• Magazine of the Korean Society of Agricultural Engineers
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• v.37 no.2
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• pp.13.2-20
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• 1995

An water balance model was formulated to simulate the change in water levels at the estuary reservoir from sluice gate releases and the inflow hydrographs, and an one-di- mensional flood routing model was formulated to simulate temporal and spatial varia- tions of flood hydrographs along the estuarine river. Flow rates through sluice gates were calibrated with data from the estuary dam, and the results were used for a water balance model, which did a good job in predicting the water level fluctuations. The flood routing model which used the results from two hydrologic models and the water balance model simulated hydrographs that were in close agreement with the observed data. The flood forecasting model was found to be applicable to real-time forecasting of water level fluc- tuations with reasonable accuracies.

• Journal of Korea Water Resources Association
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• v.33 no.1
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• pp.73-85
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• 2000

A water quality model was developed for small stream at a upland agricultural watershed. A control volume method was employed to digest the severe variability of stream shape, water quality and discharge at small streams. We estimated optimum reaction coefficients and model structure using a random number generation technique. The index of agreement and coefficient of efficiency were introduced for the model calibration criterion. As the result, the reliability of model parameter estimation could be improved. The applicability of model was tested by a set of sampling results at Yongduckchun in Kimhae. The variability of water quality reaction coefficient was explored through the observed data and using the developed model. model.

• Journal of Korean Society of Water and Wastewater
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• v.30 no.1
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• pp.9-17
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• 2016

Disinfectant/oxidation process is a crucial process in water treatment for supplying safe drinking water. Chlorination is still widely used for water treatment area due to its effectiveness on microbial inactivation and economic feasibility. Recently, disinfection concern in marine environment is increasing, for example, movement of hazardous marine organism due to ballast water, marine environmental degradation due to power plant cooling water discharge, and increase of the amount of disinfectant in the offshore plant. It is needed to conduct the assessment of disinfectant behavior and the development of disinfectant prediction model in seawater. The appropriate prediction model for disinfectant behavior is not yet provided. The objective of the study is to develop chlorine decay model in seawater. Various model types were applied to develop the seawater chlorine decay model, such as first order decay model, EPA model, and two-phase model. The model simulation indicated that chlorine decay in seawater is influenced by both organic and inorganic matter in seawater. While inorganic matter has a negative correlation with the chlorine decay, organic matter has a positive correlation with the chlorine decay.

• Journal of Korean Society of Water and Wastewater
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• v.11 no.1
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• pp.109-118
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• 1997

In this study, we examined the structural analysis of water demand fluctuation for water distribution control of water supply network. In order to analyze for the length of stationary time series, we calculate autocorrelation coefficient of each case equally divided data size. As a result, it was found that, with the data size of around three months, any case could be used as stationary time series. we analyze cross-correlation coefficient between the daily water consumption's data and primary influence factors. As a result, we have decided to use weather conditions and maximum temperature as natural primary factors and holidays as a social factor. Applying the multiple ARIMA model, we obtains an effective model to describe the daily water demand prediction. From the forecasting result, even though we forecast water distribution quantity of the following year, estimated values well express the flctuations of measurements. Thus, the suitability of the model for practical use can be confirmed. When this model is used for practical water distribution control, water distribution quantity for the following day should be found by inputting maximum temperature and weather conditions obtained from weather forecast, and water purification plants and service reservoirs should be operated based on this information while operation of pumps and valves should be set up. Consequently, we will be able to devise a rational water management system.

• Journal of Environmental Science International
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• v.8 no.3
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• pp.355-361
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• 1999

The aim of this study is to develop the water quality simulation model (BAYQUAL) that deal with the physical, chemical and biological aspects of fate/behavior of pollutants in the bay. BAYQUAL is a two dimensional, time-variable finite element water quality model based on the flow simulation model in bay(BAYFLOW). The algorithm is composed of a hydrodynamic module which solves the equations of motion and continuity, a pollutnat dispersion module which solves the dispersion-advection equation. The applicability and feasibility of the model are discussed by applications of the model to the Kwangyang bay of south coastal waters of Korea. Based on the field data, the BAYQUAL model was calibrated and verified. The results were in good agreement with measured value within relative error of 14% for COD, T-N, T-P. Numerical simulations of velocity components and tide amplitude(M2) were agreed closely with the actual data.

• Journal of Environmental Health Sciences
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• v.27 no.3
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• pp.35-42
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• 2001

The characteristics of water quality variation were predicted by stochastic model in Chungju dam, north Chungcheong province of south Korea, Monthly time series data of water quality from 1989 to 2001;temperature, BOD, COD and SS, were obtained from environmental yearbook and internet homepage of ministry of environment. Development of model was carried out with Box-Jenkins method, which includes model identification, estimation and diagnostic checking. ACF and PACF were used to model identification. AIC and BIC were used to model estimation. Seosonal multiplicative ARIMA(1, 0, 1)(1, 1, 0)$_{12}$ model was appropriate to explain stochastic characteristics of temperature. BOD model was ARMa(2, 2, 1), COD was seasonal multiplicative ARIMA(2. 0. 1)(1. 0, 1)$_{12}$, and SS was ARIMA(1, 0, 2) respectively. The simulated water quality data showed a good fitness to the observed data, as a result of model verification.ion.

