• Journal of the Korean association of regional geographers
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• v.12 no.5
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• pp.614-630
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• 2006

Improved estimates of populations at risk for quick and effective response to natural and man-made disasters require spatial disaggregation of zonal population data because of the spatial mismatch problem in areal units between census and impact zones. This paper implements a dasymetric surface model to facilitate spatial disaggregation of the population of a census block group into populations associated with each constituent pixel and evaluates the performance of the surface-based spatial disaggregation model visually and statistically. The surface-based spatial disaggregation model employed geographic information systems (GIS) to enable dasymetric interpolation to be guided by satellite-derived land use and land cover data as additional information about the geographic distributor of population. In the spatial disaggregation, percent cover based empirical sampling and areal weighting techniques were used to objectively determine dasymetric weights for each grid cell. The dasymetric population surface for the Atlanta metropolitan area was generated by the surface-based spatial disaggregation model. The accuracy of the dasymetric population surface was tested on census counts using the root mean square error (RMSE) and an adjusted RMSE. The errors related to each census track and block group were also visualized by percent error maps. Results indicate that the dasymetric population surface provides high-precision estimates of populations as well as the detailed spatial distribution of population within census block groups. The results also demonstrate that the population surface largely tends to overestimate or underestimate population for both the rural and forested and the urban core areas.

• 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 the Korea institute for structural maintenance and inspection
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• v.27 no.5
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• pp.30-39
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• 2023

Large dams, which are critical infrastructures for disaster prevention, are exposed to various risks such as aging, floods, and earthquakes. Better dam safety inspection and diagnosis using digital transformation technologies are needed. Traditional visual inspection methods by human inspectors have several limitations, including many inaccessible areas, danger of working at heights, and know-how based subjective inspections. In this study, drone photogrammetry was performed on two large dams to evaluate the applicability of digital data-based dam safety inspection and propose a data management methodology for continuous use. High-quality 3D digital models with GSD (ground sampling distance) within 2.5 cm/pixel were generated by flat double grid missions and manual photography methods, despite reservoir water surface and electromagnetic interferences, and severe altitude differences ranging from 42 m to 99.9 m of dam heights. Geometry profiles of the as-built conditions were easily extracted from the generated 3D mesh models, orthomosaic images, and digital surface models. The effectiveness of monitoring dam deformation by photogrammetry was confirmed. Cracks and deterioration of dam concrete structures, such as spillways and intake towers, were detected and visualized efficiently using the digital 3D models. This can be used for safe inspection of inaccessible areas and avoiding risky tasks at heights. Furthermore, a methodology for mapping the inspection result onto the 3D digital model and structuring a relational database for managing deterioration information history was proposed. As a result of measuring the labor and time required for safety inspection at the SYG Dam spillway, the drone photogrammetry method was found to have a 48% productivity improvement effect compared to the conventional manpower visual inspection method. The drone photogrammetry-based dam safety inspection is considered very effective in improving work productivity and data reliability.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.42 no.2
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• pp.108-118
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• 2014

On the optimization design problem using surrogate model, it requires considerable number of sampling points to construct a surrogate model which retains the accuracy. As an alternative to reduce construction cost of the surrogate model, Variable-Fidelity Modeling(VFM) technique, where correct high fidelity model based on the low fidelity surrogate model is introduced. In this study, hierarchical kriging model for variable-fidelity surrogate modeling is used and an optimization framework with multi-objective genetic algorithm(MOGA) is presented. To prove the feasibility of this framework, airfoil design optimization process is performed for the transonic region. The parameters of PARSEC are used to design variables and the optimization process is performed in case of varying number of grid and varying fidelity. The results showed that pareto front of all variable-fidelity models are similar with its single-level of fidelity model and calculation time is considerably reduced. Based on computational results, it is shown that VFM is a more efficient way and has an accuracy as high as that single-level of fidelity model optimization.

