• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.5
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• pp.281-302
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• 2021

Railway projects have been consistently increasing in Korea. In relation to this trend, the mechanized tunneling using Tunnel Boring Machine (TBM) is preferably applied for mining urban areas and passing under rivers. The TBM tunneling under difficult grounds like mixed faces with high water pressure could require ground improvements for stable TBM advance or safe cutter head intervention (CHI). In this study, pre-grouting works for CHI in Daegok-Sosa railway project are presented in terms of the grouting zone design, the executions and the results, the lessons learned from the experience. It should be mentioned that the grouting from inside TBM was carried out several times and turned out to be inefficient in the project. Therefore, grouting experiences from the surface are highlighted in this study. Jet grouting was implemented on CHI points on land, while permeation grouting off shore in the Han River, which mostly allow to access the cutter head of TBM in free air with stable faces. The results of CHI works have been analyzed and the lesson learned are suggested.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.409-418
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• 2021

The land cover map is a very important data that is used as a basis for decision-making for land policy and environmental policy. The land cover map is mapped using remote sensing data, and the classification results may vary depending on the acquisition time of the data used even for the same area. In this study, to overcome the classification accuracy limit of single-period data, multi-series satellite images were used to learn the difference in the spectral reflectance characteristics of the land surface according to seasons on a U-Net model, one of the deep learning algorithms, to improve classification accuracy. In addition, the degree of improvement in classification accuracy is compared by comparing the accuracy of single-period data. Seoul, which consists of various land covers including 30% of green space and the Han River within the area, was set as the research target and quarterly Sentinel-2 satellite images for 2020 were aquired. The U-Net model was trained using the sub-class land cover map mapped by the Korean Ministry of Environment. As a result of learning and classifying the model into single-period, double-series, triple-series, and quadruple-series through the learned U-Net model, it showed an accuracy of 81%, 82% and 79%, which exceeds the standard for securing land cover classification accuracy of 75%, except for a single-period. Through this, it was confirmed that classification accuracy can be improved through multi-series classification.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.493-502
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• 2021

Recently, because of changes in temperature and rising water temperatures due to increased pollution sources, many algae have been produced in the water system. Therefore, there has been a lot of research using satellite images for the generation and monitoring of green algae. However, in prior studies, it is difficult to consider the optical properties of the local water system by using only a single index, and by using medium and low-resolution satellite images to conduct large-scale algae detection, there is a problem of accuracy in narrow, broad rivers. Therefore, in this work, we utilize high-resolution images of Sentinel-2 satellites to perform green algae detection on a single index (NDVI, SEI, FGAI) and development index (NDVI & SEI, FGAI & SEI) that mixes single indices. In this study, POD, FAR, and PC values were utilized to evaluate the accuracy of green algae detection algorithms, and the FGAI & SEI index showed the highest accuracy with 98.29% overall accuracy PC.

• Journal of Korea Water Resources Association
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• v.54 no.1
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• pp.27-38
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• 2021

Recently, many studies have been conducted to use the radar rainfall in hydrology. However, in the case of weather radar, the beam is blocked due to the limitation of the observation such as mountain effect, which causes underestimation of the radar rainfall. In this study, the radar rainfall was estimated using the Hybrid Sacn Reflectivity (HSR) technique for hydrological use of weather radar and the runoff analysis was performed using the GRM model which is a distributed rainfall-runoff model. As a result of performing the radar rainfall correction and runoff simulation for 5 rainfall events, the accuracy of the dual-polarization radar rainfall using the HSR technique (Q_H_KDP) was the highest with an error within 15% of the ground rainfall. In addition, the result of runoff simulation using Q_H_KDP also showed an accuracy of R2 of 0.9 or more, NRMSE of 1.5 or less and NSE of 0.5 or more. From this study, we examined the application of the dual-polarization radar and this results can be useful for studies related to the hydrological application of dual-polarization radar rainfall in the future.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.24 no.1
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• pp.53-68
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• 2021

The study aims to propose a locating method of green space for reducing Particulate Matter (PM) in ambient air in conjunction with its source traces and vulnerable groups. In order to carry out the aims and purposes, a literature review was conducted to derive indicators of vulnerable area to PM. Based on the developed indicators, the vulnerable areas and green spaces creation strategies for each cluster were developed for the case of Seongdong-gu, Seoul. As a result, six indicators for vulnerability analysis were came out including the vulnerable groups (children's facilities, old people's facilities), emission sources (air pollutant emission workplaces, roads), and environmental indicators (particulate matter concentration, NDVI). According to the six selected indicators, the target area was divided into 39 hexagons and analyzed to result the most vulnerable areas to particulate matter. As a result of comprehensive vulnerability analysis, the Seongsu-dong area was found to be the most vulnerable to particulate matter, and 5 clusters were derived through k-means cluster analysis. Cluster 1 was analyzed as areas that most vulnerable to particulate matter as a result of the comprehensive analysis, therefore urgent need to create green spaces to reduce particulate matter. Cluster 2 was areas that mostly belonged to the Han River. Cluster 3 corresponds to the largest number of hexagons, and since many vulnerable groups are distributed, it was analyzed as a cluster that required the creation of a green spaces to reduce particulate matter, focusing on facilities for vulnerable groups. Three hexagons are included in cluster 4, and the cluster has many roads and lacks vegetation in common. Cluster 5 has a lot of green spaces and is generally distributed with fewer vulnerable groups and emission sources; however, it has a high level of particulate matter concentration. In a situation where various green spaces creation projects for reducing particulate are being implemented, it is necessary to consider the vulnerable groups and emission sources and to present green space creation strategies for each space characteristic in order to increase the effectiveness of such projects. Therefore, this study is regarded as meaningful in suggesting a method for selecting a green area for reducing PM.

