• Journal of Korea Water Resources Association
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• v.39 no.3 s.164
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• pp.227-240
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• 2006

In the several decades, various storage facilities have been developed and operated to supply water resource, flood control or environmental preservation etc. Then, how those man-maid storage facilities affect on the downstream water and environment and how the hydrologists can evaluate those features for water resources problem-solving are high-concentrated problems in this field. Most large watersheds in Korea contain various types of artificial facilities such dams, reservoirs, in-land ponds, wetlands etc. But the study to develop the technology for achieving the effect of the variances and properties of the long term streamflow caused by the artificial storage facilities have been on the simple watershed models and experimental modeling in the real fields. In this paper, we introduce the procedure and methods to consider the above problems based on continuous and semi-distributed featured SWAT model. At the first, we describe the elements and mechanisms of storage facilities in SWAT model to see how we can apply that in proper and appropriate manner for real field problems. Then, we applied the process to a sample watershed, Taewha River basin which covers the most of Ulsan region. Specially, we concentrate on our effort to the effect of upper reservoirs on down stream long term flows based on various scenario basis. The result was described and analysed in spacial and temporal variations on that basin using the precise manner.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.260-260
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• 2016

Soil erosion is a very serious problem from agricultural as well as environmental point of view. Various computer models have been used to estimate soil erosion and assess erosion control practice. Universal Soil loss equation (USLE) is a popular model which has been used in many countries around the world. Erosivity (USLE R-factor) is one of the USLE input parameters to reflect impacts of rainfall in computing soil loss. Value of R factor depends upon Energy (E) and maximum rainfall intensity of specific period ($I30_{max}$) of that rainfall event and thus can be calculated using higher temporal resolution rainfall data such as 10 minute interval. But 10 minute interval rainfall data may not be available in every part of the world. In that case we can use hourly rainfall data to compute this R factor. Maximum 60 minute rainfall ($I60_{max}$) can be used instead of maximum 30 minute rainfall ($I30_{max}$) as suggested by USLE manual. But the value of Average annual R factor computed using hourly rainfall data needs some correction factor so that it can be used in USLE model. The objective of our study are to derive relation between averages annual R factor values using 10 minute interval and hourly rainfall data and to determine correction coefficient for R factor using hourly Rainfall data.75 weather stations of Korea were selected for our study. Ten minute interval rainfall data for these stations were obtained from Korea Meteorological Administration (KMA) and these data were changed to hourly rainfall data. R factor and $I60_{max}$ obtained from hourly rainfall data were compared with R factor and $I30_{max}$ obtained from 10 minute interval data. Linear relation between Average annual R factor obtained from 10 minute interval rainfall and from hourly data was derived with $R^2=0.69$. Correction coefficient was developed for the R factor calculated using hourly rainfall data.. Similarly, the relation was obtained between event wise $I30_{max}$ and $I60_{max}$ with higher $R^2$ value of 0.91. Thus $I30_{max}$ can be estimated from I60max with higher accuracy and thus the hourly rainfall data can be used to determine R factor more precisely by multiplying Energy of each rainfall event with this corrected $I60_{max}$.

• Journal of Environmental Health Sciences
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• v.47 no.2
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• pp.155-165
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• 2021

Objectives: Acute exposure to high concentrations of chemicals can occur when a chemical accident takes place. As such exposure can cause ongoing environmental pollution, such as in the soil and groundwater, there is a need for a tool that can assess health effects in the long term. The purpose of this study was assessing the health risks of residents living near a chemical accident site due to long-term exposure while considering the temporal concentration changes of the toxic chemicals leaked during the accident until their extinction in the environment using a multimedia environmental dynamics model. Methods: A health risk assessment was conducted on three cases of formaldehyde chemical accidents. In this study, health risk assessment was performed using a multimedia environmental dynamics model that considers the behavior of the atmosphere, soil, and water. In addition, the extinction period of formaldehyde in the environment was regarded as extinction in the environment when the concentration in the air and soil fell below the background concentration prior to the accident. The subjects of health risk assessment were classified into four groups according to age: 0-9 years old, 10-18 years old, 19-64 years old, and over 65 years old. Carcinogenic risk assessment by respiratory exposure and non-carcinogenic risk assessment by soil intake were conducted as well. Results: In the assessment of carcinogenic risk due to respiratory exposure, the excess carcinogenic risk did not exceed 1.0×10-6 in all three chemical accidents, so there was no health effect due to the formaldehyde chemical accident. As a result of the evaluation of non-carcinogenic risk due to soil intake, none of the three chemical accidents had a risk index of 1, so there was no health effect. For all three chemical accidents, the excess cancer risk and hazard index were the highest in the age group 0-9. Next, 10-18 years old, 65 years old or older, and 19-64 years old showed the highest risk. Conclusion: This study considers environmental changes after a chemical accident occurs and until the substance disappears from the environment. It also conducts a health risk assessment by reflecting the characteristics of the long-term persistence and concentration change over time. It is thought that it is of significance as a health risk assessment study reflecting the exposure characteristics of the accident substance for an actual chemical accident.

