• Water Engineering Research
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• v.1 no.1
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• pp.25-37
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• 2000

This study presents some results of a preliminary study for the coupled precipitation and river flow prediction system. The model system in based on three numerical models, Mesoscale Atmospheric Simulation model for generating atmospheric variables. Soil-Plant-Snow model for computing interactions within soil-canopy-snow system as well as the energy and water exchange between the atmosphere and underlying surfaces, and TOPMODEL for simulating stream flow, subsurface flow, and water tabled depth in an watershed. The selected study area is the 2,703 $\alpha_4$ $\km_2$ Soyang River basin with outlet at Soyang dam site. In addition to providing the results of rainfall and stream flow predictions, some results of DEM and GIS application are presented. It is obvious that the accurate river flow predictions are highly dependant on the accurate predictation predictions.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1144_1145
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• 2009

Tidal power station is using the difference of the ebb and flow and the single action tidal power is dependent on tide amplitude and basin volume. Therefore the inflow of basin in rainy season has also effect on the daily power. Also if operating units are changed then starting head too changed. Therefore the number of units are very important for the optimization model. According to our study the primary point when we make a determination of optimization is starting head and governorl control mode. On this study optimization model for tidal power station is considered all of this conditions.

• Journal of Ship and Ocean Technology
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• v.8 no.2
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• pp.20-28
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• 2004

A dynamic-thermodynamic sea ice model with 50-km spatial and 24-hour temporal resolution is used to investigate the spatial and long-term temporal variability of the sea ice cover the Arctic Basin. The model satisfactorily reproduces the averaged main characteristics of the sea ice and the sea ice extent in the Arctic Basin and its decrease in early 1990th. At times model allows to suppose partial recovery of sea ice cover in the last years of twenty century. The employment of explicit form for description of ridging gives opportunity to assume that the observed thinning is the result of reduction the intensity of ridging processes and to estimate long-term variability of probability the ridge free navigation in the different parts of the Arctic Ocean including the North Sea Route area.

• Journal of Korean Society of Water and Wastewater
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• v.19 no.6
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• pp.728-737
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• 2005

Water quality of the Hwanggujicheon is poor because of the rapid housing and development in the large area of the basin. Establishment of water quality management strategy, based on the pollution sources survey and pollutant loads estimation, has to be established for the preservation of the stream water quality of the region. In this study, waste load allocation model to achieve the water quality goal of the stream and the optimization of pollutant load reduction, was developed. Nonpoint pollutant loads calculated by runoff model in the previous study are utilized for pollutant loads estimation of the drainage areas in this study. From the application result of the allocation model, water quality goals of the Hwanggujicheon that can be achieved as a matter of fact are BOD 8 mg/L. To achieve these goals, 23% of effluent BOD loads have to be reduced in the basin.

• Journal of Ocean Engineering and Technology
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• v.19 no.3
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• pp.47-53
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• 2005

Model for dust and salt transportation from the dried bottom of the Aral Sea is suggested. Theoretical analysis is based on the turbulent diffusion equation for the averaged function of passive impurity concentration. One-layer model of the atmospheric boundary layer is assumed. Impurity precipitation rates are calculated as the functions of the particle size and the distance source of particles. Analytical solutions for the point and two-dimensional sources of impurities are found. Model calculations for salt and sand transport from the Aral Sea basin are made on the basis of 2D source model with a constant intensity.

• Journal of Korea Water Resources Association
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• v.30 no.4
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• pp.327-334
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• 1997

The purpose of this study is to calibrate the parameters of tank model for the derivation of a design flood hydrograph at a certain river basin outlet. The selected stations are Gongju and Naju station which are located in the Keum and the Youngsan river basin, respectively. The results of parameters calibration for tank model are represented a little different values comparing with the proposed values at Gongju station through the verification of flood hydrograph in modeling procedure but the values of tank parameters at Naju station are fitted well for the derivation of flood hydrograph using the proposed design parameters of tank model.

