• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.6B
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• pp.589-598
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• 2010

When the hydraulic structures, such as bridge and weir, are consecutively installed to a short section of a river with complicated cross section, analyzing the flow characteristics and the riverbed change modality of the river is very important. In the 250 m section of the Taehwa river near the Samho-bridge, which passes through Ulsan city, three bridges has been installed, and the tributary water is flowing into both up and downstream of the section. Due to these factors, when the flood occurs, the cross section of the river changes vastly by the water level change and scour. Even so, due to the fact that the Samho-bridge divides the section into two parts, the national river and the regional river, each part is being analyzed separately by the onedimensional model. In this study, the flow characteristics due to the bridge concentration and the tributary water inflow were jointly analyzed for both up and downstream by using the one-dimensional HEC-RAS model and the two-dimensional SMS model, such as RMA2. The riverbed change modality of the section was also investigated by using the SED2D model. The results showed that the water level difference between the HEC-RAS and RMA2 was 0.87 m when applied to the three consecutive bridges. The riverbed change simulation using SED2D showed that the maximum scour was 0.231 m and it occurred at the Samho-bridge, which located in the middle and has short pier distance. In conclusion, when planning the river maintenance for the regions with concentrated bridges or the sections with severe changes in cross-section and flow, estimating the flood elevation by two-dimensional model and establishing countermeasures for the scouring of the bridge are required. In addition, an integrated analysis on both the national river and the regional river is necessary.

• Journal of Environmental Impact Assessment
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• v.32 no.6
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• pp.377-388
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• 2023

Carbon management is emerging as an important factor for global warming control, and land use change is considered one of the causes. To quantify the changes in carbon stocks due to development, this study attempted to calculate carbon storage by borrowing the formula of the InVEST Carbon Storage and Sequestration Model (InVEST Model). Before analyzing carbon stocks, a carbon pool was compiled based on previous studies in Korea. Then, we estimated the change in carbon stocks according to the development of Osong National Industrial Park (ONIP) and the application of alternatives. The analysis shows that 16,789.5 MgC will be emitted under Alternative 1 and 16,305.3 MgC under Alternative 2. These emissions account for 44.4% and 43.1% of the pre-project carbon stock, respectively, and shows that choosing Alternative 2 is advantageous for reducing carbon emissions. The difference is likely due to the difference in grassland area between Alternatives 1 and 2. Even if Alternative 2 is selected, efforts are needed to increase the carbon storage effect by managing the appropriate level of green cover in the grassland, creating multi-layered vegetation, and installing low-energy facilities. In addition, it is suggested to conserve wetlands that can be lost during the stream improvement process or to create artificial wetlands to increase carbon storage. The assessment of carbon storage using carbon pools by land cover can improve the objectivity of comparison and evaluation analysis results for land use plans in Environmental Impact Assessment and Strategic Environmental Impact Assessment. In addition, the carbon pool generated in this study is expected to be used as a basis for improving the accuracy of such analyses.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.481-493
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• 2023

In high-density urban areas, the urban heat island effect increases urban temperatures, leading to negative impacts such as worsened air pollution, increased cooling energy consumption, and increased greenhouse gas emissions. In urban environments where it is difficult to secure additional green spaces, rooftop greening is an efficient greenhouse gas reduction strategy. In this study, we not only analyzed the current status of the urban heat island effect but also utilized high-resolution satellite data and spatial information to estimate the available rooftop greening area within the study area. We evaluated the mitigation effect of the urban heat island phenomenon and carbon sequestration capacity through temperature predictions resulting from rooftop greening. To achieve this, we utilized WorldView-2 satellite data to classify land cover in the urban heat island areas of Busan city. We developed a prediction model for temperature changes before and after rooftop greening using machine learning techniques. To assess the degree of urban heat island mitigation due to changes in rooftop greening areas, we constructed a temperature change prediction model with temperature as the dependent variable using the random forest technique. In this process, we built a multiple regression model to derive high-resolution land surface temperatures for training data using Google Earth Engine, combining Landsat-8 and Sentinel-2 satellite data. Additionally, we evaluated carbon sequestration based on rooftop greening areas using a carbon absorption capacity per plant. The results of this study suggest that the developed satellite-based urban heat island assessment and temperature change prediction technology using Random Forest models can be applied to urban heat island-vulnerable areas with potential for expansion.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1413-1425
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• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.