• Journal of Environmental Impact Assessment
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• v.28 no.3
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• pp.245-262
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• 2019

The objectives of this study were to construct a three-dimensional water quality model (EFDC) for the river reach between Chilgok Weir and Gangjeong-Goryong Weir (GGW) located in Nakdong River, and evaluate the effect of hydraulic changes, such as water level and flow velocity, on the control of water quality and algae biomass. After calibration, the model accurately simulated the temporal changes of the upper and lower water temperatures that collected every 10 minutes, and appropriately reproduced changes in organic matter, nitrogen, phosphorus, and cyanobacteria. However, the simulated values were overestimated for the diatoms and green algae cell density, possibly due to the uncertainties of the parameters associated with algae metabolism and the lack of zooplankton predation function in the simulations. As a result of scenario simulation of running the water level of GGW from EL. 19.44 m to EL. 14.90 m (4.54 m drop), Chl-a and algae cell density decreased significantly.In particular,the cyanobacteria on the surface layer, which causes algal bloom, declined by 56.1% in the low water level scenario compared to the existing management level. The results of this study are in agreement with the previous studies that maintenance of critical flow velocity is effective for controlling cyanobacteria, and imply that hydraulic control such as decrease of water level and residence time in GGW is an alternative to limit the overgrowth of algae.

• Journal of Korean Society of Forest Science
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• v.108 no.2
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• pp.208-215
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• 2019

The goal of this study was to analyze the reactivity of a volumetric water content sensor (soil moisture sensor) and tensiometer and to review their use in the early detection of a shallow landslide. We attempted to demonstrate shallow and rapid slope collapses using three different soil ratios under artificial rainfall at 120 mm/h. Our results showed that the measured value of the volumetric water-content sensor converged to 30~37%, and that of the tensiometer reached -3~-5 kPa immediately before the collapse of the soil under all three conditions. Based on these results, we discussed a temporal range for early warnings of landslides using measurements of the volumetric water content sensors installed at the bottom of the soil slope, but could not generalize and clarify the exact timing for these early warnings. Further experiments under various conditions are needed to determine how to use both sensors for the early detection of shallow landslides.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.1004-1010
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• 2021

Ship-floating object accidents can lead not only to a delay in ship's operations, but also to large scale casualties. Hence, preventive measures are required to avoid them. This study analyzed the spatiotemporal aspects of such collisions based on the data on ship-floating object accidents in sea areas in the last five years, including the collisions in South Korea's territorial seas and exclusive economic zones. We also provide basic data for related research fields. To understand the distribution of the relative density of accidents involving floating objects, the sea area under analysis was visualized as a grid and a two-dimensional histogram was generated. A multinomial logistic regression model was used to analyze the effect of variables such as time of day and season on the collisions. The spatial analysis revealed that the collision density was highest for the areas extending from Geoje Island to Tongyeong, including Jinhae Bay, and that it was high near Jeongok Port in the West Sea and the northern part of Jeju Island. The temporal analysis revealed that the collisions occurred most frequently during the day (71.4%) and in autumn. Furthermore, the likelihood of collision with floating objects was much higher for professional fishing vessels, leisure vessels, and recreational fishing vessels than for cargo vessels during the day and in autumn. The results of this analysis can be used as primary data for the arrangement of Coast Guard vessels, rigid enforcement of regulations, removal of floating objects, and preparation of countermeasures involving preliminary removal of floating objects to prevent accidents by time and season.

