• Economic and Environmental Geology
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• v.56 no.3
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• pp.331-341
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• 2023

When creating a soil contamination map using geostatistical techniques, there are various sources that can affect prediction errors. In this study, a grid-based soil contamination map was created from the sampling data of heavy metal concentrations in soil in abandoned mine areas using Ordinary Kriging. Five factors that were judged to affect the prediction error of the soil contamination map were selected, and the variation of the root mean squared error (RMSE) between the predicted value and the actual value was analyzed based on the Leave-one-out technique. Then, using a machine learning algorithm, derived the top three factors affecting the RMSE. As a result, it was analyzed that Variogram Model, Minimum Neighbors, and Anisotropy factors have the largest impact on RMSE in the Standard interpolation. For the variogram models, the Spherical model showed the lowest RMSE, while the Minimum Neighbors had the lowest value at 3 and then increased as the value increased. In the case of Anisotropy, it was found to be more appropriate not to consider anisotropy. In this study, through the combined use of geostatistics and machine learning, it was possible to create a highly reliable soil contamination map at the local scale, and to identify which factors have a significant impact when interpolating a small amount of soil heavy metal data.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.4
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• pp.91-97
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• 2014

In this research, we would try to evaluate the applicability in cadastral resurvey surveying by analysing the accuracy of building boundary surveying using the reflective sheet total station surveying. When we analyse it, we refer the reflective sheet which can supplement not only the difficulties of total station surveying and GPS surveying caused by the diversity of the building structure but also the errors of non prism total station caused by material of the object. Each reflected angles $90^{\circ}$ and $60^{\circ}$, $30^{\circ}$ of RMSE results were analyzed by RMSE between 1.2mm~2.8mm and 2.2mm~4.0mm, 2.5mm~4.4mm for each distance. The result of X RMSE was analyzed to be 0.0043m in a boundary surveying for existing building between prism surveying and reflective sheet surveying, and also Y RMSE was 0.038m. The source of error is estimated that the body of the prism can not be exactly attached to the edge of a building. Therefore, it will be very helpful to use a reflective sheet surveying with a prism in both the limit of collimation and error reductions as a building boundary measurement in cadastral resurvey surveying.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.2
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• pp.133-139
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• 2011

Estimation of biomass carbon stock is an important research for estimation of public benefit of forest. Previous studies about biomass carbon stock estimation have limitations, which come from the used deterministic models. The most serious problem of deterministic models is that deterministic models do not provide any explanation about the relevant effects of errors. In this study, the effects of location errors were analyzed in order to estimation of biomass carbon stock of Danyang area using Monte Carlo simulation method. More specifically, the k-Nearest Neighbor(kNN) algorithm was used for basic estimation. In this procedure, random and systematic errors were added on the location of Sample plot, and effects on estimation error were analyzed by checking the changes of RMSE. As a result of random error simulation, mean RMSE of estimation was increased from 24.8 tonC/ha to 26 tonC/ha when 0.5~1 pixel location errors were added. However, mean RMSE was converged after the location errors were added 0.8 pixel, because of characteristic of study site. In case of the systematic error simulation, any significant trends of RMSE were not detected in the test data.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.4
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• pp.43-48
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• 2015

We studied on pseudo-range correction(PRC) modeling in order to improve differential GNSS(DGNSS) accuracy. The PRC is the range correction information that provides improved location accuracy using DGNSS technique. The digital correction signal is typically broadcast over ground-based transmitters. Sometimes the degradation of the positioning accuracy caused by the loss of PRC signals, radio interference, etc. To prevent the degradation, in this paper, we have designed a PRC model through polynomial curve fitting and evaluated this model. We compared two quantities, estimations of PRC using model parameters and observations from the reference station. In the case of GPS, the average is 0.1m and RMSE is 1.3m. Most of GPS satellites have a bias error of less than ${\pm}1.0m$ and a RMSE within 3.0m. In the case of GLONASS, the average and the RMSE are 0.2m and 2.6m, respectively. Most of satellites have less than ${\pm}2.0m$ for a bias error and less than 3.0m for RMSE. These results show that the estimated value calculated by the model can be used effectively to maintain the accuracy of the user's location. However;it is needed for further work relating to the big difference between the two values at low elevation.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.4
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• pp.139-149
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• 2008

