• Journal of Korea Water Resources Association
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• v.54 no.1
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• pp.49-60
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• 2021

This study presents a methodology to estimate two distinct fractal dimensions of natural river basin by using fractal tree concept. To this end, an analysis is performed on fractal features of a complete drainage network which consists of all possible drainage paths within a river basin based on the growth process of fractal tree. The growth process of fractal tree would occur only within the limited drainage paths possessing stream flow features in a river basin. In the case of small river basin, the bifurcation process of network is more sensitive to the growth step of fractal tree than the meandering process of stream segment, so that various bifurcation structures could be generated in a single network. Therefore, fractal dimension of network structure for small river basin should be estimated in the form of a range not a single figure. Furthermore, the network structures with fractal tree from this study might be more useful information than stream networks from a topographic or digital map for analysis of drainage structure on small river basin.

• Journal of Environmental Science International
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• v.21 no.6
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• pp.725-738
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• 2012

Recently, a variety of GIS-based tools enabling to generate topographic parameters for hydrologic and hydraulic researches have been developed. However, most of GIS-based tools are usually insufficient to estimate and visualize river channel slopes especially along the river network, which can be possibly utilized for many hydraulic equations such as Manning's formula. Many existing GIS-based tools have simply averaged cell-based slopes for the other advanced level of hydrologic units as likely as the mean watershed slope, thus that the river channel slope from the simple approach resulted in the inaccurate channel slope particularly for the mountain region where the slope varies significantly along the downstream direction. The paper aims to provide several more advanced GIS-based methodologies to assess the river channel slopes along the given river network. The developed algorithms were integrated with a newly developed tool named RiverSlope, which adapted theoretical formulas of river hydraulics to calculate channel slopes. For the study area, Han stream in the Jeju island was selected, where the channel slopes have a tendency to rapidly change the upstream near the Halla mountain and sustain the mild slope adjacent to watershed outlet heading for the ocean. The paper compared the simple slope method from the Arc Hydro, with other more complicated methods. The results are discussed to decide better approaches based on the given conditions.

• Geomechanics and Engineering
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• v.22 no.2
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• pp.133-142
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• 2020

The evolution of the mining-induced fracture network formed during longwall top coal caving (LTCC) has a great influence on the gas drainage, roof control, top coal recovery ratio and engineering safety of aquifers. To reveal the evolution of the mining-induced stress and fracture network formed during LTCC, the fracture network in front of the working face was observed by borehole video experiments. A discrete element model was established by the universal discrete element code (UDEC) to explore the local stress distribution. The regression relationship between the fractal dimension of the fracture network and mining stress was established. The results revealed the following: (1) The mining disturbance had the most severe impact on the borehole depth range between approximately 10 m and 25 m. (2) The distribution of fractures was related to the lithology and its integrity. The coal seam was mainly microfractures, which formed a complex fracture network. The hard rock stratum was mainly included longitudinal cracks and separated fissures. (3) Through a numerical simulation, the stress distribution in front of the mining face and the development of the fracturing of the overlying rock were obtained. There was a quadratic relationship between the fractal dimension of the fractures and the mining stress. The results obtained herein will provide a reference for engineering projects under similar geological conditions.

• 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.

• River and Culture
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• v.9 no.4
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• pp.105-109
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• 2013

• River and Culture
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• v.9 no.1
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• pp.121-125
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• 2013

• 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.

• Journal of Environmental Impact Assessment
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• v.30 no.2
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• pp.75-88
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• 2021

Six locations in the automated monitoring network at the Geum River Basin were selected forthis study. The water quality characteristics at two of the locations in the water quality monitoring network that were identical, or nearby, were examined, and their correlations were evaluated through statistical analysis. The results of the water quality analysis were converted to the water quality index and expressed in grades for comparison. For the data necessary for the study, public data from four years, from 2016-2019 were used and the evaluation parameters were water temperature, pH, EC, DO, TOC, TN, and TP. Results of the analysis showed that the water quality concentrations measured in the automated monitoring network and the water quality monitoring network differed in some measured values, but they tended to register variation in a specified ratio in most of the locations in the network. The analysis of the correlations of the parameters between the two monitoring networks found that water temperature, EC, and DO showed high correlations between the two monitoring networks. The TOC, TN, and TP showed high correlations, with a 0.7 or higher (correlation coefficient r), with the exception of some of the monitoring networks, although their correlations were lower than those of the basic parameters. The water quality index analysis showed that the water quality index values of the automated monitoring network and the water quality monitoring network were similar. The water quality index decreased and the pollution degree increased in the downstream direction, in both networks.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.147-147
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• 2018

In this article, we use an open source software library: TensorFlow, developed for the purposes of conducting very complex machine learning and deep neural network applications. However, the system is general enough to be applicable in a wide variety of other domains as well. The proposed model based on a deep neural network model, LSTM (Long Short-Term Memory) to predict the river water level at Okcheon Station of the Guem River without utilization of rainfall - forecast information. For LSTM modeling, the input data is hourly water level data for 15 years from 2002 to 2016 at 4 stations includes 3 upstream stations (Sutong, Hotan, and Songcheon) and the forecasting-target station (Okcheon). The data are subdivided into three purposes: a training data set, a testing data set and a validation data set. The model was formulated to predict Okcheon Station water level for many cases from 3 hours to 12 hours of lead time. Although the model does not require many input data such as climate, geography, land-use for rainfall-runoff simulation, the prediction is very stable and reliable up to 9 hours of lead time with the Nash - Sutcliffe efficiency (NSE) is higher than 0.90 and the root mean square error (RMSE) is lower than 12cm. The result indicated that the method is able to produce the river water level time series and be applicable to the practical flood forecasting instead of hydrologic modeling approaches.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.6
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• pp.127-134
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• 2012

Four river project in the South Korea contributes to solve flood damages and water shortages. Also, it has purpose for creating water ecosystem and improving the level of people' cultural leisure and quality of life through inducing water quality improvement and river restoration. It is necessary to monitor a variety of observing data in river areas among dozens to hundreds of kilometer for safe river administration. The 20th construction area of the four river project is located on Hapcheon areas, where wireless mesh network was installed to manage the basin. In the process of network construction, the characteristic of surrounding areas is considered about embodying secure service by investing the least expense. Besides, transmission environment analysis is performed such as LOS tests and reception level analysis, and transmission speed measurement to create safe service. Reception level in all places is confirmed among -55 dBm ~ -70 dBm, and data transmission speed proves more than 20 Mbps.