• Journal of Korean Society on Water Environment
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• v.23 no.1
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• pp.64-71
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• 2007

In this study daily flow data is constructed from 8-day intervals flow data which has been measured by Nakdong River Water Environmental Laboratory. TANK model is used to expand 8-day intervals flow data into daily flow data. Using the Sequential quadratic programing, TANK model is auto-calibrated with daily precipitation and 8-day interval flow data. Generated and measured daily surface flow, ground water flow data and ground water recharge are shown to be in a good agreement. From this result, it is thought that this method has the potential to provide daily flow data for calibrating an watershed model such as SWAT.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.28 no.9
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• pp.355-360
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• 2016

Continuing to the modeling of heating load, this paper, as the second part of consecutive works, presents LS-SVM (least square support vector machine) based model of winter time apartment hot water supply load in a district heating system, so as to be used in prediction of heating energy usage. Similar, but more severely, to heating load, hot water supply load varies in highly nonlinear manner. Such nonlinearity makes analytical model of it hardly exist in the literatures. LS-SVM is known as a good modeling tool for the system, especially for the nonlinear system depended by many independent factors. We collect 26,208 data of hot water supply load over a 13-week period in winter time, from 12 heat exchangers in seven different apartments. Then part of the collected data were used to construct LS-SVM based model and the rest of those were used to test the formed model accuracy. In modeling, we first constructed the model of district heating system's hot water supply load, using the unit heating area's hot water supply load of seven apartments. Such model will be used to estimate the total hot water supply load of which the district heating system needs to provide. Then the individual apartment hot water supply load model is also formed, which can be used to predict and to control the energy consumption of the individual apartment. The results obtained show that the total hot water supply load, which will be provided by the district heating system in winter time, can be predicted within 10% in MAPE (mean absolute percentage error). Also the individual apartment models can predict the individual apartment energy consumption for hot water supply load within 10% ~ 20% in MAPE.

• Journal of Integrative Natural Science
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• v.14 no.3
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• pp.99-106
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• 2021

A mixture of alcohol and water is commonly used as antifreeze, liquor, and the fundamental solvents for the manufacture of cosmetics, pharmaceuticals, and inks in our daily life. Since various properties of alcohol water mixtures such as density, boiling or melting point, viscosity, and dielectric constant are determined by their mixing ratio, it is very important to know the mixing ratio to predict their properties. One of simple method to find the mixing ratio is measuring the density of the mixtures. However, it is not easy to predict the mixing ratio from the density of the mixtures because the relationship between mixing ratio and density has not been established well. The relationship is dependent on the relative sizes of solute and solvent molecules, and their interactions. Recently, an empirical model to predict the density of glycerol water mixture from their mixing ratio has been introduced. The suggested model is simple but quite accurate for glycerol water mixture. In this article, we investigated the applicability of this model to different alcohol water mixtures. Densities for six different alcohol water mixtures containing various alcohols (e.g., ethylene glycol, 1,3-propane diol, propylene glycol, methanol, ethanol, and 1-propanol) were simulated and compared to experimentally measured ones to investigate the applicability of the model proposed for glycerol water mixtures to other alcohol water mixtures. The model predicted the actual density of all alcohol water mixtures tested in this article with high accuracy at various ratios. This model can probably be used to predict the mixing ratio of other alcohol water mixtures from their densities beyond 6 alcohols tested in this article from their densities.

• Environmental Engineering Research
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• v.24 no.3
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• pp.404-411
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• 2019

In this study, we designed a data-driven model to predict chlorophyll-a using M5P model tree and extreme learning machine (ELM). The Juksan weir in the Youngsan River has high chlorophyll-a, which is the primary indicator of algal bloom every year. Short-term algal bloom prediction is important for environmental management and ecological assessment. Two models were developed and evaluated for short-term algal bloom prediction. M5P is a classification and regression-analysis-based method, and ELM is a feed-forward neural network with fast learning using the least square estimate for regression. The dataset used in this study includes water temperature, rainfall, solar radiation, total nitrogen, total phosphorus, N/P ratio, and chlorophyll-a, which were collected on a daily basis from January 2013 to December 2016. The M5P model showed that the prediction model after one day had the highest performance power and dropped off rapidly starting with predictions after three days. Comparing the performance power of the ELM model with the M5P model, it was found that the performance power of the 1-7 d chlorophyll-a prediction model was higher. Moreover, in a period of rapidly increasing algal blooms, the ELM model showed higher accuracy than the M5P model.