• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.43-45
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• 2021

The short-term quantitative precipitation prediction (QPF) system is important socially and economically to prevent damage from severe weather. Recently, many studies for short-term QPF model applying the Deep Neural Network (DNN) has been conducted. These studies require the sophisticated pre-processing because the mistreatment of various and vast meteorological data sets leads to lower performance of QPF. Especially, for more accurate prediction of the non-linear trends in precipitation, the dataset needs to be carefully handled based on the physical and dynamical understands the data. Thereby, this paper proposes the following approaches: i) refining and combining major factors (weather radar, terrain, air temperature, and so on) related to precipitation development in order to construct training data for pattern analysis of precipitation; ii) producing predicted precipitation fields based on Convolutional with ConvLSTM. The proposed algorithm was evaluated by rainfall events in 2020. It is outperformed in the magnitude and strength of precipitation, and clearly predicted non-linear pattern of precipitation. The algorithm can be useful as a forecasting tool for preventing severe weather.

• Geophysics and Geophysical Exploration
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• v.27 no.1
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• pp.37-50
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• 2024

The magnetotelluric (MT) survey can be affected by external environmental factors. In particular, when acquiring MT data in islands, it is essential to consider the combined effect of topography and sea to understand the results and make accurate interpretations. To analyze the MT response (apparent resistivity, phase) with consideration of the effect of topography and sea, a small cone-shaped island model surrounded by deep sea was created. Two-dimensional (2-D) and three-dimensional (3-D) forward modeling were performed on the terrain model considering topography and the island model considering both topography and sea. The 2-D MT response did not reflect the topographic and sea effect of the direction orthogonal to the 2-D profile. The 3-D MT response included topographic and sea effects in all directions. The XY and YX components of the apparent resistivity were separated on undulating topography, such as a hill. A conductor at 1 km below sea level could be distinguished from topographic and sea effects in the MT response, and low resistivity anomaly was attenuated at greater depths. This study will facilitate understanding of field data measured on small islands.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.35 no.1
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• pp.68-82
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• 2017

Strengthening surveillance, one of the most important factors in the crime prevention environment of cultural heritages, has difficulty in evaluating and diagnosing the site. For this reasons, surveillance enhancement has been assessed by modelling the shape of cultural heritage, topography, and trees digitally. The purpose of this study is to develop the evaluation method of crime prevention environment for cultural heritage by using the 3D visual exposure index (3DVE) which can quantitatively evaluate the surveillance enhancement in three dimensions. For the study, the evaluation factors were divided into natural, organizational, mechanical, and integrated surveillance. To conduct the analysis, the buildings, terrain, walls, and trees of the study site were modeled in three dimensions and the analysis program was developed by using the Unity 3D. Considering the working area of the person, it is possible to analyze the surveillance point by dividing it into the head and the waist position. In order to verify the feasibility of the 3DVE as the analysis program, we assessed the crime prevention environment by digitally modeling the Donam Seowon(Historic Site No. 383) located in Nonsan. As a result of the study, it was possible to figure out the problems of patrol circulation, the blind spot, and the weak point in natural, mechanical, and organizational surveillance of Donam Seowon. The results of the 3DVE were displayed in 3D drawings, so that the position and object could be identified clearly. Surveillance during the daytime is higher in the order of natural, mechanical, and organizational surveillance, while surveillance during the night is higher in the order of organizational, mechanical, and natural surveillance. The more the position of the work area becomes low, the more it is easy to be shielded, so it is necessary to evaluate the waist position. It is possible to find out and display the blind spot by calculating the surveillance range according to the specification, installation location and height of CCTV. Organizational surveillance, which has been found to be complementary to mechanical surveillance, needs to be analyzed at the vulnerable time when crime might happen. Furthermore, it is note that the analysis of integrated surveillance can be effective in examining security light, CCTV, patrol circulation, and other factors. This study was able to diagnose the crime prevention environment by simulating the actual situation. Based on this study, consecutive researches should be conducted to evaluate and compare alternatives to design the crime prevention environment.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.22 no.4
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• pp.151-171
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• 2017