• Journal of Korean Society on Water Environment
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• v.39 no.2
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• pp.162-174
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• 2023

In order to prevent water pollution caused by organic matter, Total Organic Carbon(TOC) has been adopted indicator and monitored. TOC can be divided into Dissolved Organic Carbon(DOC) and Particulate Organic Carbon(POC). POC is largely precipitated and removed during stream flow, which making DOC environmentally significant. However, there are lack of studies to define spatio-temporal distributions of DOC in stream affected by various land use. Therefore, it is necessary to estimate the past DOC concentration using other water quality indicators to evaluate status of watershed management. In this study, DOC was estimated by correlation and regression analysis using three different organic matter indicators monitored in mixed land-use watersheds. The results of correlation analysis showed that DOC has the highest correlation with TOC. Based on the results of the correlation analysis, the single- and multiple-regression models were developed using Biochemical Oxygen Demand(BOD), Chemical Oxygen Demand(COD), and TOC. The results of the prediction accuracy for three different regression models showed that the single-regression model with TOC was better than those of the other multiple-regression models. The trend analysis using extended average concentration DOC data shows that DOC tends to decrease reflecting watershed management. This study could contribute to assessment and management of organic water pollution in mixed land-use watershed by suggesting methods for assessment of unmeasured DOC concentration.

• Journal of radiological science and technology
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• v.46 no.4
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• pp.315-323
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• 2023

Post-mortem computed tomography (PMCT) is commonly employed in the field of forensic pathology. PMCT was mainly performed using a whole-body scan with a wide field of view (FOV), which lead to a decrease in spatial resolution due to the increased pixel size. This study aims to evaluate the potential for developing a super-resolution model based on conditional generative adversarial networks (CGAN) to enhance the image quality of CT. 1761 low-resolution images were obtained using a whole-body scan with a wide FOV of the head phantom, and 341 high-resolution images were obtained using the appropriate FOV for the head phantom. Of the 150 paired images in the total dataset, which were divided into training set (96 paired images) and validation set (54 paired images). Data augmentation was perform to improve the effectiveness of training by implementing rotations and flips. To evaluate the performance of the proposed model, we used the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Deep Image Structure and Texture Similarity (DISTS). Obtained the PSNR, SSIM, and DISTS values of the entire image and the Medial orbital wall, the zygomatic arch, and the temporal bone, where fractures often occur during head trauma. The proposed method demonstrated improvements in values of PSNR by 13.14%, SSIM by 13.10% and DISTS by 45.45% when compared to low-resolution images. The image quality of the three areas where fractures commonly occur during head trauma has also improved compared to low-resolution images.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.3
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• pp.321-335
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• 2023

Monitoring sediment loads in natural rivers is the key process in river engineering, but it is costly and dangerous. In practice, suspended loads are directly measured, and total loads, which is a summation of suspended loads and bed loads, are estimated. This study proposes a real-time sediment discharge monitoring system using the horizontal acoustic Doppler current profiler (H-ADCP) and support vector regression (SVR). The proposed system is comprised of the SVR model for suspended sediment concentration (SVR-SSC) and for total loads (SVR-Q_TL), respectively. SVR-SSC estimates SSC and SVR-Q_TL mimics the modified Einstein procedure. The grid search with K-fold cross validation (Grid-CV) and the recursive feature elimination (RFE) were employed to determine SVR's hyperparameters and input variables. The two SVR models showed reasonable cross-validation scores (R²) with 0.885 (SVR-SSC) and 0.860 (SVR-Q_TL). During the time-series sediment load monitoring period, we successfully detected various sediment transport phenomena in natural streams, such as hysteresis loops and sensitive sediment fluctuations. The newly proposed sediment monitoring system depends only on the gauged features by H-ADCP without additional assumptions in hydraulic variables (e.g., friction slope and suspended sediment size distribution). This method can be applied to any ADCP-installed discharge monitoring station economically and is expected to enhance temporal resolution in sediment monitoring.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.145-151
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• 2023

3D-CNN is one of the deep learning techniques for learning time series data. Such three-dimensional learning can generate many parameters, so that high-performance machine learning is required or can have a large impact on the learning rate. When learning dynamic hand-gestures in spatiotemporal domain, it is necessary for the improvement of the efficiency of dynamic hand-gesture learning with 3D-CNN to find the optimal conditions of input video data by analyzing the learning accuracy according to the spatiotemporal change of input video data without structural change of the 3D-CNN model. First, the time ratio between dynamic hand-gesture actions is adjusted by setting the learning interval of image frames in the dynamic hand-gesture video data. Second, through 2D cross-correlation analysis between classes, similarity between image frames of input video data is measured and normalized to obtain an average value between frames and analyze learning accuracy. Based on this analysis, this work proposed two methods to effectively select input video data for 3D-CNN deep learning of dynamic hand-gestures. Experimental results showed that the learning interval of image data frames and the similarity of image frames between classes can affect the accuracy of the learning model.