• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.180-180
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• 2021

가로수는 「도로법」 제11조에 따른 도로(고속국도를 제외한다)와 보행자전용도로 및 자전거전용도로 등 대통령령으로 정하는 도로의 도로구역 안 또는 그 주변지역에 심는 수목을 말하며, 도시의 가로수는 기후조절효과 및 대기오염 정화효과 등을 가질 뿐 아니라 도심지 내에 녹색을 도입하고 도시경관을 구성하는 주요 요소이다. 전국 각 시도에서는 가로수 조성사업을 지속적으로 추진하고 있다. 하지만 몇몇 도시에서는 적절하지 않은 가로수 관리로 인해 가로수가 말라죽는 현상이 증가하고 있다. 이에 가로수 고사 현상을 감소시키기 위하여 토양수분과 토양온도를 측정하여 가로수 피해와 연관성을 조사할 필요성이 있다고 판단하였다. 본 연구는 춘천시에서 진행하였으며, 일반 가로수와 현재 가로수 고사로 문제가 되고 있는 3 모니터링 지점을 선정하고, 토양수분 센서를 5, 15, 40 cm 깊이에 설치하였다. 센서를 이용하여 토양수분과 지온, EC 모니터링을 실시하였다. 토양수분 모니터링 자료를 활용하여 토층별 토양수분 소비량 산정을 하고, 현장 토양시료를 채취하여 물리·화학적 특성을 분석하였다. 또한 가로수 증발산량 산정 및 토층별 토양수분 소비량과 소비패턴을 비교하였다. 본 연구 결과를 향후 RZWQM(Root Zone Water Quality Model) 모델의 기초자료 및 시나리오 구성에 활용될 수 있으며, 모니터링 및 모델링 결과를 활용하여 가로수 및 도시 표토 기능 위협 요인을 분석에 활용 될 수 있다.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.489-489
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• 2022

도시의 가로수들이 열악한 부지 조건과 적절하지 않은 가로수 관리로 인해 죽는 현상이 몇몇 도시에서 발생하고 있다. 열악한 부지 조건과 적절하지 않은 가로수 관리에는 생물학적·기상학적으로 많은 요소들이 있고, 그 밖에 도시 설계로 인한 요인들로 다양하다. 그중 연구지역인 춘천시에서는 가로수가 죽는 원인 중 토양수분이 가장 큰 원인일 것이라고 판단하였다. 토양수분 분포의 시간적 공간적 특성들은 증발, 침투, 지하수 함량, 토양 침식, 식생 분포 등을 지배하는 중요한 요소이며, 토양수분 연구는 물순환과정의 특성을 이해하는데 있어서 필수적인 과정이다. 하지만 토양수분 분석은 중요성에 비해 활발한 연구가 이루어지지 않고 있으며, 특히 가로수 토양수분에 대해서는 연구가 없는 실정이다. 따라서 가로수 토양수분 모니터링을 실시하였고, 장기적인 가로수 관리를 위해 모델링을 하였다. 모델링 기초자료 확보를 위한 토양수분 모니터링은 춘천시의 가로수 중 세 군데를 선정해 각각 10, 20, 30 cm에 센서를 설치하였다. 이를 통해 약 1년간의 토양수분 함량 데이터를 수집하였고, 모니터링 지점의 토양을 샘플링 후 분석하여 물리, 화학, 생물성 데이터를 수집하였다. 모델링은 RZWQM(Root Zone Water Quality Model)을 이용하여 시나리오를 구성하였다. 모델링 결과를 활용해 가로수 및 도시 표토 기능을 위협하는 요인을 분석하였다.

• Journal of Korea Water Resources Association
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• v.56 no.8
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• pp.509-520
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• 2023

Various drought indices are widely used for assessing drought conditions which are affected by many factors such as precipitation, soil moisture, and runoff. The values of drought indices varies depending on hydro-meteorological data and calculation formulas, and the judgment of the drought condition may also vary. This study selected four calculation components such as precipitation data length, accumulation period, probability distribution function, and parameter estimation method as the sources of uncertainty in the calculation of standardized precipitation index (SPI), and evaluated their contributions to the uncertainty using root mean square error (RMSE) and linear mixed model (LMM). The RMSE estimated the overall errors in the SPI calculation, and the LMM was used to quantify the uncertainty contribution of each factor. The results showed that as the accumulation period increased and the data period extended, the RMSEs decreased. The comparison of relative uncertainty using LMM indicated that the sample size had the greatest impact on the SPI calculation. In addition, as sample size increased, the relative uncertainty related to the sample size used for SPI calculation decreased and the relative uncertainty associated with accumulation period and parameter estimation increased. In conclusion, to reduce the uncertainty in the SPI calculation, it is essential to collect long-term data first, followed by the appropriate selection of probability distribution models and parameter estimation methods that represent well the data characteristics.

