• Journal of Ocean Engineering and Technology
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• v.17 no.4
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• pp.23-30
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• 2003

This study investigates the seasonal variation and spatial distribution characteristics of pollutant load, as executing the quality valuation of pollutant load inflowing into Yeong-il Bay from on-land including the Hyeong-san River. Annual total pollutant generating rate from Yeong-il Bay region are 202ton-BOD/day, 620ton-SS/day, 42ton-TN/day, and 16ton-TP/day, respectively. Particularly, the generating ration of the pollutant loads from the Hyeong-san River is greater than that of any other watershed of the Yeong-il Bay, of which BOd is about 78.2％, SS 88.5％, T-N 62.5％, T-P 73.1％, As calculating Tank model with input value of daily precipitation and evaporation of 2001 year in drainage basin of the Hyeong-san River, the estimated result of the annual river discharge effluence from this river is 830106㎥, As a result to estimating annual effluence rate outflowing at the rivers from each drainage basin. annual inflow pollutant rates are 10,633ton-BOD/year, 19,302ton-SS/year, 15,369ton-TN/year, 305ton-TP/year, respectively. The population congestion region of the Pohang-city is a greater source of pollutant loads than the Neang-Chun region with wide drainage area. Therefore, the quantity of TN inflowing into Yeong-il Bay is much more than T-P. The accumulation of pollutant load effluenced from on-land will happen at the inner coast region of Yeon-il Bay. Finally, We would make a prediction that the water quality will take a bad turn.

• Korean journal of food and cookery science
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• v.15 no.2
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• pp.127-132
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• 1999

Dried noodles (somyon) were stored for 7 months at 25, 35 and 45$^{\circ}C$, and changes of water activity, amylograms and color of dried noodle at 4 week intervals were comparatively analyzed. The water activities during storage period were 0.43∼0.56 at all storage temperature. The breakdown of dried noodle by RVA(rapid visco analyser) increased as storage period increased. Color difference ($\Delta$E) was chosen for quality index due to the highest correlation coefficient between sensory score and color difference. The shelf-life of dried noodle was estimated from change of color, which was linearly increased as the storage period increased. The activation energy and Q$\sub$10/ value for color difference were 75.21 kJ/mol and 2.76 at 25$^{\circ}C$, respectively. Shelf-life of dried noodle at 25 were 27.9 months, respectively.

• Environmental Engineering Research
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• v.21 no.4
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• pp.333-340
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• 2016

An Artificial Neural Network including a Radial Basis Function (RBF) and a Time Delay Neural Network (TDNN) was used to predict total dissolved solid (TDS) in the river Zayanderud. Water quality parameters in the river for ten years, 2001-2010, were prepared from data monitored by the Isfahan Regional Water Authority. A factor analysis was applied to select the inputs of water quality parameters, which obtained total hardness, bicarbonate, chloride and calcium. Input data to the neural networks were pH, $Na^+$, $Mg^{2+}$, Carbonate ($CO{_3}^{-2}$), $HCO{_3}^{-1}$, $Cl^-$, $Ca^{2+}$ and Total hardness. For learning process 5-fold cross validation were applied. In the best situation, the TDNN contained 2 hidden layers of 15 neurons in each of the layers and the RBF had one hidden layer with 100 neurons. The Mean Squared Error and the Mean Bias Error for the TDNN during the training process were 0.0006 and 0.0603 and for the RBF neural network the mentioned errors were 0.0001 and 0.0006, respectively. In the RBF, the coefficient of determination ($R^2$) and the index of agreement (IA) between the observed data and predicted data were 0.997 and 0.999, respectively. In the TDNN, the $R^2$ and the IA between the actual and predicted data were 0.957 and 0.985, respectively. The results of sensitivity illustrated that $Ca^{2+}$ and $SO{_4}^{2-}$ parameters had the highest effect on the TDS prediction.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.334-334
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• 2022