• Journal of the Korean Society of Radiology
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• v.17 no.5
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• pp.641-649
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• 2023

This study compares and analyzes the performance of a shield manufactured using 3D printing technology to find out its applicability as a shield in high-energy electron beam therapy. Actual measurement and monte carlo simulations were performed to evaluate the shielding performance of 3D printing materials for high-energy electron beams. First, in order to secure reliability for the simulation, a source term evaluation was conducted by referring to the IAEA's TRS-398 recommendation. Second, to analyze the shielding performance of PLA+W (93%), a specimen was manufactured using a 3D printer, and the shielding rate by thickness according to electron beam energy was evaluated. Third, the shielding thickness required for electron beam treatment was calculated through a comparative analysis of shielding performance between PLA+W (93%) and existing shielding bodies. First, as a result of the evaluation of the source term through actual measurement and simulation, the TRS-398 recommendation was satisfied with an error of less than 1%, thereby securing the reliability of the simulation. Second, as a result of the shielding performance analysis for PLA+W (93%), 6 MeV electron beams showed a shielding rate of more than 95% at 3.12 mm, and 15 MeV electron beams showed a shielding rate of more than 90% at 10 mm thickness. Third, through simulations, comparative analysis between PLA+W (93%) materials and existing shields showed high shielding rates within the same thickness in the order of tungsten, lead, copper, PLA+W (93%), and aluminum. 6 MeV electron beams showed almost similar shielding rates at 5 mm or more and 15 MeV electron beams. Through this study in the future, it is judged that it can be used as basic data for the production and application of shielding bodies using PLA+W (93%) materials in high-energy electron beam treatment.

• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.35-44
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• 2024

Research in dam inflow prediction has actively explored the utilization of data-driven machine learning and deep learning (ML&DL) tools across diverse domains. Enhancing not just the inherent model performance but also accounting for model characteristics and preprocessing data are crucial elements for precise dam inflow prediction. Particularly, existing rainfall data, derived from snowfall amounts through heating facilities, introduces distortions in the correlation between snow accumulation and rainfall, especially in dam basins influenced by snow accumulation, such as Soyang Dam. This study focuses on the preprocessing of rainfall data essential for the application of ML&DL models in predicting dam inflow in basins affected by snow accumulation. This is vital to address phenomena like reduced outflow during winter due to low snowfall and increased outflow during spring despite minimal or no rain, both of which are physical occurrences. Three machine learning models (SVM, RF, LGBM) and two deep learning models (LSTM, TCN) were built by combining rainfall and inflow series. With optimal hyperparameter tuning, the appropriate model was selected, resulting in a high level of predictive performance with NSE ranging from 0.842 to 0.894. Moreover, to generate rainfall correction data considering snow accumulation, a simulated snow accumulation algorithm was developed. Applying this correction to machine learning and deep learning models yielded NSE values ranging from 0.841 to 0.896, indicating a similarly high level of predictive performance compared to the pre-snow accumulation application. Notably, during the snow accumulation period, adjusting rainfall during the training phase was observed to lead to a more accurate simulation of observed inflow when predicted. This underscores the importance of thoughtful data preprocessing, taking into account physical factors such as snowfall and snowmelt, in constructing data models.

• Journal of Korea Water Resources Association
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• v.57 no.5
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• pp.333-346
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• 2024

