• Economic and Environmental Geology
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• v.40 no.3 s.184
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• pp.295-306
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• 2007

In this study, we predicted areas vulnerable to ground subsidence near abandoned underground coal mine at Sam-cheok City in Korea using a probability (frequency ratio) model with Geographic Information System (GIS). To extract the factors related to ground subsidence, a spatial database was constructed from a topographical map, geo-logical map, mining tunnel map, land characteristic map, and borehole data on the study area including subsidence sites surveyed in 2000. Eight major factors were extracted from the spatial analysis and the probability analysis of the surveyed ground subsidence sites. We have calculated the decision coefficient ($R^2$) to find out the relationship between eight factors and the occurrence of ground subsidence. The frequency ratio model was applied to deter-mine each factor's relative rating, then the ratings were overlaid for ground subsidence hazard mapping. The ground subsidence hazard map was then verified and compared with the surveyed ground subsidence sites. The results of verification showed high accuracy of 96.05% between the predicted hazard map and the actual ground subsidence sites. Therefore, the quantitative analysis of ground subsidence near abandoned underground coal mine would be possible with a frequency ratio model and a GIS.

• Journal of Astronomy and Space Sciences
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• v.20 no.1
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• pp.21-28
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• 2003

In case that we independently obtain orbital informations about the low earth satellites of foreign countries using radar systems, we develop the orbit determination algorithm for this purpose using a SGP4 model with an analytical orbit model and the extended Kalman filter with a real-time processing method. When the state vector is Keplerian orbital elements, singularity problems happen to compute partial derivative with respect to inclination and eccentricity orbit elements. To cope with this problem, we set state vector osculating to mean equinox and true equator cartesian elements with coordinate transformation. The state transition matrix and the covariance matrix are numerically computed using a SGP4 model. Observational measurements are the type of azimuth, elevation and range, filter process to each measurement in a lump. After analyzing performance of the developed orbit determination algorithm using TOPEX/POSEIDON POE(precision 0.bit Ephemeris), its position error has about 1 km. To be similar to performance of NORAD system that has up to 3km position accuracy during 7 days need to radar system performance that have accuracy within 0.1 degree for azimuth and elevation and 50m for range.

• Journal of Wetlands Research
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• v.20 no.4
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• pp.338-344
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• 2018

In this study, the surface air temperature (SAT) and the dew-point temperature (DPT) are applied as the covariance of the location parameter among three parameters of GEV distribution to reflect the non-stationarity of extreme rainfall due to climate change. Busan station is selected as the study site and the monthly maximum daily rainfall depth from May to October is used for analysis. Various models are constructed to select the most appropriate co-variate(SAT and DPT) function for location parameter of GEV distribution, and the model with the smallest AIC(Akaike Information Criterion) is selected as the optimal model. As a result, it is found that the non-stationary GEV distribution with co-variate of exp(DPT) is the best. The selected model is used to analyze the effect of climate change scenarios on extreme rainfall quantile. It is confirmed that the design rainfall depth is highly likely to increase as the future DPT increases.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.5
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• pp.466-473
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• 2011

The purpose of satellite thermal control design is to maintain all the elements of a spacecraft system within their temperature limits for all mission phases. The thermal analysis model for Low Earth Orbit satellite payload level simulation is established by considering thermal vacuum test environment condition, thermal vacuum chamber configuration, and satellite's payload inner thermal environment. The established thermal analysis model is used to determine thermal vacuum test conditions and test case requirements.

• Journal of the Korean earth science society
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• v.28 no.5
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• pp.613-619
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• 2007

Mesoscale low is often observed over the downstream region of the East Sea (or, northwest coast off the Japan Islands) during East-Asia winter monsoon. The low system causes a heavy snowfall at the region. A series of numerical experiments were conducted with the aid of a regional model (MM5 ver. 3.5) to examine the formation mechanism of the mesoscale low. The following results were obtained: 1) A well-developed mesoscale low was simulated by the regional model under real topography, NCEP reanalysis, and OISST; 2) The mesoscale low was simulated under a zonally averaged SST without topography. This implies that the meridional gradient of SST is the main factor in the formation of a mesoscale low; 3) A thermal contrast ($>10^{\circ}C$) of land-sea and topography-induced disturbance served as the second important factor for the formation; 4) Paektu Mountain caused the surface wind to decelerate downstream, which created a more favorable environment for thermodynamic modification than that was found in a flat topography; and 5) The types of cumulus parameterizations did not affect the development of the mesoscale low.