Improvement of old-fashioned rain gauge systems for automatic, timely, continuous, and accurate precipitation observation is highly essential for weather/climate prediction and natural hazards early warning, since the occurrence frequency and intensity of heavy and extreme precipitation events (especially floods) are recently getting more increase and severe worldwide due to climate change. Although rain gauge accuracy of 0.1 mm is recommended by the World Meteorological Organization (WMO), the traditional rain gauges in both weighting and tipping bucket types are often unable to meet that demand due to several existing technical limitations together with higher production and maintenance costs. Therefore, we aim to introduce a newly developed and cost-effective hybrid rain gauge system at 0.1 mm accuracy that combines advantages of weighting and tipping bucket types for continuous, automatic, and accurate precipitation observation, where the errors from long-term load cells and external environmental sources (e.g., winds) can be removed via an automatic drainage system and artificial intelligence-based data quality control procedure. Our rain gauge system consists of an instrument unit for measuring precipitation, a communication unit for transmitting and receiving measured precipitation signals, and a database unit for storing, processing, and analyzing precipitation data. This newly developed rain gauge was designed according to the weather instrument criteria, where precipitation amounts filled into the tipping bucket are measured considering the receiver's diameter, the maximum measurement of precipitation, drainage time, and the conductivity marking. Moreover, it is also designed to transmit the measured precipitation data stored in the PCB through RS232, RS485, and TCP/IP, together with connecting to the data logger to enable data collection and analysis based on user needs. Preliminary results from a comparison with an existing 1.0-mm tipping bucket rain gauge indicated that our developed rain gauge has an excellent performance in continuous precipitation observation with higher measurement accuracy, more correct precipitation days observed (120 days), and a lower error of roughly 27 mm occurred during the measurement period.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.3B
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• pp.221-230
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• 2009

In Korea, many water intake plants are easily affected by effluents of sewage treatment plants because sewage treatment plants are usually located upstream or nearby the plants of the same riverine area. Furthermore, the inflow of harmful contaminants owing to pollutant spills or transportation accidents of vehicles using the roads and bridges intersecting the river causes significant impact on the management of water intake plants. Paldang lake, the main water intake plants in Korea, is especially exposed to various water pollution accidents, because the drainage basin area is significantly large compared to the water surface area of the lake. Therefore it is necessary to predict the possible pollutant spill in advance and consider measurements in case of water pollution. In this study, water quality prediction was performed in Paldang Lake in Korea durig the dry season using two-dimensional numerical models. In order to represent the cases of pollutant accidents, the difference of pollutant transport patterns with varying injection points was analyzed. Numerical simulations for hydrodynamics of water flow and water quality predictions were performed using RMA-2 and RAM4 respectively. As a result of simulation, the difference of pollutant transport with the injection points was analyzed. As a countermeasure against the pollutant accident, the augmentation of the flow rate is proposed. In comparison with the present state, the rapid dilution and flushing effects on the pollutant cloud could be expected with increase of flow rate. Thus, increase of flow rate can be used for operation of water intake plants in case of pollutant spill accidents.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.41-41
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• 2018

하천과 저수지의 수질을 예측하고 관리하는데 수리 수질예측모형이 널리 활용되고 있다. 수질예측모형은 유역이나 수체 내의 오염물질 이동경로나 농도를 수치해석 방법으로 계산하여 사용자가 필요로 하는 지점과 시점에서의 수질자료 생산하는데 활용되고 있다. 수질예측모형은 검 보정을 통해 정확도를 확보하며, 정확도의 확보를 위해서는 높은 수준의 전문성을 필요로 한다. 특히 시행착오법으로 모형을 보정하는 경우 많은 시간과 노력을 필요로 하게 되며, 보정계수를 과대 혹은 과소로 모형에 적용하는 오류를 범하기 쉽고 모델러의 주관이 관여되기 쉽다. 그래서 본 연구에서는 CE-QUAL-W2모형의 조류항목에 대한 모형 보정을 위하여 Chl-a와 남조류세포수에서 주로 활용되고 있는 보정계수에 대한 민감도 분석 결과를 토대로 매개변수별 모의결과 변화율을 산정하였으며, 시기적 경향성을 재현하기 위해 Ensemble-Bagging 기법과 머신 러닝 기법을 적용하여 모형 구동횟수를 최소화 할 수 있는 방법으로 구성하였다. Chl-a를 보정하기 위한 매개변수는 9개를 선정하였으며, 규조류, 남조류, 녹조류에 총 27개 매개 변수를 민감도 분석으로 도출 한 후 예상 변화율 대비 이벤트별 모의치와 실측치 간 %difference가 유사하도록 매개변수를 조정하였다. 또한 각 이벤트 조합의 매개변수 빈도수와 매개변수별 예상변화율, 시기적 조류특성을 고려하여 가중치를 도출하였으며, 1회 보정에 맞춰 Chl-a 모델 실행결과를 %difference로 평가한 후 "good"등급을 만족할 때까지 반복 적용하였다. 남조류세포수의 경우 Chl-a에 맞춰 매개변수 최적화 이후 남조류세포수 농도를 세포수로 환산하기 위한 CACEL에 대해 머신러닝 기법을 적용하였으며, CACEL 추정변화율 회귀식에 따라 평가 한 후 %difference "good"등급 이상을 만족할 때까지 반복 수행하는 방법을 적용하였다. 본 연구에서는 수질예측모형의 정확도를 확보하기 위하여 최적화 기법을 적용하였으며, 이를 통해 모형을 보정하는 과정에서 요구되는 시간과 노력을 줄일 수 있도록 하였으며, Ensemble기법과 머신러닝 기법을 적용하여 모형보정계수 적용에 객관성을 확보할 수 있도록 하였다.