• Korean Journal of Soil Science and Fertilizer
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• v.37 no.4
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• pp.259-265
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• 2004

A quicker method was developed for foliar analysis in diagnosis of nitrogen in apple trees based on multivariate calibration procedure using partial least squares regression (PLSR) and principal component regression (PCR) to establish the relationship between reflectance spectra in the near infrared region and nitrogen content of fresh- and dry-leaf. Several spectral pre-processing methods such as smoothing, mean normalization, multiplicative scatter correction (MSC) and derivatives were used to improve the robustness and performance of the calibration models. Norris first derivative with a seven point segment and a gap of six points on MSC gave the best result of partial least squares-1 PLS-1) model for dry-leaf samples with root mean square error of prediction (RMSEP) equal to $0.699g\;kg^{-1}$, and that the Savitzky-Golay first derivate with a seven point convolution and a quadratic polynomial on MSC gave the best results of PLS-1 model for fresh-samples with RMSEP of $1.202g\;kg^{-1}$. The best PCR model was obtained with Savitzky-Golay first derivative using a seven point convolution and a quadratic polynomial on mean normalization for dry leaf samples with RMSEP of $0.553g\;kg^{-1}$, and obtained with the Savitzky-Golay first derivate using a seven point convolution and a quadratic polynomial for fresh samples with RMSEP of $1.047g\;kg^{-1}$. The results indicate that nitrogen can be determined by the near infrared reflectance (NIR) technology for fresh- and dry-leaf of apple.

• Journal of Korean Society of Forest Science
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• v.112 no.4
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• pp.426-441
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• 2023

This study investigated the predictive accuracy of a model of landslide displacement in Jecheon-si, where a great number of landslides were triggered by heavy rain on both natural (non-clear-cut) and clear-cut slopes during August 2020. This was accomplished by applying three flow direction methods (single flow direction, SFD; multiple flow direction, MFD; infinite flow direction, IFD) and the degree of root cohesion to an infinite slope stability equation. The application assumed that the soil saturation and any changes in root cohesion occurred following the timber harvest (clear-cutting). In the study area, 830 landslide locations were identified via landslide inventory mapping from satellite images and 25 cm resolution aerial photographs. The results of the landslide modeling comparison showed the accuracy of the models that considered changes in the root cohesion following clear-cutting to be improved by 1.3% to 2.6% when compared with those not considered in the area under the receiver operating characteristics (AUROC) analysis. Furthermore, the accuracy of the models that used the MFD algorithm improved by up to 1.3% when compared with the models that used the other algorithms in the AUROC analysis. These results suggest that the discriminatory application of the root cohesion, which considers changes in the vegetation condition, and the selection of the flow direction method may influence the accuracy of landslide predictive modeling. In the future, the results of this study should be verified by examining the root cohesion and its dynamic changes according to the tree species using the field hydrological monitoring technique.

• Atmosphere
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• v.26 no.1
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• pp.111-126
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• 2016

In the numerical weather model, surface properties can be defined by various parameters such as terrain height, landuse, surface albedo, soil moisture, surface emissivity, roughness length and so on. And these parameters need to be improved in the Seoul metropolitan area that established high-rise and complex buildings by urbanization at a recent time. The surface roughness length map is developed from digital elevation model (DEM) and it is implemented to the high-resolution numerical weather (WISE-WRF) model. Simulated results from WISE-WRF model are analyzed the relationship between meteorological variables to changes in the surface roughness length. Friction speed and wind speed are improved with various surface roughness in urban, these variables affected to temperature and relative humidity and hence the surface roughness length will affect to the precipitation and Planetary Boundary Layer (PBL) height. When surface variables by the WISE-WRF model are validated with Automatic Weather System (AWS) observations, NEW experiment is able to simulate more accurate than ORG experiment in temperature and wind speed. Especially, wind speed is overestimated over $2.5m\;s^{-1}$ on some AWS stations in Seoul and surrounding area but it improved with positive correlation and Root Mean Square Error (RMSE) below $2.5m\;s^{-1}$ in whole area. There are close relationship between surface roughness length and wind speed, and the change of surface variables lead to the change of location and duration of precipitation. As a result, the accuracy of WISE-WRF model is improved with the new surface roughness length retrieved from DEM, and its surface roughness length is important role in the high-resolution WISE-WRF model. By the way, the result in this study need various validation from retrieved the surface roughness length to numerical weather model simulations with observation data.

• Geomechanics and Engineering
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• v.31 no.4
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• pp.339-352
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• 2022

Studying slope stability is an important branch of civil engineering. In this way, engineers have employed machine learning models, due to their high efficiency in complex calculations. This paper examines the robustness of various novel optimization schemes, namely equilibrium optimizer (EO), Harris hawks optimization (HHO), water cycle algorithm (WCA), biogeography-based optimization (BBO), dragonfly algorithm (DA), grey wolf optimization (GWO), and teaching learning-based optimization (TLBO) for enhancing the performance of adaptive neuro-fuzzy inference system (ANFIS) in slope stability prediction. The hybrid models estimate the factor of safety (F_S) of a cohesive soil-footing system. The role of these algorithms lies in finding the optimal parameters of the membership function in the fuzzy system. By examining the convergence proceeding of the proposed hybrids, the best population sizes are selected, and the corresponding results are compared to the typical ANFIS. Accuracy assessments via root mean square error, mean absolute error, mean absolute percentage error, and Pearson correlation coefficient showed that all models can reliably understand and reproduce the F_S behavior. Moreover, applying the WCA, EO, GWO, and TLBO resulted in reducing both learning and prediction error of the ANFIS. Also, an efficiency comparison demonstrated the WCA-ANFIS as the most accurate hybrid, while the GWO-ANFIS was the fastest promising model. Overall, the findings of this research professed the suitability of improved intelligent models for practical slope stability evaluations.

