• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.723-736
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• 2022

In this study, monthly precipitation forecasting models that can predict up to 12 months in advance were constructed for the Geum River basin, and two statistical techniques, multiple linear regression (MLR) and artificial neural network (ANN), were applied to the model construction. As predictor candidates, a total of 47 climate indices were used, including 39 global climate patterns provided by the National Oceanic and Atmospheric Administration (NOAA) and 8 meteorological factors for the basin. Forecast models were constructed by using climate indices with high correlation by analyzing the teleconnection between the monthly precipitation and each climate index for the past 40 years based on the forecast month. In the goodness-of-fit test results for the average value of forecasts of each month for 1991 to 2021, the MLR models showed -3.3 to -0.1% for the percent bias (PBIAS), 0.45 to 0.50 for the Nash-Sutcliffe efficiency (NSE), and 0.69 to 0.70 for the Pearson correlation coefficient (r), whereas, the ANN models showed PBIAS -5.0~+0.5%, NSE 0.35~0.47, and r 0.64~0.70. The mean values predicted by the MLR models were found to be closer to the observation than the ANN models. The probability of including observations within the forecast range for each month was 57.5 to 83.6% (average 72.9%) for the MLR models, and 71.5 to 88.7% (average 81.1%) for the ANN models, indicating that the ANN models showed better results. The tercile probability by month was 25.9 to 41.9% (average 34.6%) for the MLR models, and 30.3 to 39.1% (average 34.7%) for the ANN models. Both models showed long-term predictability of monthly precipitation with an average of 33.3% or more in tercile probability. In conclusion, the difference in predictability between the two models was found to be relatively small. However, when judging from the hit rate for the prediction range or the tercile probability, the monthly deviation for predictability was found to be relatively small for the ANN models.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.627-646
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• 2022

Recently, the seriousness of climate change-related problems caused by global warming is growing, and the average temperature is also rising. As a result, it is affecting the environment in which various temperature-sensitive creatures and creatures live, and changes in the ecosystem are also being detected. Seasons are one of the important factors influencing the types, distribution, and growth characteristics of creatures living in the area. Among the most popular and easily recognized plant seasonal phenomena among the indicators of the climate change impact evaluation, the blooming day of flower and the peak day of autumn leaves were modeled. The types of plants used in the modeling were forsythia and cherry trees, which can be seen as representative plants of spring, and maple and ginkgo, which can be seen as representative plants of autumn. Weather data used to perform modeling were temperature, precipitation, and solar radiation observed through the ASOS Observatory of the Korea Meteorological Administration. As satellite data, MODIS NDVI was used for modeling, and it has a correlation coefficient of about -0.2 for the flowering date and 0.3 for the autumn leaves peak date. As the model used, the model was established using multiple regression models, which are linear models, and Random Forest, which are nonlinear models. In addition, the predicted values estimated by each model were expressed as isopleth maps using spatial interpolation techniques to express the trend of plant seasonal changes from 2003 to 2020. It is believed that using NDVI with high spatio-temporal resolution in the future will increase the accuracy of plant phenology modeling.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.747-763
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• 2022

Ocean salinity affects ocean circulation on a global scale and low salinity water around coastal areas often has an impact on aquaculture and fisheries. Microwave satellite sensors (e.g., Soil Moisture Active Passive [SMAP]) have provided sea surface salinity (SSS) based on the dielectric characteristics of water associated with SSS and sea surface temperature (SST). In this study, a Light Gradient Boosting Machine (LGBM)-based model for generating high resolution SSS from Geostationary Ocean Color Imager (GOCI) data was proposed, having machine learning-based improved SMAP SSS by Jang et al. (2022) as reference data (SMAP SSS (Jang)). Three schemes with different input variables were tested, and scheme 3 with all variables including Multi-scale Ultra-high Resolution SST yielded the best performance (coefficient of determination = 0.60, root mean square error = 0.91 psu). The proposed LGBM-based GOCI SSS had a similar spatiotemporal pattern with SMAP SSS (Jang), with much higher spatial resolution even in coastal areas, where SMAP SSS (Jang) was not available. In addition, when tested for the great flood occurred in Southern China in August 2020, GOCI SSS well simulated the spatial and temporal change of Changjiang Diluted Water. This research provided a potential that optical satellite data can be used to generate high resolution SSS associated with the improved microwave-based SSS especially in coastal areas.