A load effect by vehicles running on a road and an increase of traffic is distinguished as a serious issue in the level of bridges' maintenance and management since it causes a quick damage of bridges. The expansion joint is the most important since it makes vehicles' traveling amicable and stress or additional load harmful to molding patterns minimized. However, it is very difficult to measure its expansion length since vehicles continue to pass on the expansion joint. Therefore, the study could see that it was possible to carry out a qualitative and quantitative maintenance and management if its expansion length is extracted with images. The study could acquire three dimensional coordinates of expansion joints with images. As the results of calculating RMSE of check point residual at 32 points in A area and at 28 points in B area, both A and B areas had very good results of RMSEsms 0.829mm~1.680mm. As the results of analyzing expansion length and immediate value extracted by images, the study analyzed that RMSE of A area was 0.64mm and RMSE of B area was 0.28. The average residual of A area was 0.60% and the average rresidual of B area was 0.27%. Therefore, it is judged that it is more scientific and efficient than the past to measure expansion length with images at the time of repairing and managing bridges in the future.

• Korean Journal of Remote Sensing
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• v.30 no.5
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• pp.571-586
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• 2014

Moderate Resolution Imaging Spectroradiometer (MODIS) provides air temperature (Tair) and dew point temperature (Tdew) profiles at a spatial resolution of 5 km. New Collection 6 (C006) MODIS07_L2 atmospheric profile product has been produced since 2012. The Collection 6 algorithm has several modifications from the previous Collection 5 (C005) algorithm. This study evaluated reliabilities of two alternative datasets of surface-level Tair and Tdew derived from C005 and C006 Aqua MODIS07_L2 (MYD07_L2) products using ground measured temperatures from 77 National Weather Stations (NWS). Saturated and actual vapor pressures were calculated using MYD07_L2 Tair and Tdew. The C006 Tair showed lower mean error (ME, -0.76 K) and root mean square error (RMSE, 3.34 K) than the C005 Tair (ME = -1.89 K, RMSE = 4.06 K). In contrasts, ME and RMSE of C006 Tdew were higher than those (ME = -0.39 K, RMSE = 5.65 K) of C005 product. Application of ambient lapse rate for Tair showed appreciable improvements of estimation accuracy for both of C005 and C006, though this modification slightly increased errors in C006 Tdew. The C006 products provided better estimation of vapor pressure datasets than the C005-derived vapor pressure. Our results indicate that, except for Tdew, C006 MYD07_L2 product showed better reliability for the region of South Korea than the C005 products.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.497-505
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• 2018

In a situation where the amount of bulky waste needs to be quantified, a three-dimensional model of the wastes can be constructed using drones. This study constructed a drone-based 3D model with a range of flight parameters and a GCPs survey, analyzed the relationship between the accuracy and time required, and derived a suitable drone application technique to estimate the amount of waste in a short time. Images of waste were photographed using the drone and auto-matching was performed to produce a model using 3D coordinates. The accuracy of the 3D model was evaluated by RMSE calculations. An analysis of the time required and the characteristics of the top 15 models with high accuracy showed that the time required for Model 1, which had the highest accuracy with an RMSE of 0.08, was 954.87 min. The RMSE of the 10th 3D model, which required the shortest time (98.27 min), was 0.15, which is not significantly different from that of the model with the highest accuracy. The most efficient flight parameters were a high overlapping ratio at a flight altitude of 150 m (60-70% overlap and 30-40% sidelap) and the minimum number of GCPs required for image matching was 10.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.120-120
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• 2021