High-resolution medium-range streamflow prediction is crucial for sustainable water quality and aquatic ecosystem management. For reliable medium-range streamflow predictions, it is necessary to understand the characteristics of forcings and to effectively utilize weather forecast data with low spatio-temporal resolutions. In this study, we presented a comparative analysis of medium-range streamflow predictions using the distributed hydrological model, WRF-Hydro, and the numerical weather forecast Global Data Assimilation and Prediction System (GDAPS) in the Geumho River basin, Korea. Multiple forcings, ground observations (AWS&ASOS), numerical weather forecast (GDAPS), and Global Land Data Assimilation System (GLDAS), were ingested to investigate the performance of streamflow predictions with highresolution WRF-Hydro configuration. In terms of the mean areal accumulated rainfall, GDAPS was overestimated by 36% to 234%, and GLDAS reanalysis data were overestimated by 80% to 153% compared to AWS&ASOS. The performance of streamflow predictions using AWS&ASOS resulted in KGE and NSE values of 0.6 or higher at the Kangchang station. Meanwhile, GDAPS-based streamflow predictions showed high variability, with KGE values ranging from 0.871 to -0.131 depending on the rainfall events. Although the peak flow error of GDAPS was larger or similar to that of GLDAS, the peak flow timing error of GDAPS was smaller than that of GLDAS. The average timing errors of AWS&ASOS, GDAPS, and GLDAS were 3.7 hours, 8.4 hours, and 70.1 hours, respectively. Medium-range streamflow predictions using GDAPS and high-resolution WRF-Hydro may provide useful information for water resources management especially in terms of occurrence and timing of peak flow albeit high uncertainty in flood magnitude.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.1
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• pp.59-78
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• 2024

In tunnel construction, the stability is evaluated by the settlement of adjacent structures and ground, but the shear strain of the ground is the main factor that determines the failure mechanism of the ground due to the tunnel excavation and the change of the operating load, and can be used to review the stability of the tunnel excavation and to calculate the reinforcement area. In this study, a twin tunnel excavation was simulated on a soft ground in an urban area through a laboratory model test to analyze the behavior of the twin tunnel excavation on the adjacent pile grouped foundation and adjacent ground. Both the displacement and the shear strain of ground were obtained using a close-range photogrammetry during laboratory model test. In addition, two-dimensional finite element numerical analysis was performed based on the model test. The results of a back-analysis showed that the maximum shear strain rate tends to decrease as the horizontal distance between the pillars of the twin tunnel and the vertical distance between the toe of the pile group and the crown of the tunnel were decreased. The impact of the second tunnel on the first tunnel and pile group was decreased as the horizontal distance between the pillars of the twin tunnel was increased. In addition, the vertical distance between the toe of the pile group and the crown of the tunnel had a relatively greater impact on the shear strain results than the horizontal distance of the pillars between the twin tunnels. According to the results of the close-range photogrammetry and numerical analysis, the settlement of adjacent pile group and adjacent ground was measured within the design criteria, but the shear strain of the ground was judged to be outside the range of small strain in all cases and required reinforcement.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.68 no.4
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• pp.431-437
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• 2023

This study evaluated the ecological characteristics related to spike heading time of three Korean foxtail millet cultivars, i.e., one early and two late maturities, and a finger millet cultivar in the north-central region of the Korean Peninsula, Kangwon Province. The changes in heading time occurred due to the changes in planting time from mid-May to late June. The heading time of the early-maturity cultivars was early August, with 80 days required for heading (DH) for the mid-May planting; late August, with 65 DHs for the late June planting; and mid-late August, with 100 DHs and mid-October, with 65 DHs, respectively, for the late-maturity cultivars. The accumulated temperature at heading time ranged from 1,700℃ of mid-May planting to 1,500℃ of late June planting in the early-maturity cultivars. In contrast, it ranged from 2,100℃ to 1,900℃ in the late-maturity cultivars. The photoperiod at heading time ranged from 14.0 h to 13.2 h in the early-maturity cultivars, whereas it was from 13.2 h to 12.5 h in the late-maturity cultivars. Considering that the limiting heading time of Korean foxtail millet and finger millet in the northern region of Kangwon Povince is late August, the limiting accumulated temperature at the heading time was evaluated to be approximately 1,500℃ and 2,000℃ for early and late-maturity cultivars, respectively. The mean daily temperature from planting to heading time showed a negative correlation with the DH, which was shortened with the increase in mean daily temperature. This suggests that delaying the planting time from May to June in the north-central region of the Korean Peninsula increases the mean daily temperature during vegetative growth periods, resulting in the decrease of the DH and the accumulated temperature.