• Journal of Korea Water Resources Association
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• v.51 no.4
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• pp.335-346
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• 2018

A vast amount of riverine spatial dataset have recently become available, which include hydrodynamic and morphological survey data by advanced instrumentations such as ADCP (Acoustic Doppler Current Profiler), transect measurements obtained through building various river basic plans, riverine environmental and ecological data, optical images using UAVs, river facilities like multi-purposed weir and hydrophilic sectors. In this regard, a standardized data model has been subsequently required in order to efficiently store, manage, and share riverine spatial dataset. Given that riverine spatial dataset such as river facility, transect measurement, time-varying observed data should be synthetically managed along specified river network, conventional data model showed a tendency to maintain them individually in a form of separate layer corresponding to each theme, which can miss their spatial relationship, thereby resulting in inefficiency to derive synthetic information. Moreover, the data model had to be significantly modified to ingest newly produced data and hampered efficient searches for specific conditions. To avoid such drawbacks for layer-based data model, this research proposed a relational data model in conjunction with river network which could be a backbone to relate additional spatial dataset such as flowline, river facility, transect measurement and surveyed dataset. The new data model contains flexibility to minimize changes of its structure when it deals with any multi-dimensional river data, and assigned reach code for multiple river segments delineated from a river. To realize the newly developed data model, Seom river was applied, where geographic informations related with national and local rivers are available.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.976-976
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• 2012

The applicability of a distributed rainfall-runoff model for large river basin flood forecasts is analyzed by applying the model to the Nakdong River basin. The spatially explicit hydrologic model was constructed and calibrated by the several storm events. The assimilation of the large scale Nakdong River basin were conducted by calibrating the sub-basin channel outflow, dam discharge in the basin rainfall-runoff model. The applicability of automatic and semi-automatic calibration methods was analyzed for real time calibrations. Further an ensemble distributed rainfall runoff model has been developed to measure the runoff hydrograph generated for any temporally-spatially varied rainfall events, also the runoff of basin can be forecast at any location as well. The results of distributed rainfall-runoff model are very useful for flood managements on the large scale basins. That offer facile, realistic management method for the avoiding the potential flooding impacts and provide a reference for the construct and developing of flood control facilities.

• Water for future
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• v.18 no.4
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• pp.327-333
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• 1985

The flow pattern of the nearshore current generated around the breached breakwaters and river-mouth was simulated by numerical model in the case of the inclined incident wave without river discharge when the detached breakwaters were installed at the river-mouth area for the protection against the blockade of the river-mouth. The validity of the numerical model was testified y comparision with the results obtained through the hydraulic model test at the fixed bed. The deformation of the topographic features around the river-mouth and the detached breakwaters was examined through the three-dimensional hydraulic model test at the movable bed. The usefulness of the detached breakwater work for the protection against the blockade of the river-mouth was identified by the experimental results.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1201-1208
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• 2009

A making a decision of construction cost has important meaning and function for both contractor and owner in construction projects. Especially, it should be premised that estimating the construction cost in efficient and rational way in public construction, which is invested by government funds, for efficient execution of the budget and investment as a side of government. The systematic methodology for estimating construction cost approximately of a river facility construction project has not yet been established because of its unique characteristics including its relatively small project size in terms of cost. On this study, It collect and analyze a river facility construction historical cost data for develop an approximate cost estimating model for river applied by typical embankment section method and rate application of the others activity type. And it verify suitability of model through a that result of application of real river facility construction statement at developed model. By this study, it is expected to reasonable and systematic estimating construction cost through application of developed model.

• Journal of Environmental Science International
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• v.3 no.1
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• pp.11-16
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• 1994

Generally, it is difficult to predict water quality in a tidal river, because tidal flows make the transport phenomena more complicated. The purpose of this study is to clarify long-term mass transport in a tidal river through suggestion of simulation model. A simulation model based on a Lagrangian coordinate system, which has the advantage reducing numerical dispersion, was used to calculate changes in concentration of chlorides. Several field surveys were conducted to verify calculated results. Concludingly, long-term behavior of mas transport in a tidal river can be represented using the model.