Most of oceanic current maps in the secondary school science and earth science textbooks have been made on the base of extensive in-situ measurements conducted by Japanese oceanographers during 1930s. According to up-to-date scientific knowledge on the currents in the Yellow Sea and the East China Sea (YES), such maps have significant errors and are likely to cause misconceptions to students, thus new schematic map of ocean currents is needed. The currents in the YES change seasonally due to relatively shallow water depths, complex terrain, winds, and tides. These factors make it difficult to construct a unified ocean current map of the YES. Sixteen major items, such as the flow of the Kuroshio Current into the East China Sea and its northward path, the origin of the Tsushima Warm Current and its path into the Korea Strait, the path of Taiwan Warm Current, the Jeju Warm Current, the runoff pattern of the Yangtze River flow, the routes of the northward Yellow Sea Warm Current, the Chinese Coastal Current, and the West Korea Coastal Current off the west coast of the Korean Peninsula, were selected to produce the schematic current map. Review of previous scientific researches, in-depth discussions through academic conferences, expert discussions, and consultations for three years since 2014 enabled us to produce the final ocean current maps for the YES after many revisions. Considering the complexity of the ocean currents, we made seven ocean current maps: two representative current patterns in summer and winter, seasonal current maps for upper layer and lower layer in summer and winter, and one representative surface current map. It is expected that the representative maps of the YES, connected to the current maps of the East Sea and the Northwest Pacific Ocean, would be widely utilized for diverse purposes in the secondary-school textbooks as well as high-level educational purposes and even for scientific scholarly experts.

• Journal of Korean Society of Transportation
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• v.19 no.4
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• pp.35-47
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• 2001

Traffic represents one of the largest sources of primary air pollutants in urban area. As a consequence. numerous abatement strategies are being pursued to decrease the ambient concentration of pollutants. A characteristic of most of the these strategies is a requirement for accurate data on both the quantity and spatial distribution of emissions to air in the form of an atmospheric emission inventory database. In the case of traffic pollution, such an inventory must be compiled using activity statistics and emission factors for vehicle types. The majority of inventories are compiled using passive data from either surveys or transportation models and by their very nature tend to be out-of-date by the time they are compiled. The study of current trends are towards integrating urban traffic control systems and assessments of the environmental effects of motor vehicles. In this study, a methodology of motor vehicle emission calculation by using real-time traffic data was studied. A methodology for estimating emissions of CO at a test area in Seoul. Traffic data, which are required on a street-by-street basis, is obtained from induction loops of traffic control system. It was calculated speed-related mass of CO emission from traffic tail pipe of data from traffic system, and parameters are considered, volume, composition, average velocity, link length. And, the result was compared with that of a method of emission calculation by VKT(Vehicle Kilometer Travelled) of vehicles of category.

• Journal of Bio-Environment Control
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• v.24 no.3
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• pp.226-234
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• 2015

To provide the data necessary to determine exposure coefficients used for calculating the snow load acting on a greenhouse, we compared the exposure coefficients in the greenhouse structure design standards for various countries. We determined the exposure coefficient for each region and tried to improve on the method used to decide it. Our results are as follows: After comparing the exposure coefficients in the standards of various countries, we could determine that the main factors affecting the exposure coefficient were terrain roughness, wind speed, and whether a windbreak was present. On comparing national standards, the exposure coefficients could be divided into three groups: exposure coefficients of 0.8(0.9) for areas with strong winds, 1.0(1.1) for partially exposed areas, and 1.2 for areas with dense windbreaks. After analyzing the exposure coefficients for 94 areas in South Korea according to the ISO4355 standard, all of the areas had two coefficients (1.0 and 0.8), except Daegwallyeong (0.5) and Yeosu (0.6), which had one coefficient each. In South Korea, the probability of snow is greater inland than in coastal areas and there are fewer days with a maximum wind velocity > $5m{\cdot}s^{-1}$ inland. When determining the exposure coefficients in South Korea, we can subdivide the country into three regions: coastal areas with strong winds have an exposure coefficient of 0.8; inland areas have a coefficient of 1.0; and areas with dense windbreaks have an exposure coefficient of 1.2. Further research that considers the number of days with a wind velocity > $5m{\cdot}s^{-1}$ as the threshold wind speed is needed before we can make specific recommendations for the exposure coefficient for different regions.

• Korean Journal of Remote Sensing
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• v.36 no.5_2
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• pp.893-906
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• 2020

This paper reports on the analysis of the location accuracy of a precision image generation system manufactured for CAS 500. The planned launch date of the CAS 500 is 2021, and since it has not yet been launched, the analysis was performed using KOMPSAT-3A satellite images having similar specifications to the CAS 500. In this paper, we have checked the geolocation accuracy of initial sensor model, the model point geolocation accuracy of the precise sensor model, the geolocation accuracy of the precise sensor model using the check point, and the geolocation accuracy of the precise orthoimage using 30 images of the Korean Peninsula. In this study, the target geolocation accuracy is to have an RMSE within 2 pixels when an accurate ground control point is secured. As a result, it was confirmed that the geolocation accuracy of the precision sensor model using the checkpoint was about 1.85 pixels in South Korea and about 2.04 pixels in North Korea, and the geolocation accuracy of the precise orthoimage was about 1.15 m in South Korea and about 3.23 m in North Korea. Overall, it was confirmed that the accuracy of North Korea was low compared to that of South Korea, and this was confirmed to have affected the measured accuracy because the GCP (Ground Control Point) quality of the North Korea images was poor compared to that of South Korea. In addition, it was confirmed that the accuracy of the precision orthoimage was slightly lower than that of precision sensor medel, especially in North Korea. It was judged that this occurred from the error of the DTM (Digital Terrain Model) used for orthogonal correction. In addition to the causes suggested by this paper, additional studies should be conducted on factors that may affect the position accuracy.