2020년 8월 7일부터 8월 8일까지 호우는 용담댐, 섬진강댐, 합천댐 하류 유역의 막대한 침수피해를 일으켰다. 이들 다목적 댐 유입량의 신뢰도 높은 모의는 홍수기 댐 운영 및 하류하천의 홍수 해석에 필수다. 여기서는 일 유출 모의 기반으로 개발된 ONE 모형을 10분 단위, 1시간 단위로 적용한 결과를 제시하고자 한다. 보통 홍수모의는 사상별로 실시하지만, 여기서는 1월1일부터 12월 31일까지 연속으로 모의한 결과에서 해당 홍수사상 결과를 제시하였다. 3개 다목적 댐의 홍수사상은 8월6일부터 8월 10일까지 5일간으로 설정하였다. 유역면적은 용담댐, 섬진강댐, 합천댐, 각각 930km², 763km², 925km², 총강우량은 각각 490.7mm, 451.9mm, 452.4mm, 첨두유입량은 10분 단위는 각각 4,872.7m³/s, 3,533.7.0m³/s, 2,776.0m³/s, 1시간 단위는 각각 4,394.9m³/s, 3,401.8m³/s, 2,745.6m³/s, 총유입량은 각각 3억8,836만m³, 3억1,324만m³, 3억2,816만m³였다. 첨두유입량 상대오차가 0일 때의 매개변수로 모의한 결과를 제시하며, 총유입량 상대오차(Vq), R², RMSE, NSE 등으로 평가하였다. 용담댐 결과는 10분 단위 경우 최대면적강우량 7.3mm, 첨두유입량 4,872.4m³/s, 총유입량 3억 8,138만m³, Vq 1.9%, R² 0.968, RMSE 207.347, NSE 0.978였고, 1시간의 경우 최대면적강우량 29.6mm, 첨두유입량 4394.9m³/s, 총유입량 4억157만m³, Vq -8.4%, R² 0.970, RMSE 186.962, NSE 0.982였다. 섬진강댐 결과는 10분 단위 경우 최대면적강우량 9.2mm, 첨두유입량 3,533.3m³/s, 총유입량 2억7,223만m³, Vq 18.4%, R² 0.885, RMSE 808.296, NSE 0.925였고, 1시간의 경우 최대 면적강우량 37.9mm, 첨두유입량 3401.6m³/s, 총유입량 2억7,029만m³, Vq 13.7%, R² 0.907, RMSE 285.544, NSE 0.936였다. 합천댐 결과는 10분 단위 경우 최대면적강우량 5.5mm, 첨두유입량 2,776.2m³/s, 총유입량 3억3,667만m³, Vq -2.7%, R² 0.941, RMSE 191.896, NSE 0.965였고, 1시간의 경우 최대면적강우량 17.0mm, 첨두유입량 2,746.7m³/s, 총유입량 3억1,333만m³, Vq 4.5%, R² 0.965, RMSE 140.739, NSE 0.981였다. 이상 ONE 모형으로 10분, 1시간 단위의 댐 홍수 유입량 모의결과는 높은 신뢰도를 나타냈다.

• Journal of Environmental Science International
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• v.18 no.9
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• pp.1045-1055
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• 2009

The discharge within the basin in Jeju Island was calculated by using SWAT model, which a Semi-distributed rainfall-runoff model to the important rivers. The basin of Chunmi river of the eastern region of Jeju Island, as the result of correcting as utilizing direct runoff data of 2 surveys, appeared the similar value to the existing basin average runoff rate as 22% of average direct runoff rate for the applied period. The basin of Oaedo river of the northern region showed $R^2$ of 0.93, RMSE of 14.92 and ME of 0.70 as the result of correcting as utilizing runoff data in the occurrence of 7 rainfalls. The basin of Ongpo river of the western region showed $R^2$ of 0.86, RMSE of 0.62 and ME of 0.56 as the result of correcting as utilizing runoff data except for the period of flood in $2002{\sim}2003$. Yeonoae river of the southern region showed $R^2$ of 0.85, RMSE of 0.99 and ME of 0.83 as the result of correcting as utilizing runoff data of 2003. As the result of calculating runoff for the long term about 4 basins of Jeju Island from the above results, SWAT model wholly appears the excellent results about the long-term daily runoff simulation.

• Journal of Biosystems Engineering
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• v.37 no.5
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• pp.319-326
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• 2012

Purpose: Cheese texture is an important sensory attribute mainly considered for consumers' acceptance. The feasibility of nondestructive measurements of cheese texture was explored using non-contact ultrasonic sensors. Methods: A novel non-contact air instability compensation ultrasonic technique was used for five varieties of hard cheeses to measure ultrasonic parameters, such as velocity and attenuation coefficient. Five texture properties, such as fracturability, hardness, springiness, cohesiveness, and chewiness were assessed by a texture profile analysis (TPA) and correlated with the ultrasonic parameters. Results: Texture properties of five varieties of hard cheese were estimated using ultrasonic parameters with regression analysis models. The most effective model predicted the fracturability, hardness, springiness, and chewiness, with the determination coefficients of 0.946 (RMSE = 21.82 N), 0.944 (RMSE = 63.46 N), 0.797 (RMSE = 0.06 ratio), and 0.833 (RMSE = 17.49 N), respectively. Conclusions: This study demonstrated that the non-contact air instability compensation ultrasonic sensing technique can be an effective tool for rapid and non-destructive determination of cheese texture.