• Korean Journal of Agricultural Science
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• v.37 no.1
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• pp.105-112
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• 2010

The objective of this study is to develop a standardized model for analyzing water balances in large scaled water basin by considering agricultural water districts, and to evaluate the hydrological feasibility of applying this model to several water districts such as Nonbul, Geumbok, Daejeon 1, Daejeon 2, and Cheonggang in Geum river basin. Ten types of stream network were considered in developed model. Using this model, streamflows were simulated by major stations and water balances were analyzed by water districts. Simulated streamflows and measured streamflows were compared at check stations such as Gapcheon and Bugang stations in which Nash and Schcliffe's model efficiencies were 0.633, 0.902, respectively. This results showed its applicabilities to national water resources plan, rural water development plan, and total maximum daily load plan in Korea.

• Journal of Korean Society on Water Environment
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• v.37 no.5
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• pp.335-343
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• 2021

Water quality prediction is essential for the proper management of water supply systems. Increased suspended sediment concentration (SSC) has various effects on water supply systems such as increased treatment cost and consequently, there have been various efforts to develop a model for predicting SSC. However, SSC is affected by both the natural and anthropogenic environment, making it challenging to predict SSC. Recently, advanced machine learning models have increasingly been used for water quality prediction. This study developed an ensemble machine learning model to predict SSC using the XGBoost (XGB) algorithm. The observed discharge (Q) and SSC in two fields monitoring stations were used to develop the model. The input variables were clustered in two groups with low and high ranges of Q using the k-means clustering algorithm. Then each group of data was separately used to optimize XGB (Model 1). The model performance was compared with that of the XGB model using the entire data (Model 2). The models were evaluated by mean squared error-ob servation standard deviation ratio (RSR) and root mean squared error. The RSR were 0.51 and 0.57 in the two monitoring stations for Model 2, respectively, while the model performance improved to RSR 0.46 and 0.55, respectively, for Model 1.

• Journal of Korean Society on Water Environment
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• v.30 no.1
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• pp.16-24
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• 2014

A combined watershed and receiving waterbody model was developed for operational water flow forecasting of the Nakdong river. The Hydrological Simulation Program Fortran (HSPF) was used for simulating the flow rates at major tributaries. To simulate the flow dynamics in the main stream, a three-dimensional hydrodynamic model, EFDC was used with the inputs derived from the HSPF simulation. The combined models were calibrated and verified using the data measured under different hydrometeological and hydraulic conditions. The model results were generally in good agreement with the field measurements in both calibration and verification. The 7-days forecasting performance of water flows in the Nakdong river was satisfying compared with model calibration results. The forecasting results suggested that the water flow forecasting errors were primarily attributed to the uncertainties of the models, numerical weather prediction, and water release at the hydraulic structures such as upstream dams and weirs. From the results, it is concluded that the combined watershed-waterbody model could successfully simulate the water flows in the Nakdong river. Also, it is suggested that integrating real-time data and information of dam/weir operation plans into model simulation would be essential to improve forecasting reliability.

• Magazine of the Korean Society of Agricultural Engineers
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• v.43 no.6
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• pp.174-186
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• 2001

Excessive outflow of pollutant loads resulting from rapid industrialization has unbalanced the water ecosystem, deteriorating the water quality environment severely. Therefore, measures for improving the water quality are necessary to maintain clean reservoir water and restore water-friendly spaces. A water quality model which is capable of simulating daily reservoir water quality was developed. The model had been applied to Masan reservoir and Wanggung reservoir in Korea. The model appeared to be satisfactory in representing the trend of water quality variations by comparing measured and simulated results. The model had been also applied to assess water purification techniques such as dredged pool, floating island and vegetation purification system. The model was considered to assess the effect of water purification techniques on reservoir water quality improvement. The results of water quality simulation for lake water purification techniques showed that a large facility would be needed to meet the targeted water quality of the reservoir when only one technique is applied. To effectively improve the quality of the polluted reservoir water, it is therefore recommended that pollutant sources should first be controlled, and a combination of the water purification techniques applied to make the utmost use of their secondary effects such as conservation of the reservoir volume capacity, establishment of a recreation space, promotion of bio-diversity, and improvement of the lake landscape.

• Journal of Environmental Impact Assessment
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• v.14 no.4
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• pp.157-164
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• 2005

Existing models that predict of Daily water supply include statistical models and neural network model. The neural network model was more effective than the statistical models. Only neural network model, which predict of Daily water supply, is focused on estimation of the operational control. Neural network model takes long learning time and gets into local minimum. This study proposes Neuro Genetic hybrid model which a combination of genetic algorithm and neural network. Hybrid model makes up for neural network's shortcomings. In this study, the amount of supply, the mean temperature and the population of the area supplied with water are use for neural network's learning patterns for prediction. RMSE(Root Mean Square Error) is used for a MOE(Measure Of Effectiveness). The comparison of the two models showed that the predicting capability of Hybrid model is more effective than that of neural network model. The proposed hybrid model is able to predict of Daily water, thus it can apply real time estimation of operational control of water works and water drain pipes. Proposed models include accidental cases such as a suspension of water supply. The maximum error rate between the estimation of the model and the actual measurement was 11.81% and the average error was lower than 1.76%. The model is expected to be a real-time estimation of the operational control of water works and water/drain pipes.