• Korean Journal of Plant Resources
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• v.35 no.5
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• pp.594-606
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• 2022

This study was conducted to figure out the environment factors including vegetation structure and soil characteristics in natural habitats of Sageretia thea, and offers the basic information for habitats conservation and proliferation. The natural habitats of Sageretia thea were located at altitudes between 0~370 m with inclinations ranged as 3~35°. Through the vegetation research, the dominant species of tree layers were found to be divided into four communities. Cornus macrophylla (Com. I), Pinus thunbergii - Cinnamomum camphora (Com. II), Machilus thunbergii (Com. III), and Pinus thunbergii (Com. IV). The Species diversity (H') was 1.397~1.455, evenness (J') was 0.972~0.986, and dominance (D) was found to be 0.014~0.028. As a result of the physicochemical characteristics of soils, habitats soil mainly consisted of sandy soil and sandy loam soil. The average soil pH was 5.28~5.98, electronic conductivity was 0.22~63 ds/m, soil organic matter was 13.33~19.33 cmol⁺/kg, Exchange cations were appeared in the order of Ca²⁺, Mg²⁺, K⁺, and Na⁺. The Ordination result showed that Correlation coefficient between communities and environmental factors were significantly correlated with 4 main factors altitude, electronic conductivity, cation exchange capacity, exchangeable Na⁺. As expected, The result of this study will be helpful information on the preservation and mass production for use.

• Journal of the Korean Chemical Society
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• v.65 no.3
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• pp.197-202
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• 2021

In this study, activated clay treated with H₂SO₄ (20% by weight) and heat at 90 ℃ for 8 h for acid white soil was used as an adsorbent for the removal of PO₄^3--P in water. Prior to the adsorption experiment, the characteristics of activated clay was examined by X-ray Fluorescence Spectrometry (XRF) and BET surface area analyser. The adsorption of PO₄^3--P on activated clay was steeply increased within 0.25 h and reached equilibrium at 4 h. At 5 mg/L of low PO₄^3--P concentration, roughly 98% of adsorption efficiency was accomplished by activated clay. The adsorption data of PO₄^3--P were introduced to the adsorption isotherm and kinetic models. It was seen that both Freundlich and Langmuir isotherms were applied well to describe the adsorption behavior of PO₄^3--P on activated clay. For adsorption PO₄^3--P on activated clay, the Freundlich and Langmuir isotherm coefficients, K_F and Q, were found to be 8.3 and 20.0 mg/g, respectively. The pseudo-second-order kinetics model was more suitable for adsorption of PO₄^3--P in water/activated clay system owing to the higher correlation coefficient R² and the more proximity value of the experimental value q_e,exp and the calculated value q_e,cal than the pseudo-first-order kinetics model. The results of study indicate that activated clay could be used as an efficient adsorbent for the removal of PO₄³-P from water.

• Journal of Korea Entertainment Industry Association
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• v.14 no.4
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• pp.321-331
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• 2020

This study was conducted to investigate communication competence, clinical competence and experience of handover which influence self-efficacy of handover among nursing students. The study design was a descriptive survey. A total of 255 students were recruited from nursing departments in G-city. Structured questionnaire was self-administrated from June to September, 2019. The collected data were analyzed using t-test, ANOVA, Pearson's coefficient and stepwise multiple regression. As results of the study, communication competence 57.3, clinical competence 69.8 and self-efficacy of handover was 33.8. Self-efficacy of handover had significant differences in gender(F=4.60, p<.001), age(F=16.72, p<.001), grade(t=-6.39, p<.001), satisfaction of clinical practice(F=3.68, p=.027), education experience about handover(t=26.44, p<.001), experience of handover(t=4.84, p<.001), fear of handover(F=16.97, p<.001), and handover importance of patient's safety(F=6.42, p=.002). Self-efficacy of handover had significant positive correlations with communication competence(r=.249, p<.001) and clinical competence(r=.426, p<.001). In multiple regression analysis, fear of handover(β=-.294, p<.001), clinical competence(β=.252, p<.001), grade(β=.191, p=.001), experience of handover(β=.185, p<.001), gender(β=.150, p=.003), and education experience about handover(β=.126, p=.017) were significant factors of self-efficacy of handover explaining 40.0%(F=29.26, p<.001) of the variables. In conclusion, to enhance self-efficacy of handover for nursing students, it is necessary to develop educational program for increasing experiences of handover, education experience about handover, and clinical competence.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.1
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• pp.19-29
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• 2022

