extreme values which are estimated from observation data is different depending on the combination and procedure order of statistical methods.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.879-879
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• 2012

강우현상의 공간적 변동성에 대한 해석은 수자원 계획 및 관리를 위해 중요한 관심사가 되고 있다. 일반적으로 우리가 얻을 수 있는 강우자료는 한 지점에 설치되어 있는 우량계에 의한 관측된 지점강우량자료이다. 기존의 집중형 수문모형이 유출과정의 공간적인 분포 및 변화를 유역단위로 평균화해서 취급하는 개념기반의 모형임에 반해서 분포형 수문모형은 유역을 수문학적으로 균일한 매개변수를 갖는 소유역 또는 격자망으로 구분하여 적용하는 것으로, 도시화 등 토지이용의 변화나 기타 유역내의 물리적인 특성의 변화가 수문과정에 미치는 영향을 잘 모의할 수 있다. 따라서 본 연구에서는 Vflo$^{TM}$ 분포형 강우-유출 모형과 IDW, Ordinary Kriging, Thiessen 등의 강우 분포 기법을 이용하여 낙동강 제 1지류인 금호강의 동촌 수위관측소 유역($1,544km^2$)을 출구로 하여 강우-유출모의를 하였다. 이를 위하여 강우-유출에 영향을 주는 매개변수를 선정하고 동촌 수위관측소의 실측 유량자료를 바탕으로 하여 IDW, Kriging, Thiessen 등의 면적강우량 산정방법별로 모형의 보정(2007, 2009) 및 검증(2010)을 실시하였다. 모의 된 유출량과 실측유량의 상관성은 결정계수 $R^2$에서 IDW 과 Kriging의 경우 0.95 ~ 0.99의 상관성을 나타냈으며 Thiessen 의 경우 0.94 ~ 0.99의 값을 나타냈다. Nash-Sutcliffe 모형효율은 IDW의 경우 0.95 ~ 0.98, Kriging의 경우 0.94 ~ 0.99를 나타냈으며 Thiessen의 경우는 0.90 ~ 0.98의 모형효율을 나타내었다. 이때 포화투수계수와 조도계수가 전체 유량과 첨두시간에 영향을 주었다. 호우사상을 선정하여 검보정을 실시 한 결과, 유역의 유출 모의를 수행하였을 때 선행강우량에 따라서 토양의 침투능에 영향을 많이 주고 있기 때문에, 선행 토양함수조건(Antecedent Moisture Condition: AMC)으로 분류한 뒤에 AMC 조건에 따라서 유출-모의를 수행하는 것이 타당하다고 판단된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1520-1524
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• 2010

지상에 설치된 우량관측소를 통해서 자료가 수집되는 강우자료는 공간적으로 분포하고 있는 공간자료(spatial data)이며, 지점자료(point data)이다. 공간자료(spatial data)는 공간적으로 분포되지 않는 일반 데이터와는 다른 속성을 가지고 있으며 공간적인 위치가 데이터 발생의 중요한 변수로 적용될 수 있고, 인접 데이터와의 상관관계가 고려되어야 한다. 본 연구는 공간분석기법을 이용하여 보다 효과적인 강우분석을 하기 위한 것으로서, 우리나라 총 679개 우량관측소의 2008년 강우자료를 바탕으로 티센(Thiessen) 기법, IDW(Inverse Distance Weighted), 스플라인(Spline) 등과 공간통계학적 방법인 크리깅(Kriging)을 이용하여 주요 유역별 면적 강우량 산정 및 미계측 지역의 강우량 추정을 모의하였다. 본 연구결과 유역별 면적강우량 추정시 티센 및 경향면 분석법, Natural Neighbor 방법은 일부 과다 추정되는 것으로 나타났고, IDW, RBF, 크리깅의 방법은 큰 차이를 보이지 않았으나, 미계측 지역의 강우량 추정에는 일반크리깅의 정확도가 비교적 높은 것으로 나타났다.