• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2005년도 학술발표회 논문집
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• pp.644-648
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• 2005

본 연구의 목적은 새만금 유역의 복잡한 용$\cdot$배수체계와 다양한 하천의 유출체계를 반영할 수 있는 유역 물수지모형을 구축하여 합리적인 유출량 추정을 위하여 새만금 상류유역의 신태인수위표 소유역을 대상으로 유역물수지모형의 적용성을 검토하고 일별 유출량을 모의하였다. 유역물수지모형을 이용하여 대상유역에서 모형의 보정기간인 1998년의 유출량을 모의한 결과, RMSE는 2.64mm/day, RMAE는 0.24mm/day, 그리고 결정계수($R^2$)는 0.91로 모의되었으며, 모형의 검정기간인 2003년의 유출량을 모의한 결과, RMSE는 3.53mm/day, RMAE는 0.35mm/day, 그리고 결정계수($R^2$)는 0.83로 모의되었다.

• 한국농공학회:학술대회논문집
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• 한국농공학회 2003년도 학술발표논문집
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• pp.535-538
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• 2003

A Grey model was developed to forecast short-term runoff from the Naju watershed in Korea. In calibration, the root mean square error(RMSE) of the simulated runoff of six hours ahead using Grey model ranged from 6.3 to $290.52m^3/s,\;R^2$ ranged from 0.91 to 0.99, compared to the observed data. In verification, the RMSE ranged from 75.7 to $218.9m^3/s,\;R^2$ ranged from 0.87 to 0.96, compared to the observed data. The results in this study demonstrate that the proposed model can reasonably forecast runoff one to six hours ahead.

• 한국농공학회:학술대회논문집
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• 한국농공학회 2005년도 학술발표논문집
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• pp.673-678
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• 2005

The objective of this paper is to develop the microorganism concentration simulation model for the health related effect analysis while farmers and water managers reuse the wastewater for agricultural irrigation. This model consists of the CE-QUAL-R1 model and the CREAMS-PADDY model. The CE-QUAL-R1 model is the 1-D numerical model to analyze the water quality of the reservoir and the CREAMS-PADDY model is modified from CREAMS model for considering the hydrologic cycles in paddy field. This model was applied to examine the application by the observed data from 2003 in Byoungjum study area. From this research, the average root mean square error (RMSE) for the simulated concentration during the calibration period was 0.51 MPN/100ml and correlation coefficient $(R^2)$ was 0.71. And the RMSE for the simulated concentration during the verification period was 0.46 MPN/100ml and $R^2$ was 0.73. This simulation results show that the coliform inflow concentrations by the wastewater irrigation wield great influence upon the temporal coliform concentrations in paddy field.

• Journal of Biosystems Engineering
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• 제36권2호
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• pp.96-108
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• 2011

Several methods of in-field measurements of Nitrogen and other soil properties using cores extracted by a hydraulic soil sampler were evaluated. A prototype core scanner was built to accommodate Veris Technologies commercial Vis-NIRS equipment. The testing result for pH, P and Mg were close to RPD (Ratio of Prediction to Deviation = Standard deviation/RMSE) of 2, however the scanner could not achieve the goal of RPD of 2 on some other properties, especially on nitrate nitrogen ($NO_3$) and potassium (K). In situ NIRS/EC probe showed similar results to the core scanner; pH, P and Mg were close to RPD of 2, while $NO_3$ and K were RPD of 1.5 and 1.2, respectively. Correlations between estimations using the probe and the core scanner were strong, with $r^2$ > 0.7 for P, Mg, Total N, Total C and CEC. Preliminary results for mid-IR spectroscopy showed an $r^2$ of 0.068 and an RMSE for nitrate (N) of 18 ppm, even after the removal of calcareous samples and possible N outlier. After removal of calcareous samples on a larger sample set, results improved considerably with an $r^2$ of 0.64 and RMSE of 6 ppm. However, this was only possible after carbonate samples were detected and eliminated, which would not be feasible under in-field measurements. Testing of $NO_3$ and K ion-selective electrodes (ISEs) revealed promising results, with acceptable errors measuring soil solutions containing nitrate and potassium levels that are typical of production agriculture fields.

