• Journal of Korean Society of Water and Wastewater
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• v.35 no.6
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• pp.417-424
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• 2021

Algal bloom is an ongoing issue in the management of freshwater systems for drinking water supply, and the chlorophyll-a concentration is commonly used to represent the status of algal bloom. Thus, the prediction of chlorophyll-a concentration is essential for the proper management of water quality. However, the chlorophyll-a concentration is affected by various water quality and environmental factors, so the prediction of its concentration is not an easy task. In recent years, many advanced machine learning algorithms have increasingly been used for the development of surrogate models to prediction the chlorophyll-a concentration in freshwater systems such as rivers or reservoirs. This study used a light gradient boosting machine(LightGBM), a gradient boosting decision tree algorithm, to develop an ensemble machine learning model to predict chlorophyll-a concentration. The field water quality data observed at Daecheong Lake, obtained from the real-time water information system in Korea, were used for the development of the model. The data include temperature, pH, electric conductivity, dissolved oxygen, total organic carbon, total nitrogen, total phosphorus, and chlorophyll-a. First, a LightGBM model was developed to predict the chlorophyll-a concentration by using the other seven items as independent input variables. Second, the time-lagged values of all the input variables were added as input variables to understand the effect of time lag of input variables on model performance. The time lag (i) ranges from 1 to 50 days. The model performance was evaluated using three indices, root mean squared error-observation standard deviation ration (RSR), Nash-Sutcliffe coefficient of efficiency (NSE) and mean absolute error (MAE). The model showed the best performance by adding a dataset with a one-day time lag (i=1) where RSR, NSE, and MAE were 0.359, 0.871 and 1.510, respectively. The improvement of model performance was observed when a dataset with a time lag up of about 15 days (i=15) was added.

• Journal of Korean Society of Water and Wastewater
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• v.37 no.6
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• pp.363-373
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• 2023

Numerous studies have established a correlation between sociodemographic characteristics and water usage, identifying population as a primary independent variable in mid- to long-term demand forecasting. Recent dramatic sociodemographic changes, including urban concentration-rural depopulation, low birth rates-aging population, and the rise in single-person households, are expected to impact water demand and supply patterns. This underscores the necessity for operational and managerial changes in existing water supply systems. While sociodemographic characteristics are regularly surveyed, the conducted surveys use aggregate units that do not align with the actual system. Consequently, many water demand forecasts have been conducted at the administrative district level without adequately considering the water supply system. This study presents an upward water demand forecasting model that accurately reflects real water facilities and consumers. The model comprises three key steps. Firstly, Statistics Korea's SGIS (Statistical Geological Information System) data was reorganized at the DMA level. Secondly, DMAs were classified using the SOM (Self-Organizing Map) algorithm to consider differences in water facilities and consumer characteristics. Lastly, water demand forecasting employed the PCR (Principal Component Regression) method to address multicollinearity and overfitting issues. The performance evaluation of this model was conducted for DMAs classified as rural areas due to the insufficient number of DMAs. The estimation results indicate that the correlation coefficients exceeded 0.9, and the MAPE remained within approximately 10% for the test dataset. This method is expected to be useful for reorganization plans, such as the expansion and contraction of existing facilities.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1201-1204
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• 2004

This paper suggests a DCS (Distributed Control System) model for steam temperature system of the thermal power plant. The model calculated within sectional range is linear. In order to calculate mathematical models, the system is partitioned into two or three sectors according to its thermal conditions, that is, saturated water/steam and superheating state. It is divided into three sections; water supply, steam generation and steam heating loop. The steam heating loop is called 'superheater' or steam temperature system. Water spray supply is the control input. A first order linear model is extracted. For linear approach, sectional linearization is achieved. Modeling methodology is a decomposition-synthetic technique. Superheater is composed of several tube-blocks. For this block, linear input-output model is to be calculated. Each tiny model has its transfer function. By expanding these block models to total system, synthetic DCS linear models are derived. Control instrument include/exclude models are also considered. The resultant models include thermal combustion conditions, and applicable to practical plant engineering field.