• Journal of Korea Water Resources Association
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• v.55 no.1
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• pp.71-84
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• 2022

The objective of this study is to develop a two-dimensional (2D) flood model that can perform accurate flood analysis with simple input data. The 2D flood inundation models currently used to create flood forecast maps require complex input data and grid generation tools. This sometimes requires a lot of time and effort for flood modeling, and there may be difficulties in constructing input data depending on the situation. In order to compensate for these shortcomings, in this study, a grid-based model that can derive accurate and rapid flood analysis by reflecting correct topography as simple input data was developed. The calculation efficiency was improved by extending the existing 2×2 sub-grid model to a 5×5. In order to examine the accuracy and applicability of the model, it was applied to the Gamcheon Basin where both urban and river flooding occurred due to Typhoon Rusa. For efficient flood analysis according to user's selection, flood wave propagation patterns, accuracy and execution time according to grid size and number of sub-grids were investigated. The developed model is expected to be highly useful for flood disaster mapping as it can present the results of flooding analysis for various situations, from the flood inundation map showing accurate flooding to the flood risk map showing only approximate flooding.

• Journal of Korea Water Resources Association
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• v.55 no.7
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• pp.515-529
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• 2022

Identifying the available water resources amount is an essential process in establishing a sustainable water resources management plan. Dam facility is a major infrastructure storing and supplying water during the dry season, and the water supply yield of the dam varies depending on dam inflow conditions or operation rule. In South Korea, water supply yield of dam is calculated by reservoir simulation based on observed historical dam inflow data. However, the water supply capacity of a dam can be underestimated or overestimated depending on the existence of historical drought events during the simulation period. In this study, probabilistic inflow data was generated and used to estimate the appropriate range of the water supply yield of hydropower dams. That is, a method for estimating the probabilistic dam inflow that fluctuates according to climatic and socio-economic conditions and the range of water supply yield for hydropower dams was presented, and applied to hydropower dams located in the Han river in South Korea. It is expected that the understanding water supply yield of the hydropower dams will become more important to respond to climate change in the future, and this study will contribute to national water resources management planning by providing potential range of water supply yield of hydropower dams.

• Journal of Internet of Things and Convergence
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• v.8 no.5
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• pp.11-16
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• 2022

As an interest in the development of artificial intelligence(AI) technology in the water supply sector increases, we have developed an AI algorithm that can predict improvement decision-making ratings through repetitive learning using the data of pipe condition evaluation results, and present the most reliable prediction model through a verification process. We have developed the algorithm that can predict pipe ratings by pre-processing 12 indirect evaluation items based on the 2020 Han River Basin's basic plan and applying the AI algorithm to update weighting factors through backpropagation. This method ensured that the concordance rate between the direct evaluation result value and the calculated result value through repetitive learning and verification was more than 90%. As a result of the algorithm accuracy verification process, it was confirmed that all water pipe type data were evenly distributed, and the more learning data, the higher prediction accuracy. If data from all across the country is collected, the reliability of the prediction technique for pipe ratings using AI algorithm will be improved, and therefore, it is expected that the AI algorithm will play a role in supporting decision-making in the objective evaluation of the condition of aging pipes.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.197-213
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• 2022

In this study, text mining analysis was performed on online big data for recognition and assessment of urban brand I Seoul U. To this end, TEXTOM, a processing program for data acquisition and analysis was used, and the 'I SEOUL U' keyword was selected as an analysis keyword. Keyword analysis shows the keywords associated with I Seoul U to be as follows: First, as a business and marketing term, keywords include pop-up store, gallery, co-branding, (festival, etc.), commodities, private companies and online. Second, as an event-related term, keywords include Han River, tree-planting day, tree planting, Hongdae, Christmas, Mapo, Jung-gu, Sejong University, and festival. Third, as a promotional term, keywords include robotics engineer Dr. Dennis Hong, Government, Art and Korea. In the N Gram analysis, as the city brand of Seoul, I Seoul U, in the public interest, was found to contribute to the commercial activities of private companies. In connection-oriented analysis, business and marketing, events, and promotions have been derived as categories. In matrix analysis, it was found that the products of the pop-up store are mainly developed, and products in the form of co-branding were being developed. In the topic modeling, a total of 10 topics were extracted and needs for commercial utilization and information for event festivals were mostly found.

• Korean Journal of Ichthyology
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• v.33 no.2
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• pp.57-63
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• 2021

This study was carried out to clarify the egg, larvae and juveniles development of Carassius cuvieri in Nakdong River, Gyeongbuk. The amount of spawning of female C. cuvieri was about 30,400~44,900 (average 37,650±7,250). The egg shape was circular and the size was 1.24~1.37 mm (average 1.31±0.04 mm, n=30). At 100 hours after fertilization, more than of the total embryos were hatched. The newly hatched larvae had an 4.69~5.65 mm in total length (average 5.15±0.31 mm, n=10) and had egg yolk in the abdomen. At 3 days after hatching, the preflexion larvae absorbed all egg yolk was 6.27~6.70 mm in total length (average 6.59±0.08 mm, n=10). On the 10 days after hatching, the postflexion larvae were 8.71~8.92 mm in total length (average 8.81±0.07 mm, n=10), and the tip of the caudal fin was bent at 45°. On the 42 days after hatching, the total length of 15.1~16.8 mm (average 15.8±0.57 mm, n=10) was transferred to juvenile as the number of fins was (iii17 dorsal fins, iii4 anal fins) reached a constant number of each part.