• 수산해양기술연구
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• 제49권1호
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• pp.18-28
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• 2013

It was compared the estimated parameters by the surplus production from three different models, i.e., three types (Schaefer, Gulland, and Schnute) of the traditional surplus production models, a stock production model incorporating covariates (ASPIC) model and a maximum entropy (ME) model. We also evaluated the performance of models in the estimation of their parameters. The maximum sustainable yield (MSY) of small yellow croaker (Pseudosciaena polyactis) in Korean waters ranged from 35,061 metric tons (mt) by Gulland model to 44,844mt by ME model, and fishing effort at MSY ($f_{MSY}$) ranged from 262,188hauls by Schnute model to 355,200hauls by ME model. The lowest root mean square error (RMSE) for small yellow croaker was obtained from the Gulland surplus production model, while the highest RMSE was from Schnute model. However, the highest coefficient of determination ($R^2$) was from the ME model, but the ASPIC model yielded the lowest coefficient. On the other hand, the MSY of Kapenta (Limnothrissa miodon) ranged from 16,880 mt by ASPIC model to 25,373mt by ME model, and $f_{MSY}$, from 94,580hauls by ASPIC model to 225,490hauls by Schnute model. In this case, both the lowest root mean square error (RMSE) and the highest coefficient of determination ($R^2$) were obtained from the ME model, which showed relatively better fits of data to the model, indicating that the ME model is statistically more stable and robust than other models. Moreover, the ME model could provide additional ecologically useful parameters such as, biomass at MSY ($B_{MSY}$), carrying capacity of the population (K), catchability coefficient (q) and the intrinsic rate of population growth (r).

• Journal of Biosystems Engineering
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• 제42권3호
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• pp.190-198
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• 2017

Purpose: A thin-layer drying equation was developed to analyze the drying processes of soybeans (white and black beans) and investigate drying conditions by verifying the suitability of existing grain drying equations. Methods: The drying rates of domestic soybeans were measured in a drying experiment using air at a constant temperature and humidity. The drying rate of soybeans was measured at two temperatures, 50 and $60^{\circ}C$, and three relative humidities, 30, 40 and 50%. Experimental constants were determined for the selected thin layer drying models (Lewis, Page, Thompson, and moisture diffusion models), which are widely used for predicting the moisture contents of grains, and the suitability of these models was compared. The suitability of each of the four drying equations was verified using their predicted values for white beans as well as the determination coefficient ($R^2$) and the root mean square error (RMSE) of the experiment results. Results: It was found that the Thompson model was the most suitable for white beans with a $R^2$ of 0.97 or greater and RMSE of 0.0508 or less. The Thompson model was also found to be the most suitable for black beans, with a $R^2$ of 0.97 or greater and an RMSE of 0.0308 or less. Conclusions: The Thompson model was the most appropriate prediction drying model for white and black beans. Empirical constants for the Thompson model were developed in accordance with the conditions of drying temperature and relative humidity.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2017년도 학술발표회
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• pp.222-223
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• 2017

기후변화로 인한 태풍 및 집중호우의 발생빈도가 증가함에 따라 매년 많은 홍수피해가 발생하고 있다. 특히 제주도는 지리적 특성상 태풍의 길목에 위치하고 있어 집중호우, 돌발홍수 등과 같은 자연재해에 연중 노출되어 있으며, 이상기후로 인한 일강우량의 경신이 빈번하게 발생함에 따라 홍수피해 위험이 증가하고 있다. 홍수피해를 저감시키기 위해서는 정확한 홍수량 산정을 통한 하천기본계획 및 치수계획 수립이 매우 중요하다. 실무에서는 홍수량 산정 시 대부분 HEC-HMS 모형을 활용하고 있으나 본 연구에서는 기존 방법이 아닌 분포형 모형인 Vflo를 활용하여 제주도심하천의 홍수유출을 해석하였다. 도심하천인 외도천을 연구대상유역으로 선정하였으며 Arc-GIS를 이용하여 DEM, 토지피복도, 토양도 등 지형인자들을 $30m{\times}30m$ 격자크기로 나누어 매개변수로 구축하였다. 제주도는 강우관측소가 조밀하고 고르게 분포되어 있어 강우자료의 경우는 레이더영상 자료로부터 추출하여 G/R 기법을 적용하여 보정하였다. 2012년 7월 태풍 카눈은 RMSE 2.6954와 0.9115, 8월 집중호우는 RMSE 2.5703, $R^2$ 0.9202, 9월 태풍 산바는 RMSE 2.1569, $R^2$ 0.9842로 높은 상관관계를 보였다. 본 연구의 홍수량 산정 방법 정확도 비교를 위해 현장관측자료(FSIV)를 분석한 유출량과 비교 분석하였다. Vflo를 활용한 홍수량 산정 방법은 미계측 유역이 많은 제주도에서 효율적으로 활용될 수 있을 것으로 판단되며, 다양한 홍수량 산정 방법을 통하여 하천기본계획 및 유역종합치수계획 등 치수계획 수립 시 많은 활용이 될 것으로 기대한다.