• Magazine of the Korean Society of Agricultural Engineers
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• v.45 no.2
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• pp.116-125
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• 2003

Predictions of stream water quality require both estimation of pollutant loading from different sources and simulation of water quality processes in the stream. Nonpoint source pollution models are often employed for estimating pollutant loading in rural watersheds. In this study, a conjunctive application of SWAT model and WASP model was made and evaluated for its applicability based on the simulation results. Runoff and nutrient loading obtained from the SWAT model were used for generating input data for WASP model. The results showed that the simulated runoff was in good agreement with the observed data and indicated reasonable applicability. Loading for the water quality parameters predicted by WASP model also showed a reasonable agreement with the observed data. It is expected that stream water quality could be predicted by the coupled application of the two models, SWAT and WASP, in rural watersheds.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.6
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• pp.65-76
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• 2012

Agricultural reservoir water quality simulation model (ARSIM-rev) was developed in this study for water quality simulation of a small and shallow agricultural reservoir with limited observed water quality data. Developed ARSIM-rev is a zero-dimensional water quality model because of little spatial differences in water quality between stations in a small and shallow agricultural reservoir. ARSIM-rev used same water quality reaction equations with WASP except for several equations, and daily based input parameters such as settling rate, release rate from sediment, and light extinction coefficient changed yearly based input parameters in ARSIM-rev. A number of pre- and post-processors were developed such as auto calibration and scenario analysis for ARSIM-rev. CE-QUAL-W2, WASP, and developed ARSIM-rev were applied to Mansu agricultural reservoir to evaluate model performance, and ARSIM-rev demonstrated similar model performance with CE-QUAL-W2 and WASP when low number of observed data was used for agricultural reservoir water quality simulation. Overall, developed ARSIM-rev was feasible for water quality simulation in a small and shallow agricultural reservoir with limited observed water quality data, and it can simulate agricultural reservoir water quality precisely enough like common water quality model such as CE-QUAL-W2 and WASP within a limited time.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.1
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• pp.107-120
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• 2019

Reservoir water level data identify the current water storage of the reservoir, and they are utilized as primary data for management and research of agricultural water. For the reservoir storage management, Korea Rural Community Corporation (KRC) installed water level stations at around 1,600 agricultural reservoirs and has been collecting the water level data every 10 minutes. However, various kinds of outliers due to noise and erroneous problems are frequently appearing because of environmental and physical causes. Therefore, it is necessary to detect outlier and improve the quality of reservoir water level data to utilize the water level data in purpose. This study was conducted to detect and classify outlier and normal data using two different models including the threshold model and the artificial neural network (ANN) model. The results were compared to evaluate the performance of the models. The threshold model identifies the outlier by setting the upper/lower bound of water level data and variation data and by setting bandwidth of water level data as a threshold of regarding erroneous water level. The ANN model was trained with prepared training dataset as normal data (T) and outlier (F), and the ANN model operated for identifying the outlier. The models are evaluated with reference data which were collected reservoir water level data in daily by KRC. The outlier detection performance of the threshold model was better than the ANN model, but ANN model showed better detection performance for not classifying normal data as outlier.

• Journal of Soil and Groundwater Environment
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• v.26 no.1
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• pp.1-7
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• 2021

Air-water interfacial area is of great importance for the analysis of contaminant mass transfer processes occurring in the soil systems. Capillary bundle model has been proposed to estimate the specific air-water interfacial areas in unsaturated soils. In this study, the measured air-water interfacial areas of a soil (loam) using the gaseous interfacial tracer technique were compared to those from capillary bundle model. The measured values converged to the specific solid surface area (7.6×104 ㎠/㎤) of the soil. However, the simulated air-water interfacial areas based on the capillary bundle model deviated significantly from those measured. The simulated values were substantially over-estimated at low end of the water content range, whereas the model under-estimated the air-water interfacial area for the most of the water content range. This under-estimation is considered to be caused by the nature of the capillary bundle model that replaces the soil pores with a bundle of glass capillaries and thus no surface roughness at the inner surface of the capillaries is taken into account for the estimation of the air-water interfacial area with the capillary bundle model. Subsequently, appropriate correction is necessary for the capillary bundle model to estimate the air-water interfacial area in soils. Since the soil-moisture release curve data is the basis of the capillary bundle model, the model can be of use due to its simplicity, while the gaseous tracer technique requires complicated experimental equipment followed by moment analysis of the breakthrough curves. The size distribution profile of the pores filled with gas estimated by the water retention curve was found to be similar to that of particle size at different size range. The shifted distribution of gas-filled pores toward smaller size side compared to the particle size distribution was also found.

• Journal of the Korea Society of Computer and Information
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• v.22 no.11
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• pp.65-71
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• 2017

An integrated water environment management system is necessary in improving water quality, properly allocating water resources, and supporting socio-economic development. Specifically, water quality management system using web map can be an efficient approach to accomplish this system. This paper aims to construct a dynamic water quality management system to reflect a water environment management system which includes three sub-models with consideration of their interrelationships (a socio-economic model based on dynamic Input-Output model, a water resources cycle model, and a water pollutants flow model). Based on simulation, the model can precisely estimate trends of water utilization, water quality, and economic development under certain management targets, and propose an optimal plan. This study utilized the model to analyze the potential of using reclaimed water to accomplish local water environment management and sustainable development plan while exploring the applicable approaches. This study indicates that the constructed water environment management system can be effective and easily adopted to assess water resources and environment while improving the trade-off between economic and environment development, as well as formulate regional development plan.