• Journal of Korea Water Resources Association
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• v.53 no.6
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• pp.395-408
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• 2020

This paper aims to develop and apply three different machine learning approaches (i.e., artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and wavelet-based neural networks (WNN)) combined with an evolutionary optimization algorithm and the k-fold cross validation for multi-step (days) streamflow forecasting at the catchment located in Algeria, North Africa. The ANN and ANFIS models yielded similar performances, based on four different statistical indices (i.e., root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and peak flow criteria (PFC)) for training and testing phases. The values of RMSE and PFC for the WNN model (e.g., RMSE = 8.590 ㎥/sec, PFC = 0.252 for (t+1) day, testing phase) were lower than those of ANN (e.g., RMSE = 19.120 ㎥/sec, PFC = 0.446 for (t+1) day, testing phase) and ANFIS (e.g., RMSE = 18.520 ㎥/sec, PFC = 0.444 for (t+1) day, testing phase) models, while the values of NSE and R for WNN model were higher than those of ANNs and ANFIS models. Therefore, the new approach can be a robust tool for multi-step (days) streamflow forecasting in the Seybous River, Algeria.

• Korean Journal of Plant Resources
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• v.27 no.5
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• pp.546-555
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• 2014

The final aim of this study is to develop a biofiltration system integrated with plant vegetation for improving indoor air quality effectively depending on indoor space and characteristics. However, to approach this final goal, several requirements such as constant pressure drops (PDs) and soil moisture contents (SMCs), which influence the capacity design for a proper ventilation rate of biofiltration system, should be satisfied. Thus, this fundamental experiment was carried out to adjust a proper wind speed and to ensure a stabilization of initial SMCs within biofilter for uniform distribution of SMCs and PDs, and for normal plant growth, especially avoiding root stress by wind. Therefore, we designed horizontal biofliter models and manufactured them, and then calculated the ventilation rate, air residence time, and air-liquid ration based on the biofilter depending on three levels of wind speed (1, 2, and $3cm{\cdot}s^{-1}$). The relative humidity (RH) and PD of the humidified air coming out through the soil within the biofilter, and SMC of the soil and plant growth parameters of lettuce and duffy fern grown within biofilter were measured depending on the three levels of wind speed. As a result of wind speed test, $3{\cdot}sec^{-1}$ was suitable to keep up a proper RH, SMC, and plant growth. Thus, the next experiment was set up to be two levels of initial SMCs (low and high initial SMC, 18.5 and 28.7%) within each biofilter operated and a non-biofiltered control (initial SMC, 29.7%) on the same wind speed ($3cm{\cdot}sec^{-1}$), and measured on the RH and PD of the air coming out through the soil within the biofilter, and SMC of the soil and plant growth parameters of Humata tyermani grown within biofilter. This result was similar to the first results on RHs, SMCs, and PDs keeping up with constant levels, and three SMCs did not show any significant difference on plant growth parameters. However, two biofiltered SMCs enhanced dry weights of the plants slightly than non-biofiltered SMC. Thus, the stability of this biofiler system keeping up major physical factors (SMC and PD) deserved to be adopted for designing an advanced integrated biofilter model in the near future.

• Journal of Radiation Protection and Research
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• v.18 no.1
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• pp.9-24
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• 1993

The time-dependent radioecological model applicable to Korean environment has been developed in order to assess the radiological consequences following the short-term deposition of radionuclides in an accident of nuclear power plant. Time-dependent radioactivity concentrations in foodstuffs can be estimated by the model called 'KORFOOD' as well as time-dependent and time-integrated ingestion doses. Three kinds of critical radionuclides and thirteen kinds of foodstuffs were considered in this model. Dynamic variation of radioactivities were simulated by considering several effects such as deposition, weathering and washout, resuspension, root uptake, translocation, leaching, senescence, intake and excretion of soil by animals, intake and excretion of feedstuffs by animals, etc. The input data to the KORFOOD are the time of the year when the deposition occurs, the kinds of radionuclides and foodstuffs for estimation. The time-dependent specific activities in rice and the ingestion doses due to the consumption of all considered foodstuffs were calculated with deposition time using agricultural data-base in Kori region. In order to validate results of KORFOOD, the calculated results were compared with those by a leading German model, ECOSYS-87. The comparison of results shows good agreements within a factor of ten.