• Korean Journal of Environmental Agriculture
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• v.21 no.4
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• pp.243-247
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• 2002

Irrigation water quality along Donjin river watershed was monitored to find a possible pollutant, for maintaining water quality to achieve food safety through water quality preservation of river. As a pollution indicators, such as Biological Oxygen Demand(BOD), Chemical Oxygen Demand(COD), Total Nitrogen(T-N), and Total Phosphate(T-P) in Dongjin river were examined from May to November in 2001. The results were as follows : The BOD level of Dongjin river ranged from 2.84 to 6.45 mg/L, which would be in a II$\sim$IV grade of the potable water criteria by Ministry of Environment. Averaged BOD level of downstream DJ6(After Jeongupcheon confluence) was 4.07 mg/L. The average COD level of Dongjin river ranged from 11.20 to 32.96 mg/L. COD level of DJ6 rapidly increased rapidly after the junction of Dongjin river and Jungupcheon because it showed the latter had relatively high pollution level. T-N content were significantly high in all sites of Dongjin river ranged through 4.16 to 5.84 mg/L. T-P examined high concentration than another thing point by 0.19 mg/L after Jeongupcheon confluence as BOD and COD. COD of main stream was expressed high concentration to dry season after rainy season. In case of T-P, pollution degree of dry season before rainy season appeared and examined that quality of water was worsened go by dry season after rainy season. The water quality of Dongjin river was deteriorated with inflow of Jungupcheon polluted by municipal and industrial sites near Jungup city.

• Journal of the Korean Geographical Society
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• v.28 no.1
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• pp.1-15
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• 1993

This research aims to investigate some respects of chemical weathering processes, espcially the amount of solute leaching, formation of clay minerals, and the chemical weathering rate of granite rocks under present climatic conditions. For this purpose, I investigated geochemical mass balance in a small catchment and the mineralogical composition of weathered bedrocks including clay mineral assemblages at four res-pective sites along one slope. The geochemical mass blance for major elements of rock forming minerals was calculated from precipitation and streamwater data which are measured every week for one year. The study area is a climatically and litholo-gically homogeneous small catchment($3.62Km^2$)in Anyang-shi, Kyounggi-do, Korea. The be-drock of this area id Anyang Granite which is composed of coarse-giained, pink-colored miner-als. Main rock forming minerals are quartz, K-Feldspar, albite, and muscovite. One of the chracteristics of this granite rock is that its amount of Ca and Mg is much lower than other granite rock. The leaching pattern in the weathering profiles is in close reltion to the geochemical mass balance. Therefore the removal or accumulation of dissolved materials shows weathering patterns of granite in the Korean peninsula. Oversupplied ions into the drainage basin were $H^+$, $K^+$, Fe, and Mn, whereas $Na^2+$, $Mg^2+$, $Ca^2+$, Si, Al and $HCO-3^{-}$ were removed from the basin by the stream. The consumption of hydrogen ion in the catchment implies the hydrolysis of minerals. The surplus of $K^+$ reflects that vegetation is in the aggravation stage, and the nutrient cycle of the forest in study area did not reach a stable state. And it can be also presumed that the accumulation of $K^+$ in the top soil is related to the surplus of $K^+$. Oversupplied Fe and Mn were presumed to accumulate in soil by forming metallic oxide and hydroxide. In the opposite, the removal of $Na^+$, Si, Al resulted from the chemical weathering of albite and biotite, and the amount of removal of $Na^+$, Si, Al reflected the weathering rate of the bedrock. But $Ca^2+$ and $Mg^2+$ in stream water were contaminated by the scattered calcareous structures over the surface. Kaolinite is a stable clay mineral under the present environment by the thermodynamical analysis of the hydrogeochemical data and Tardy's Re value. But this result was quite different from the real assemblage of clay miner-als in soil and weathered bedrock. This differ-ence can be explained by the microenvironment in the weathering profile and the seasonal variation of climatic factors. There are different clay forming environments in the stydy area and these differences originate from the seasonal variation of climate, especially the flushing rate in the weathering profile. As it can be known from the results of the analysis of thermodynamic stability and characteristics of geochemical mas balance, the climate during winter and fall, when it is characterized by the low flushing rate and high solute influx, shows the environmental characteristics to from 2:1 clay minerals, such as illite, smectite, vermiculite and mixed layer clay minerals which are formed by neoformation or transformation from the primary or secondary minerals. During the summer and spring periods, kaoli-nite is a stable forming mineral. However it should consider that the other clay minerals can transformed into kaolinite or other clay minerals, because these periods have a high flushing rte and temperature. Materials which are directly regulated by chemical weathering in the weathered bedrock are $Na^+$, Si, and Al. The leaching of Al is, however, highly restricted and used to form a clay mineral, and that of Si falls under the same category. $Na^+$ is not taked up by growing veget ation, and fixed in the weathering profile by forming secondary minerals. Therefore the budget of $Na^+$ is a good indicator for the chemical weathering rate in the study area. The amount of chemical weathering of granite rocks was about 31.31g/$m^2+$/year based on $Na^+$ estimation.