• Water for future
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• v.19 no.3
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• pp.267-276
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• 1986

This study aims at the development of flood runoff model by comparing and analyzing nonlinear models with linear models in rier basins. The models which are used at the analysis are Nash model and Runoff function method as linear models, and Tank model and Storage function method as nonlinear models. The results, which are obtained from the analysis of these models by using hydrologic data of a representative basin in Nakdong river, Wi-chun basin, show that the peak time, peak flow and flood hydrogrphs by nonlinear models are better than those by linear models in comparison with observed ones, and that nonlinear models are suittable as flood runoff model.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.4
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• pp.13-20
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• 2013

The purpose of this study was to evaluate the applicability of the HSPEXP expert system for the calibration of the Hydrological Simulation Program - Fortran (HSPF) for the study watershed. HSPEXP offers advice to the modeler, suggesting parameter changes that might result in better representation of a river basin and provides explanations supporting the recommended parameter changes. The study watershed, Sancheong, is located within the Nakdong River Basin and having the size of $1,072.4km^2$. Input data for the HSPF model were obtained from the landuse map, digital elevation map, meteorological data and others. Water flow data from 2006 to 2008 were used for calibration and from 2009 to 2010 were for validation. Using the HSPEXP expert system, hydrological parameters were adjusted based on total volume, then low flows, storm flows, and finally seasonal flows. For the calibration and validation period, all the HSPEXP model performance criteria were satisfied.

• Journal of Korean Society on Water Environment
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• v.32 no.6
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• pp.589-599
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• 2016

Implemented since 2004, TPLC (Total Pollution Load Control) is the most powerful water-quality protection program. Recently, uncertainty of prediction using steady state model increased due to changing water environments, and necessity of a dynamic state model, especially the watershed model, gained importance. For application of watershed model on TPLC, it needs to be feasible to adjust the relationship (mass-balance) between discharged loads estimated by technical guidance, and arrived loads based on observed data at the watershed outlet. However, at HSPF, simulation is performed as a semi-distributed model (lumped model) in a sub-basin. Therefore, if the estimated discharged loads from individual pollution source is directly entered as the point source data into the RCHRES module (without delivery ratio), the pollutant load is not reduced properly until it reaches the outlet of the sub-basin. The hypothetic RCHRES generated using the HSPF BMP Reach Toolkit was applied to solve this problem (although this is not the original application of Reach Toolkit). It was observed that the impact of discharged load according to spatial distribution of pollution sources in a sub-basin, could be expressed by multi-segmentation of the hypothetical RCHRES. Thus, the discharged pollutant load could be adjusted easily by modification of the infiltration rate or characteristics of flow control devices.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.140-140
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• 2021

Urban flood management is a crucial and challenging task, particularly in developed cities. Therefore, accurate prediction of urban flooding under heavy precipitation is critically important to address such a challenge. In recent years, machine learning techniques have received considerable attention for their strong learning ability and suitability for modeling complex and nonlinear hydrological processes. Moreover, a survey of the published literature finds that hybrid computational intelligent methods using nature-inspired algorithms have been increasingly employed to predict or simulate the streamflow with high reliability. The present study is aimed to propose a novel approach, an ensemble tree, Bayesian Additive Regression Trees (BART) model incorporating a nature-inspired algorithm to predict hourly multi-step ahead streamflow. For this reason, a hybrid intelligent model was developed, namely GA-BART, containing BART model integrating with Genetic algorithm (GA). The Jungrang urban basin located in Seoul, South Korea, was selected as a case study for the purpose. A database was established based on 39 heavy rainfall events during 2003 and 2020 that collected from the rain gauges and monitoring stations system in the basin. For the goal of this study, the different step ahead models will be developed based in the methods, including 1-hour, 2-hour, 3-hour, 4-hour, 5-hour, and 6-hour step ahead streamflow predictions. In addition, the comparison of the hybrid BART model with a baseline model such as super vector regression models is examined in this study. It is expected that the hybrid BART model has a robust performance and can be an optional choice in streamflow forecasting for urban basins.