• Korean Journal of Agricultural Science
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• v.46 no.4
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• pp.843-856
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• 2019

Real-time flood prediction has an important role in significantly reducing potential damage caused by floods for urban residential areas located downstream of river basins. This paper presents an effective approach for flood forecasting based on the construction of a deep neural network (DNN) model. In addition, this research depends closely on the open-source software library, TensorFlow, which was developed by Google for machine and deep learning applications and research. The proposed model was applied to forecast the flowrate one, two, and three days in advance at the Son Tay hydrological station on the Red River, Vietnam. The input data of the model was a series of discharge data observed at five gauge stations on the Red River system, without requiring rainfall data, water levels and topographic characteristics. The research results indicate that the DNN model achieved a high performance for flood forecasting even though only a modest amount of data is required. When forecasting one and two days in advance, the Nash-Sutcliffe Efficiency (NSE) reached 0.993 and 0.938, respectively. The findings of this study suggest that the DNN model can be used to construct a real-time flood warning system on the Red River and for other river basins in Vietnam.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.26-26
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• 2012

The Kurobe river, which runs through eastern Toyama Prefecture is one of the most famous rivers for wild water because of its steep slope in the range from 1/5 to 1/120. This river forms an alluvial fan in the range up to 13 kilometers from the sea. In this region, significant seepage flow occurs and thus the stream sometimes been intermitted. Moreover, the amount of seepage flow seems to vary with the groundwater level of the region. To keep the river environment healthy for flora and fauna, especially to conserve good condition for spawning of fishes, an appropriate environmental flow should be maintained in the river. To achieve this target, controlling of the upstream reservoir has to be studied in depth. One of the major problems to decide the amount of water to be released from the reservoir to maintain the environmental flow is to estimate the amount of water leaked into the groundwater from the river. This phenomenon is affected by the river flow rate as well as the groundwater level in the alluvial fan and the conditions vary in space and time. Thus, a grid-based hydrological cycle analysis model NK-GHM has been applied to clarify the hydrological cycle componentsin this area including seepage/discharge from/to the river. The model was tested by comparing with river flow rate, groundwater levels and other observations and found that the model described those observations well. Consequently, the seepage from the Kurobe river was found significant but it was also found that the groundwater in this region has been preserved by the recharge from the irrigation water supply into paddy fields in the alluvial fan.

• Journal of Korean Society on Water Environment
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• v.22 no.6
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• pp.1144-1150
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• 2006

Recently, as water pollution accidents in rivers have increased, there is an increased interest in water quality forecast with accurate simulation. QUAL2E model, widely used for water quality analysis, uses the same hydraulic characteristics, such as depth and velocity, in a reach. The flow of the river is changed by various hydraulic constructions or by topography in a real river channel. In this study, a hydraulic connection module is developed to consider flow variations of river channels in QUAL2E model. The module uses the simulations results of non-uniform flow of a 1-D hydraulic model such as DWOPER or HEC-RAS. The improved QUAL2E model with this module was applied to a downstream section of Paldang Dam on the Han River. The results show the variation of water quality very well in a reach where flowing vary abruptly, like the Jamsil submerged weir.

• Journal of Environmental Science International
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• v.9 no.6
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• pp.455-462
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• 2000

This study aims at the development of the model for a forecasting of water quality in river basins using artificial neural network technique. Water quality by Artificial Neural Network Model forecasted and compared with observed values at the Sangju q and Dalsung stations in Nakdong river basin. For it, a multi-layer neural network was constructed to forecast river water quality. The neural network learns continuous-valued input and output data. Input data was selected as BOD, CO discharge and precipitation. As a result, it showed that method III of three methods was suitable more han other methods by statistical test(ME, MSE, Bias and VER). Therefore, it showed that Artificial Neural Network Model was suitable for forecasting river water quality.

• Journal of Environmental Science International
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• v.24 no.8
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• pp.1023-1036
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• 2015

A reliable streamflow forecasting is essential for flood disaster prevention, reservoir operation, water supply and water resources management. This study proposes a hybrid model for river stage forecasting and investigates its accuracy. The proposed model is the wavelet packet-based artificial neural network(WPANN). Wavelet packet transform(WPT) module in WPANN model is employed to decompose an input time series into approximation and detail components. The decomposed time series are then used as inputs of artificial neural network(ANN) module in WPANN model. Based on model performance indexes, WPANN models are found to produce better efficiency than ANN model. WPANN-sym10 model yields the best performance among all other models. It is found that WPT improves the accuracy of ANN model. The results obtained from this study indicate that the conjunction of WPT and ANN can improve the efficiency of ANN model and can be a potential tool for forecasting river stage more accurately.