• The Korean Journal of Quaternary Research
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• v.22 no.2
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• pp.43-44
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• 2008

In this brief present are about some advanced results from investigation geomorphology in basin of river Tui and distribution of relief their peculiarities, types morphogenetic, convert to ekzogen process of relief ( fluvial system, permafrost process, wind process, slope process etc) and dynamic process of sedimentation.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09b
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• pp.1359-1360
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• 2005

• Proceedings of the Korea Water Resources Association Conference
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• 1995.05a
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• pp.378-383
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• 1995

• Proceedings of the KSEEG Conference
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• 2002.04a
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• pp.91-91
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• 2002

• Journal of Korea Water Resources Association
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• v.53 no.7
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• pp.469-480
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• 2020

This study analyzed the differences of mean basin precipitation between TM and radar based on the 51 standard basins in Han river and Nakdong river when large scale of heavy rains occurred in 2018. The result shows that the differences between radar and TM are -65.05 ~ 26.09% and -82.00 ~ 3.80% for accumulated and 10 min. maximum mean basin precipitation, respectively. The correlation analysis between the differences of estimated mean basin precipitation and basin characteristics such as average altitude of basin, area of basin, and shape factor of basin presents that there is no clear correlation between them. And the differences of point precipitation also shows the similar tendency with those of mean basin precipitation. In order to find out the correlation between them and meteorological conditions such as rainfall patterns, the reflectivity of radars according to the observation angles is analyzed at the selected basins, and then it is found that the differences of mean basin precipitation between TM and radar is more dominated by the meteorological conditions than by the topographic conditions such as basin characteristics.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09b
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• pp.985-986
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• 2005

• Water for future
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• v.19 no.4
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• pp.329-338
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• 1986

the purpose of this study is to estimate the storage deficit by Run's Characteristics of (-)Run-length and (-)Run-sum. Runoff data are obtained from the guaging stations of Y대-Ju in Hanriver Basin, Wae-Gwan in Nak Dong River Basin and Gyo Am in Geum River Basin. In order to estimate the storage deficit, runhydrographs are established with each return period of 10, 30, ......, 200 years and regression equation is derived from relationship between (-) run-length and storage deficit. From the comparison of estimated reservoir storage with observed values., it was proved that this suggested method can be used for the estimation of the storage deficit.

• Journal of Korea Water Resources Association
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• v.45 no.10
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• pp.1023-1034
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• 2012

Linear regression technique has been used widely in water resources field as well as various fields such as economics and statistics, and so on. Using fuzzy regression technique, it is possible to quantify uncertainty and reflect them to the regression model. In this study, fuzzy regression model is developed to compute design floods in any place in Pyeongchang River basin. In ungaged basins, it is usually difficult to obtain data required for flood discharge analysis. In this study, basin characteristics elements are analyzed spatially using GIS and the technique of estimating design flood in ungaged mountainous basin is studied based on the result. Fuzzy regression technique is applied to Pyeongchang River basin which has mountainous basin characteristics and well collected rainfall and runoff data through IHP test basin project. Fuzzy design flood estimation equations are developed using the basin characteristics elements for Pyeongchang River basin. The suitability of developed fuzzy equations are examined by comparing the results with design floods computed in 9 locations along the river. Using regional regression method and fuzzy regression analysis, the uncertainties of the design floods occurred from the data monitoring can be quantified.

• Journal of Environmental Science International
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• v.22 no.12
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• pp.1615-1624
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• 2013

The aims of this study were to investigate and confirm the occurrence and distribution patterns of blood lipid lower agents (BLLAs) in Nakdong river basin (mainstream and its tributaries). 4 (atorvastatin, lovastatin, mevastatin and simvastatin) out of 5 statins and 2 (clofibric acid and zemfibrozil) out of 3 fibrates were detected in 29 sampling sites and simvastatin (>50%) was predominant compound followed by atorvastatin, lovastatin and clofibric acid. The total concentration levels of BLLAs on April, August and November 2009 in surface water samples ranged from ND~25.7 ng/L, ND~18.8 and ND to 38.8 ng/L, respectively. The highest concentration level of BLLAs in the mainstream and tributaries in Nakdong river were Goryeong and Jincheon-cheon, respectively. The sewage treatment plants (STPs) along the river affect the BLLAs levels in river and the BLLAs levels decreased with downstream because of dilution effects.

• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.4
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• pp.51-57
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• 2024

This study was conducted on how to simulate runoff, which was done using existing physical models, using an LSTM (Long Short-Term Memory) model based on deep learning. Tancheon, the first tributary of the Han River, was selected as the target area for the model application. To apply the model, one water level observatory and four rainfall observatories were selected, and hourly data from 2020 to 2023 were collected to apply the model. River water level of the outlet of the Tancheon basin was simulated by inputting precipitation data from four rainfall observation stations in the basin and average preceding 72-hour precipitation data for each hour. As a result of water level simulation using 2021 to 2023 data for learning and testing with 2020 data, it was confirmed that reliable simulation results were produced through appropriate learning steps, reaching a certain mean absolute error in a short period time. Despite the short data period, it was found that the mean absolute percentage error was 0.5544~0.6226%, showing an accuracy of over 99.4%. As a result of comparing the simulated and observed values of the rapidly changing river water level during a specific heavy rain period, the coefficient of determination was found to be 0.9754 and 0.9884. It was determined that the performance of LSTM, which aims to simulate river water levels, could be improved by including preceding precipitation in the input data and using precipitation data from various rainfall observation stations within the basin.