• Journal of Korea Water Resources Association
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• v.30 no.6
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• pp.753-759
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• 1997

A new numerical model was doveloped to simulate density-dependent ground water flow and solute transport. Accuracy of a numerical model depends upon how well it simulates advection dominant situations because numerical oscillations can spoil solutions for these situations. Nonlinear oscillation-absorption finite element method. based on the variational principle, was employed. Unlike previous numerical models, this model can easily be expanded for more complex situations. Accuracy of the model is evaluated by comparing with analytical solutions and results of other numerical model.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.1
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• pp.33-39
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• 2018

As the retrieval accuracy of PWV estimates from GPS measurements is proportional to the accuracy of water vapor WMT, the WMT model is a significant formulation in the conversion of PWV from the GPS ZWD. The purpose of this study is to develop a MWMT model for the retrieval of highly accurate GPS PWV using the radiosonde measurements from six upper-air observing stations in the region of Korea. The values of 1-hr PWV estimated at four GPS stations during one year are used to evaluate the validity of the MWMT model. It is compared to the PWV obtained from radiosonde data that are located in the vicinity of GPS stations. Intercomparison of radiosonde PWVs and GPS PWVs derived using different WMT models is performed to assess the quality of our MWMT model for Korea. The result in this study indicates that the MWMT model is an effective model to retrieve the enhanced accurate GPS PWV, compared to other GPS PWV derived by Korean annual or global WMT models.

• Journal of Ocean Engineering and Technology
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• v.24 no.6
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• pp.41-50
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• 2010

This study established a 3D ecosystem model composed of stratification considering the topographic heat accumulation effect and river outflow, and then applied this model to Jinhae, Masan Bay. Specifically, it reenacted the formation and developmental process of ODW according to the stratification by calculating the kinematic eddy viscosity and eddy diffusion coefficient of the stratification model. The results were used as input data for the ecosystem model and compared with DO, COD, I-N, and I-P, which is the standard index of ocean water quality. As a result, it was determined that COD and T-N are third grade and T-P is second grade standards for a natural environment.

• Journal of Korean Society on Water Environment
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• v.23 no.6
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• pp.872-880
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• 2007

For effective management of sediment-related diffuse pollution, it is of utmost importance to estimate spatial variation of sediment transport processes within a catchment. A mathematical model can play a critical role in estimating sediment transport processes at the catchment scale provided that the model structure is appropriate for representing major sediment transport processes of the catchment of interest. This paper introduces a distributed catchment model River Basin Water Quality Simulator (RBWQS) and presents some results of its application to a small rural catchment in Korea. The model has been calibrated and validated for a wet period using hourly hydrographs and sediment concentrations observed at the catchment outlet. Based on the model simulation results, the spatial variation of sediment transport processes across the catchment and the effects of paddy fields and small reservoirs on hydrology and sediment transport have been analyzed at the catchment scale.

• Magazine of the Korean Society of Agricultural Engineers
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• v.42 no.4
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• pp.67-75
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• 2000

A simulation model for reservoir sizing was developed to be applied in a region with insufficient hydrological data. Reservoir storage balance equation was formulated on a monthly basis. Gajiyama equation was generalized to estimate monthly reservoir inflow more accurately. Monthly evaporation equation on a reservoir water surface was introduced , which was functioned with monthly mean temperature. Generalized Gajiyama equation was applied to estmate reservoir inflow of the Sayeon dam. Nash-Sutcliffe's model efficiency was 0.793. Using developed model for reservoir sizing, water supply capacity was analyzed with 118.000㎥/day on the Sayeon dam. This showed a reasonable result as compared with 110000㎥/day in other technical report. For general application of developed model, a virtual reservoir was considered and its dta of surface area and volume by elevation was prepared using DEM. Using the model, size of reservoir was determined and water supply capacity was anlayzed on a virtual reservoir.

• Journal of Environmental Impact Assessment
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• v.20 no.4
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• pp.435-442
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• 2011

In this study, a regression model was developed for prediction of inflow temperature to support an effective thermal stratification simulation of Yongdam Reservoir, using the relationship between gaged inflow temperature and air temperature. The effect of reproductability for thermal stratification was evaluated using EFDC model by gaged vertical profile data of water temperature(from June to December in 2005) and ex-developed regression models. Therefore, in the development process, the coefficient of correlation and determination are 0.96 and 0.922, respectively. Moreover, the developed model showed good performance in reproducing the reservoir thermal stratification. Results of this research can be a role to provide a base for building of prediction model for water quality management in near future.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.24 no.6
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• pp.538-544
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• 2012

Analysis of thermal performance is required for the economic operation of turbine cycle of power plant. We developed corrective model of main feed water flow which is the most important parameter for the precise analysis of turbine cycle performance. Classification model for the identification of feed water flow measurement status was applied to increase the suitability of the corrective model. We used neural network and support vector machine to develop estimation model of main feed water flow with more generalization capability. The estimation model can be used practically to evaluate corrective performance of turbine cycle plant.