• Journal of the Korean Institute of Rural Architecture
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• v.2 no.2
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• pp.115-126
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• 2000

Retaining walls are used to prevent excessive movement of retained soils. Typical retaining walls include gravity, reinforced concrete, reinforced earth and tie-back. However, from a practical viewpoint there are still drawbacks among these often constructed retaining walls. New types of retaining walls constructed with precast concrete blocks are proposed. This type of retaining wall is incorporates each blocks interconnected with adjacent block by connecting unit to build up a flexible retaining-wall system. This paper focus to behavior characteristics includes deformation and distribution of lateral earth pressure by loading tests and FEM analysis. For model tests, a 1/10 scale reduce models are manufactured include unevenness part, drainage hole and connecting unit and steel wire used to connect each blocks with adjacent block. To simulate the real retaining walls closely, uneven parts are interconnected each other and the construction type of blocks and wall front inclination are varied to investigate the relative displacement of individual block and the location of maximum deformation of wall as increasing surcharging. Additionally, PENTAGON3D, which solve the geotechnical and other problem, used for verifying and comparing with model tests.

• Journal of Environmental Science International
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• v.27 no.11
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• pp.1141-1154
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• 2018

Numerical experiments were carried out to investigate the effect of data assimilation of observational data on weather and PM (particulate matter) prediction. Observational data applied to numerical experiment are aircraft observation, satellite observation, upper level observation, and AWS (automatic weather system) data. In the case of grid nudging, the prediction performance of the meteorological field is largely improved compared with the case without data assimilations because the overall pressure distribution can be changed. So grid nudging effect can be significant when synoptic weather pattern strongly affects Korean Peninsula. Predictability of meteorological factors can be expected to improve through a number of observational data assimilation, but data assimilation by single data often occurred to be less predictive than without data assimilation. Variation of air pressure due to observation nudging with high prediction efficiency can improve prediction accuracy of whole model domain. However, in areas with complex terrain such as the eastern part of the Korean peninsula, the improvement due to grid nudging were only limited. In such cases, it would be more effective to aggregate assimilated data.

• Korean Journal of Remote Sensing
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• v.33 no.2
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• pp.135-147
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• 2017

We, for the first time, estimated daily and monthly surface nitrogen dioxide ($NO_2$) volume mixing ratio (VMR) using three regression models with $NO_2$ tropospheric vertical column density (OMIT-rop $NO_2$ VCD) data obtained from Ozone Monitoring Instrument (OMI) in Seoul in South Korea at OMI overpass time (13:45 local time). First linear regression model (M1) is a linear regression equation between OMI-Trop $NO_2$ VCD and in situ $NO_2$ VMR, whereas second linear regression model (M2) incorporates boundary layer height (BLH), temperature, and pressure obtained from Atmospheric Infrared Sounder (AIRS) and OMI-Trop $NO_2$ VCD. Last models (M3M & M3D) are a multiple linear regression equations which include OMI-Trop $NO_2$ VCD, BLH and various meteorological data. In this study, we determined three types of regression models for the training period between 2009 and 2011, and the performance of those regression models was evaluated via comparison with the surface $NO_2$ VMR data obtained from in situ measurements (in situ $NO_2$ VMR) in 2012. The monthly mean surface $NO_2$ VMRs estimated by M3M showed good agreements with those of in situ measurements(avg. R = 0.77). In terms of the daily (13:45LT) $NO_2$ estimation, the highest correlations were found between the daily surface $NO_2$ VMRs estimated by M3D and in-situ $NO_2$ VMRs (avg. R = 0.55). The estimated surface $NO_2$ VMRs by three modelstend to be underestimated. We also discussed the performance of these empirical modelsfor surface $NO_2$ VMR estimation with respect to otherstatistical data such asroot mean square error (RMSE), mean bias, mean absolute error (MAE), and percent difference. This present study shows a possibility of estimating surface $NO_2$ VMR using the satellite measurement.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.

• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.252-254
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• 2003

In this study, it was purposed to develop the new method for the prediction of pollutant concentration in road tunnels. The new method was the use of artificial neural network with the back-propagation algorithm which can model the non-linear system of tunnel environment. This network system was separated into two parts as the visibility and the CO concentration. For this study, data was collected from two highway road tunnels on Yeongdong Expressway. The tunnels have two lanes with one-way direction and adopt the longitudinal ventilation system. The actually measured data from the tunnels was used to develop the neural network system for the prediction of pollutant concentration. The output results from the newly developed neural network system were analysed and compared with the calculated values by PIARC method. Results showed that the prediction accuracy by the neural network system was approximately five times better than the one by PIARC method. ill addition, the system predicted much more accurately at the situation where the drivers have to be stayed for a while in tunnels caused by the low velocity of vehicles.