The result of a questionnaire survey conducted on fishermen using coastal fish traps shows that fall accidents during trap dropping and pulling constitute the highest proportion of accidents at 42.1 %, whereas slipping accidents on the deck or stricture accidents to the body due to the trap winding device constitute 21.1 % each. In addition, 53.2 % of all surveyed subjects responded that trap pulling is the most dangerous task, followed by fish sorting 33.8 %, and trap dropping 9.1 %. As for the main items requested by fishermen for improving the trap winding device, 36.8 % indicated a method to easily lift the trap from the water to the work deck, and 31.6 % indicated a method to overcome the rope tension and prevent slip when pulling the trap to reduce the accidents. The small trap fishing vessel winding device proposed herein can increase the winding force by strengthening the rope contact area and friction coefficient via an appropriate contact angle between the driving roller of the winding device and the rope. When the contact angles between the driving roller and the rope are 1°, 5°, 9°, 14° and 19°, the rope tension showed a difference according to each contact angle. When the contact angle is 9°, the rope tension is the highest at 392.62 kgf. Based on these experimental results, a prototype winding device is manufactured, and 25 traps are installed on a rope with a total length of 100 m at 4 m intervals in the sea, and then the rope tension is measured during trap pulling. As a result, the rope tension increases rapidly at the initial stage of trap pulling and shows the highest value of 31.89 kgf, which subsequently decreases significantly. Therefore, it is appropriate to design the winding force of a small trap fishing vessel winding device based on the maximum tension value of the rope specified at the beginning of the trap pulling operation.

• Journal of Food Hygiene and Safety
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• v.37 no.3
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• pp.136-142
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• 2022

The research aims to develop a rapid and easy analytical method for methoprene using liquid chromatography-tandem mass spectrometry (LC-MS/MS). A simple, highly sensitive, and specific analytical method for the determination of methoprene in livestock products (beef, pork, chicken, milk, eggs, and fat) was developed. Methoprene was effectively extracted with 1% acetic acid in acetonitrile and acetone (1:1), followed by the addition of anhydrous magnesium sulfate (MgSO₄) and anhydrous sodium acetate. Subsequently, the lipids in the livestock sample were extracted by freezing them at -20℃. The extracts were cleaned using MgSO₄, primary secondary amine (PSA), and octadecyl (C₁₈), which were then centrifuged to separate the supernatant. Nitrogen gas was used to evaporate the supernatant, which was then dissolved in methanol. The matrix-matched calibration curves were constructed using 8 levels (1, 2.5, 5, 10, 25, 50, 100, 150 ng/mL) and the coefficient of determination (R²) was above 0.9964. Average recoveries spiked at three levels (0.01, 0.1, and 0.5 mg/kg), and ranged from 79.5-105.1%, with relative standard deviations (RSDs) smaller than 14.2%, as required by the Codex guideline (CODEX CAC/GL 40). This study could be useful for residue safety management in livestock products.

• Journal of the Korean earth science society
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• v.43 no.3
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• pp.367-385
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• 2022

This study evaluates the temperature and precipitation results in East Asia simulated from the Hadley Centre Global Environmental Model version 3 regional climate model (HadGEM3-RA) developed by the UK Met Office. The HadGEM3-RA is conducted in the Coordinated Regional climate Downscaling Experiment-East Asia (CORDEX-EA) Phase II domain for 15 year (2000-2014). The spatial distribution of rainbands produced from the HadGEM3-RA by the summer monsoon is in good agreement with the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of water resources (APRODITE) data over the East Asia. But, precipitation amount is overestimated in Southeast Asia and underestimated over the Korean Peninsula. In particular, the simulated summer rainfall and APRODITE data show the least correlation coefficient and the maximum value of root mean square error in South Korea. Prediction of temperature in Southeast Asia shows underestimation with a maximum error during winter season, while it appears the largest underestimation in South Korea during spring season. In order to evaluate local predictability, the time series of temperature and precipitation compared to the ASOS data of the Seoul Meteorological Station is similar to the spatial average verification results in which the summer precipitation and winter temperature underestimate. Especially, the underestimation of the rainfall increases when the amounts of precipitation increase in summer. The winter temperature tends to underestimate at low temperature, while it overestimates at high temperature. The results of the extreme climate index comparison show that heat wave is overestimated and heavy rainfall is underestimated. The HadGEM3-RA simulated with a horizontal resolution of 25 km shows limitations in the prediction of mesoscale convective system and topographic precipitation. This study indicates that improvement of initial data, horizontal resolution, and physical process are necessary to improve predictability of regional climate model.