• 농업과학연구
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• 제46권1호
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• pp.183-194
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• 2019

Adulteration of argan oil with some other cheaper oils with similar chemical compositions has resulted in increasing demands for authenticity assurance and quality control. Fast and simple analytical techniques are thus needed for authenticity analysis of high-priced argan oil. Raman spectroscopy is a potent technique and has been extensively used for quality control and safety determination for food products In this study, Raman spectroscopy in combination with a net analyte signal (NAS)-based methodology, i.e., hybrid linear analysis method developed by Goicoechea and Olivieri in 1999 (HLA/GO), was used to predict the different concentrations of olive oil (0 - 20%) added to argan oil. Raman spectra of 90 samples were collected in a spectral range of $400-400cm^{-1}$, and calibration and validation sets were designed to evaluate the performance of the multivariate method. The results revealed a high coefficient of determination ($R^2$) value of 0.98 and a low root-mean-square error (RMSE) value of 0.41% for the calibration set, and an $R^2$ of 0.97 and RMSE of 0.36% for the validation set. Additionally, the figures of merit such as sensitivity, selectivity, limit of detection, and limit of quantification were used for further validation. The high $R^2$ and low RMSE values validate the detection ability and accuracy of the developed method and demonstrate its potential for quantitative determination of oil adulteration.

• 한국농공학회논문집
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• 제63권1호
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• pp.11-25
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• 2021

The objective of this study was to analyze the impact of activation functions on flood forecasting model based on Artificial neural networks (ANNs). The traditional activation functions, the sigmoid and tanh functions, were compared with the functions which have been recently recommended for deep neural networks; the ReLU, leaky ReLU, and ELU functions. The flood forecasting model based on ANNs was designed to predict real-time runoff for 1 to 6-h lead time using the rainfall and runoff data of the past nine hours. The statistical measures such as R2, Nash-Sutcliffe Efficiency (NSE), Root Mean Squared Error (RMSE), the error of peak time (ETp), and the error of peak discharge (EQp) were used to evaluate the model accuracy. The tanh and ELU functions were most accurate with R2=0.97 and RMSE=30.1 (㎥/s) for 1-h lead time and R2=0.56 and RMSE=124.6~124.8 (㎥/s) for 6-h lead time. We also evaluated the learning speed by using the number of epochs that minimizes errors. The sigmoid function had the slowest learning speed due to the 'vanishing gradient problem' and the limited direction of weight update. The learning speed of the ELU function was 1.2 times faster than the tanh function. As a result, the ELU function most effectively improved the accuracy and speed of the ANNs model, so it was determined to be the best activation function for ANNs-based flood forecasting.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2020년도 학술발표회
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• pp.200-200
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• 2020

본 연구에서는 SWAT 모형과 APEX-Paddy 모형의 연계 모델링을 통한 대표 BMP(Best management practice) 적용, 정식시기 및 벼 생육기간을 고려한 시나리오 적용을 통해 농업용수의 관리 및 수질환경 개선 등에 활용할 수 있는 저영향 영농활동을 분석하고자 하였다. 만경강 유역을 대상으로 SWAT 모형을 구축하고 유역 내에 위치한 논 시험포장을 대상으로 강우-유출 및 비점오염원 모니터링 자료를 활용하여 APEX-Paddy 모형을 구축하였다. SWAT 모형과 APEX 모형을 연계하여 유역의 수문, 수질에 대한 정밀한 모델링을 수행하였으며, 이는 저영향 영농활동을 분석하기 위한 필드단위의 정확한 결과를 유역차원에 반영하기 위함이다. 특히, 본 연구에 사용된 APEX-Paddy 모형은 농촌진흥청과 Texas A&M의 공동연구를 통해 개발된 새로운 모형으로서 한국의 논 영농활동 및 담수환경을 반영하여 논에서의 유출 및 비점오염원을 모의할 수 있다. 연계 모형의 적합성 평가를 위해 R² (Determine of Coefficient), RMSE (Root mean square error), NSE (Nash-sutcliffe efficiency)를 사용하였다. 적합성 평가 지표를 분석한 결과, 유출량은 R² 평균 0.91, RMSE 평균 2.87 mm/day, NSE 평균 0.78로 나타났다. T-N 부하량은 R² 평균 0.74, RMSE 평균 59.3 kg/ha/day, NSE 평균 0.50으로 나타났다. 저영향 영농활동 관리방안을 위한 시나리오로 1) 논의 물꼬높이(BMP) 관리 적용, 2) 벼 생육기간 조절을 고려하여 기온변화에 따른 정식시기, 벼 생육기간 등을 조정하여 적용하였다. 기후변화 시나리오는 10개 GCM 모델의 RCP 8.5 시나리오를 통해 분석하였으며, 유역차원의 미래 영향을 분석한 결과, 물꼬관리 BMP에 따라 담수심이 증가되며, 관개량이 감소하고 유출량 10.7%, T-N 11.2% 저감되는 것을 나타냈으며, 벼 생육기간 조절은 BMP보다 상대적으로 효과가 높진 않았지만, 유출량 1.4%, T-N 3.1%의 저감효과를 나타냈다. 따라서 두 가지의 저영향 영농활동 관리방안은 미래기간의 기후변화에 대응하여 농업용수 및 물관리에 도움이 될 것으로 사료된다. 하지만 본 연구결과는 모델링 결과에 의존한 것이며, 추후 지속적인 연구와 보완이 필요하다.