• Journal of Environmental Impact Assessment
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• v.30 no.3
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• pp.141-154
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• 2021

In this study, the effect of the restoration of Yaksa stream and the construction of an apartment complex by the urban renewal project in the Yaksa district of Chuncheon on air quality in the surrounding area was evaluated using computational fluid dynamics (CFD) model simulations. In orderto compare the impact of the project, wind and pollutant concentration fields were simulated using topographic data in 2011 and 2017, which stand for the periods before and after the urban renewal project, respectively. In the numerical experiments, the scenarios were set to analyze the effect of the construction of the apartment complex and the effect of stream restoration. Wind direction and wind speed data obtained from the Chuncheon Automated Synoptic Observing System (ASOS) were used as the inflow boundary conditions, and the simulation results were weighted according to the frequencies of the eight-directional inflow wind directions. The changes in wind speed and NO_X concentration distribution according to the changes in building and terrain between scenarios were compared. As a result, the concentration of NO_X emitted from the surrounding roads increased by the construction of the apartment complex, and the magnitude of the increase was reduced as the result of including the effect of stream restoration. The concentration of NO_X decreased around the restored stream, while the concentration increased significantly around the constructed apartment complex. The increase in the concentration of NO_X around the apartment complex was more pronounced in the place located in the rear of the wind direction to the apartment complex, and the effect remains up to the height of the building. In conclusion, it was confirmed that the relative arrangement of apartment complex construction and stream restoration in relation to the main wind direction of the target area was one of the major factors in determining the surrounding air quality.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.351-361
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• 2024

Satellite imagery contains various elements such as clouds, cloud shadows, and terrain shadows. Accurately identifying and eliminating these factors that complicate satellite image analysis is essential for maintaining the reliability of remote sensing imagery. For this reason, satellites such as Landsat-8, Sentinel-2, and Compact Advanced Satellite 500-1 (CAS500-1) provide Usable Data Masks(UDMs)with images as part of their Analysis Ready Data (ARD) product. Precise detection of clouds and their shadows is crucial for the accurate construction of these UDMs. Existing cloud and their shadow detection methods are categorized into threshold-based methods and Artificial Intelligence (AI)-based methods. Recently, AI-based methods, particularly deep learning networks, have been preferred due to their advantage in handling large datasets. This study aims to analyze the applicability of constructing UDMs for high-resolution satellite images through deep learning-based cloud and their shadow detection using open-source datasets. To validate the performance of the deep learning network, we compared the detection results generated by the network with pre-existing UDMs from Landsat-8, Sentinel-2, and CAS500-1 satellite images. The results demonstrated that high accuracy in the detection outcomes produced by the deep learning network. Additionally, we applied the network to detect cloud and their shadow in KOMPSAT-3/3A images, which do not provide UDMs. The experiment confirmed that the deep learning network effectively detected cloud and their shadow in high-resolution satellite images. Through this, we could demonstrate the applicability that UDM data for high-resolution satellite imagery can be constructed using the deep learning network.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.3
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• pp.161-173
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• 2024

In South Korea, 50-65% of the annual precipitation is concentrated during the summer monsoon season, which is called Changma. In 2023, extreme precipitation was observed during Changma period, and was recorded the highest amount in southern part of Korea. Extreme precipitation in forest region is one of significant factors related to the landslide. Therefore, accurate monitoring and understanding of precipitation patterns are crucial for preventing the landslide disasters in Changma period. This study investigated the precipitation patterns including precipitation intensity, duration, and total amount in mountainous and non-mountainous regions during the Changma period using dataset observed from the Korea Forest Service's Automatic Mountain Meteorology Observation Station (AMOS) and the Korea Meteorological Administration (KMA). Precipitation map produced from the Modified Korean-Parameter elevation Regressions an Independent Slopes Model (MK-PRISM) was also used to verify precipitation patterns in areas affected by landslides in Gyeongsangbuk-do province. The results used from precipitation observations revealed that the total amount of precipitation was greater at elevations such as mountainous regions. In particular, extreme precipitation events such as precipitation duration exceeding 50 hours with amount of over 300 mm and heavy rainfalls of over 30 mm/hr occurred at landslide areas including Mungyeong, Bonghwa, and Yeongju in Gyeongsangbuk-do province. Total amount of precipitation produced by MK-PRISM in these areas during Changma period were more than double compared with 30 years mean values obtained from KMA. The results conducted in this study indicate that it is essential to establish the thresolds considering recent precipitation patterns to effectively prepare and prevent for landslide disasters.