• Journal of The Geomorphological Association of Korea
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• v.23 no.1
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• pp.87-101
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• 2016

This study was conducted to develop a hydrological model of catchment water balance which is able to estimate irrigation return flows, so BROOK90-K (Kongju National University) was developed as a result of the study. BROOK90-K consists of three main modules. The first module was designed to simulate water balance for reservoir and its catchment. The second and third module was designed to simulate hydrological processes in rice paddy fields located on lower watershed and lower watershed excluding rice paddy fields. The models consider behavior of floodgate manager for estimating the storage of reservoir, and modules for water balance in lower watershed reflects agricultural factors, such as irrigation period and, complex sources of water supply, as well as irrigation methods. In this study, the models were applied on Guryangcheon stream watershed. R2, Nash-Sutcliffe efficiency (NS), NS-log1p, and root mean square error between simulated and observed discharge were 0.79, 0.79, 0.69, and 4.27 mm/d respectively in the model calibration period (2001~2003). Furthermore, the model efficiencies were 0.91, 0.91, 0.73, and 2.38 mm/d respectively over the model validation period (2004~2006). In the future, the developed BROOK90-K is expected to be utilized for various modeling studies, such as the prediction of water demand, water quality environment analysis, and the development of algorithms for effective management of reservoir.

• Journal of the Korean GEO-environmental Society
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• v.3 no.4
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• pp.19-27
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• 2002

Prediction of exact soil loss yield has as important engineering meaning as prediction of exact flow measurement in a stream. The quantity of soil loss in a stream should be considered in planning and management of water resources and water quality such as design and maintenace of hydraulic structures : dams, weirs and seawalls, channel improvement, channel stabilization, flood control, design and operation of reservoirs and design of harbors. In this study, the soil loss of Sap-gyo reservoir watershed is simulated and estimated by RUSLE model which is generally used in the estimation of soil loss. The parameters of RUSLE model are selected and estimated using slope map, landuse map and soil map by GIS. These parameters are applied to RUSLE's estimating program. And soil loss under probability rainfall in different frequencies are estimated by recent 30 years of rainfall data of Sap-gyo reservoir watershed.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1275-1284
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• 2021

This paper presents a machine learning technique applied to prediction of time-dependent local scour around bridge piers in both non-cohesive and cohesive beds. The support vector machines (SVM), which is known to be free from overfitting, is used. The time-dependent scour depths are expressed by 7 and 9 variables for the non-cohesive and cohesive beds, respectively. The SVM models are trained and validated with time series data from different sources of experiments. Resulting Mean Absolute Percentage Error (MAPE) indicates that the models are trained and validated properly. Comparisons are made with the results from Choi and Choi's formula and Scour Rate in Cohesive Soils (SRICOS) method by Briaud et al., as well as measured data. This study reveals that the SVM is capable of predicting time-dependent local scour in both non-cohesive and cohesive beds under the condition that sufficient data of good quality are provided.

• Korean Journal of Food Science and Technology
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• v.31 no.6
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• pp.1557-1562
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• 1999

As a series of studies on the preservation methods for boiled-dried anchovies, determination of sorption properties and shelf-life prediction were made for the samples. Dried anchovies, which were gamma-irradiated at pre-established dose (5 kGy) after packaging in both a polyethylene film (PE, 0.1 mm) and a laminated film $(nylon\;15\;{\mu}m/polyethylene\;100\;{\mu}m,\;NY/PE)$, were subjected to a quality evaluation during 4 months at different storage conditions, such as $15^{\circ}C/68%\;RH,\;25{\circ}C/75%\;RH,\;and\;35^{\circ}C/84%$ RH. The sample showed 5.47% of BET monomolecular layer moisture content and the corresponding water activity, 0.15. The velocity constants of browning reaction and organoleptic changes in the sample were in proportion to storage temperature, and $Q_{10}$, values were ranged from 2.17 to 2.40 in a given packaging and irradiation conditions. In the shelf-life prediction of the stored sample at $25^{\circ}C$, non-irradiated groups packaged in PE and NY/PE were 84 days and 125 days. While 5 kGy-irradiated groups in the same packaging were 126 days and 138 days, respectively. This finding proved the efficacy of laminated-film packaging and irradiation treatment in preserving the quality of dried anchovies.