• 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 the Korean Society of Industry Convergence
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• v.2 no.2
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• pp.51-59
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• 1999

This study aims at the development of the techniques for the rainfall forecasting in river basins by applying neural network theory and compared with results of Multivariate Model (MVM). This study forecasts rainfall and compares with a observed values in the San Chung gauging stations of Nakdong river basin for the rainfall forecasting of river basin by proposed Neural Network Model(NNM). For it, a multi-layer Neural Network is constructed to forecast rainfall. The neural network learns continuous-valued input and output data. The result of rainfall forecasting by the Neural Network Model is superior to the results of Multivariate Model for rainfall forecasting in the river basin. So I think that the Neural Network Model is able to be much more reliable in the rainfall forecasting.

• Journal of Korea Water Resources Association
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• v.52 no.8
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• pp.515-529
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• 2019

Geospatial information for river network and watershed boundary have played a fundamental roles in terms of river management, planning and design, hydrological and hydraulic analysis. Irrespective of their importance, the lack of punctual update and improper maintenance in currently available river-related geospatial information systems has revealed inconsistency issues between individual systems and spatial inaccuracy with regard to reflecting dynamically transferring riverine geography. Given that digital elevation models (DEMs) of high spatial resolution enabling to reproduce precise river network are only available adjacent to national rivers, DEMs with poor spatial resolution lead to generate unreliable river network information and thereby reduce their extensible applicabilities. This study first of all evaluated published spatial information available in Korea with respect to their spatial accuracy and consistency, and also provides a methodology and tool to modify existing low resolution of DEMs by means of striation of conventional or digitized river network to replicate input river network in various degree of further delineation. The tool named FSND was designed to be operated in ArcGIS ModelBuilder which ensures to automatically simulate river network striation to DEMs and delineation with different flow accumulation threshold. The FNSD was successfully validated in Seom River basin to identify its replication of given river network manually digitized based on recent aerial photograph in conjunction with a DEM with 30 meter spatial resolution. With the derived accuracy of reproducibility, substantiation of a various order of river network and watershed boundary from the striated DEM posed tangible possibility for highly extending DEMs with low resolution to be capable of producing reliable riverine spatial information subsequently.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.2039-2043
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• 2007

Like other major river basin systems in the West of the United States the Platte River Basin are faced with the challenges of allocating more water for plant and animal species. A part of the Central Platte River was designated as critical habitat for the whooping crane in 1978. The water allocation system in the Platte River Basin is dominated by the Prior Appropriation Doctrine, which allocates water according to the priorities based on the date of water use. The Platte River Basin segregated into five subregions for purpose of analysis. 24 years of historic records of monthly flow and all the demands were complied. The simulation of river basin modeling includes physical operation of the system including water allocation by water rights and interstate compact agreements, reservoir operations, and diversion with consumptive use and return flow. MODSIM, a generalized river basin network model, was used for estimating the timing and magnitude of impacts on river flows and diversions associated with water transfers from each region. A total of 20 alternatives were considered, covering transfers from each of the five regions of basin with several options. The result shows that the timing and availability of augmented water at the critical habitat is not only a function of use by junior appropriators, but also of river losses, and timing of return flows.

• Journal of Wetlands Research
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• v.5 no.1
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• pp.15-27
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• 2003

The fractal study in river basin has been performed for the sinuosity of an individual stream and bifurcation of the stream network. The previous studies has suggested many methods or equations for the fractal dimension estimation in a river network. This study used those many equations for the estimation of fractal dimensions on the streams such as Bokha, Gonjiam, and Pocheon streams. The estimated dimensions are in the range of 1 to 1.359 for the individual stream and 1.634 to 2 for the stream network. The most of equations were suggested based on the assumption of self-similarity of a river basin for the individual stream and stream network. However, the real river basin could be characterized by self-affinity rather than self-similarity. Even though we estimate the dimensions by using many equations, we could not recommend which one is better equation for the estimation of fractal dimension. This might be from the self-similarity assumption of equations. Therefore, the assumption and research work of self-affinity will be needed for the appropriate estimation of fractal dimension in river basin.

• 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.

• Water Engineering Research
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• v.1 no.2
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• pp.129-136
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• 2000

Predicting water levels is a difficult task because a lot of uncertainties are included. Therefore the neural network which is appropriate to such a problem, is introduced. One day ahead forecasting of river stage in the Bocheong River is carried out by using the neural network model. Historical water levels at Snagye gauging point which is located at the downstream of the Bocheong River and average rainfall of the Bocheong River basin are selected as training data sets. With these data sets, the training process has been done by using back propagation algorithm. Then waters levels in 1997 and 1998 are predicted with the trained algorithm. To improve the accuracy, a filtering method is introduced as predicting scheme. It is shown that predicted results are in a good agreement with observed water levels and that a filtering method can overcome the lack of training patterns.

• Journal of Environmental Impact Assessment
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• v.18 no.5
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• pp.271-280
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• 2009

The objective of this study is to develop a reliable flow forecasting model based on neural network algorithm in order to provide flow rate at stream sections without flow measurement in Nakdong river. Stream flow rate measured at 8-days interval by Nakdong river environment research center, daily upper dam discharge and precipitation data connecting upstream stage gauge were used in this development. Back propagation neural network and multi-layer with hidden layer that exists between input and output layer are used in model learning and constructing, respectively. Model calibration and verification is conducted based on observed data from 3 station in Nakdong river.

• Journal of The Geomorphological Association of Korea
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• v.23 no.3
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• pp.57-66
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• 2016

The objective of this study is to find out geomorphological characteristics of historical ruins where people produced and consumed large jar coffins excavated in the Yeongsan river basin using the map of old drainage network to restore distribution network. For this purpose, we chose the 21 consumption sites. The results are as follows. First of all, large jar coffins(relics, 47.6% of total) in the Yeongsan River basin were located in Sampo stream basin, almost all of them were located within the Yeongsan River main stream basin and Sampo stream basin. Also, distance from all consumption site to river was within about 2km. Therefore, it is thought that the all consumption sites are located at the place of the gift of nature that was very favorable to water transport of jar coffins. The results of this study may be used as basic data for research of cultural relics in the Yeongsan river basin.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.156-156
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• 2019

In this study, a gated recurrent unit (GRU) network is constructed based on a deep neural network (DNN) with the aim of restoring the missing daily flow data in river basins. Lai Chau hydrological station is located upstream of the Da river basin (Vietnam) is selected as the target station for this study. Input data of the model are data on observed daily flow for 24 years from 1961 to 1984 (before Hoa Binh dam was built) at 5 hydrological stations, in which 4 gauge stations in the basin downstream and restoring - target station (Lai Chau). The total available data is divided into sections for different purposes. The data set of 23 years (1961-1983) was employed for training and validation purposes, with corresponding rates of 80% for training and 20% for validation respectively. Another data set of one year (1984) was used for the testing purpose to objectively verify the performance and accuracy of the model. Though only a modest amount of input data is required and furthermore the Lai Chau hydrological station is located upstream of the Da River, the calculated results based on the suggested model are in satisfactory agreement with observed data, the Nash - Sutcliffe efficiency (NSE) is higher than 95%. The finding of this study illustrated the outstanding performance of the GRU network model in recovering the missing flow data at Lai Chau station. As a result, DNN models, as well as GRU network models, have great potential for application within the field of hydrology and hydraulics.