• Journal of the Mineralogical Society of Korea
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• v.31 no.3
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• pp.193-213
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• 2018

Soil geochemical exploration to check anomalies related to mineralization was carried out targeting around Quy Hop area within Nghe An province, Northern Vietnam. The interval of sampling are horizontal 250 m with 13 line and longitudinal 300 m with 25 line, resulting in 325 soil samples. Based on the result of soil geochemical exploration, the pitting survey was carried out targeting the grid point with high TREO content, resulting in 73 soil samples within 8 pits. The geology of the survey area are consisted of Ban Chieng biotite granite complex intruding Bu Khang formation comprising of schist, gneiss and limestone. In order to elucidate the source rock of monazite and xenotime confirmed from heavy sand, soil geochemical exploration was carried out. By ICP-MS result of soil samples, total REE oxide content of background amount is about 1.4 times of crustal abundance, depleting the light rare earth (about 0.2 times) and enriching the heavy rare earth (about 1.5 times). By ICP-MS result of pit soil samples, we identified TREO more than 1,000 ppm in 6 pits. It may be considered that REE ore bodies may develop in NE-SW direction, compared with the geochemical results of Quy Chau area.

• Economic and Environmental Geology
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• v.45 no.6
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• pp.599-622
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• 2012

The phase I soil geochemical exploration was carried out targeting around Chau Binh area far from about 14 km with southeastern direction from Quy Chau within Nghe An province. The interval of sampling are horizontal 300 m with 14 line and longitudinal 500 m with 15 line, resulting in 194 soil samples. Based on the result of the phase I soil geochemical exploration, the phase II detailed pitting survey was carried out targeting the grid point with high TREO content, resulting in 56 soil samples within 7 pits. The geology of survey area are consisted of Ban Chieng biotite granite complex and Dai Loc gneissic granite complex intruding Bu Khang formation comprising of schist, gneiss and limestone. Main mineralization in the study area have the characteristics of occurrence with tin, ruby and REE-bearing monazite(about 300 g/t) and xenotime(about 10 g/t) to be thought as occurring at the alteration zone of granite complex. In order to elucidate the source rock of monazite and xenotime confirmed from heavy sand, soil geochemical exploration was carried out. As a analysis result with ICP-MS on the soil samples from the phase I soil geochemical exploration, total REE oxide content of background amount to about 2 times of crustal abundance, enriching the heavy rare earth(about 2 times) and light rare earth(about 1.84 times). As a analysis result with ICP-MS on the soil samples from the phase II soil detailed pit survey, we identified outcrop considering as economic ore body at the grid point 4-7 pit with N40W attitude. As a synthetic consideration on the phase I soil geochemical exploration and phase II detailed pit survey, we tentatively designated areas considering as the extension of economic ore body with REE anomaly. In the near future, we have the plan to carry out the geophysical exploration and test drilling targeting the interval anticipated to the economic ore body.

• Economic and Environmental Geology
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• v.47 no.2
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• pp.147-168
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• 2014

The soil geochemical exploration was carried out targeting around Tri Le area far from about 30 km with northwestern direction from Que Phong within Nghe An province. The interval of sampling are horizontal 200 m interval with 23 line and longitudinal 300 m with 10 line, resulting in 228 soil samples. Based on the result of the soil geochemical exploration, the detailed pitting survey was carried out targeting the grid point with high TREO content, resulting in 75 soil samples within 7 pits. The geology of survey area are consisted of Ban Chieng biotite granite complex and granitic gneiss intruding Ban Khang formation comprising of quartz schist and marble. Main mineralization in the study area have the characteristics of occurrence with tin, ruby and REE-bearing monazite and xenotime to be thought as occurring at the alteration zone of granite complex. In order to elucidate the source rock of monazite and xenotime confirmed from heavy sand, soil geochemical exploration was carried out. As a analysis result with ICP-MS on the soil samples from the soil geochemical exploration, total REE oxide content of background amount to about 2 times of crustal abundance, enriching the heavy rare earth(about 2 times) and light rare earth(about 1.5 times). As a analysis result with ICP-MS on the soil samples from the soil detailed pit survey, we only identified outcrop considering as economic weathered granite body at the grid point 1-10 pit among 7 pits. As a synthetic consideration on the soil geochemical exploration and detailed pit survey, we tentatively designated Tri Le area as no promising target for REE. In 2014, we have the plan to carry out the soil geochemical exploration targeting the extended economic REE ore body in Quy Chau as project area from 2011 to 2012.