• Korean Journal of Environment and Ecology
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• v.20 no.4
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• pp.415-424
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• 2006

This study was carried out to analyze the characteristics of forest watershed factors by using the quantification theory(I) for evaluation and prediction of the failure hazard area. Present $sediment(m^3/yr/ha)$ of erosion control dams were investigated in 95 sites of erosion control dam constructed during 1986 to 1999 in Gyeongnam province. The results obtained from this study were summarized as follows; General condition of class I(Very hazard area) were as follow; Igneous rock in parent rock, coniferous in forest type, below 20year in stand age, below 30cm in soil depth, SCL in soil texture, $31{\sim}40%$ in gravel contents, $S{\sim}E$ in aspect, $2,501{\sim}3,600m$ in length of main stream, $26{\sim}30$ in number of total streams, $6,601{\sim}10,000m$ in length of total streams, over 3 in stream order, over 16 in number of first streams order and over $31^{\circ}$ of slope gradient. General condition of class IIl(hazard area) were as follow; Metamorphic rock in parent rock, hardwood in forest type, over $21{\sim}24year$ in stand age, $31{\sim}40cm$ in soil depth, SiCL or SCL in soil texture, $11{\sim}20%$ in gravel contents, $S{\sim}W$ in aspect, $1,501{\sim}2,600m$ in length of main stream, $6{\sim}10$ in number of total streams, $3,501{\sim}5,500m$ in length of total streams, 2 in stream order, $6{\sim}10$ in number of first streams order and over $31^{\circ}$ of slope gradient. General condition of class III(Un hazard area) were as follow; Sedimentary rock in parent rock, mixed in forest type, over 25year in stand age, $41{\sim}50cm$ in soil depth, SiCL in soil texture, below 10% in gravel contents, $N{\sim}W$ in aspect, below 500m in length of main stream, below 5 in number of total streams, below 1,000m in length of total treams, below 1 in stream order, below 2 in number of first streams order and below $25^{\circ}$ of slope gradient. The prediction method of suitable for failure hazard area divided into class I, II, and III for the convenience of use. The score of class I evaluated as a very hazard area was over 4.8052. A score of class II was 4.8051 to 2.5602, it was evaluated as a hazard area, and class III was below 2.5601, it was evaluated as a un hazard area.

• Journal of Wetlands Research
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• v.25 no.4
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• pp.306-314
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• 2023

Stream sediments are an important component of water quality management because they are receptors of various pollutants such as heavy metals and organic matters emitted from upland sources and can be secondary pollution sources, adversely affecting water environment. To effectively manage the stream sediments, identification of primary sources of sediment contamination and source-associated control strategies will be required. We evaluated the performance of machine learning models in identifying primary sources of sediment contamination based on the physico-chemical properties of stream sediments. A total of 356 stream sediment data sets of 18 quality parameters including 10 heavy metal species(Cd, Cu, Pb, Ni, As, Zn, Cr, Hg, Li, and Al), 3 soil parameters(clay, silt, and sand fractions), and 5 water quality parameters(water content, loss on ignition, total organic carbon, total nitrogen, and total phosphorous) were collected near abandoned metal mines and industrial complexes across the four major river basins in Korea. Two machine learning algorithms, linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the sediments into four cases of different combinations of the sampling period and locations (i.e., mine in dry season, mine in wet season, industrial complex in dry season, and　industrial complex in wet season). Both models showed good performance in the classification, with SVM outperformed LDA; the accuracy values of LDA and SVM were 79.5% and 88.1%, respectively. An SVM ensemble model was used for multi-label classification of the multiple contamination sources inlcuding landuses in the upland areas within 1 km radius from the sampling sites. The results showed that the multi-label classifier was comparable performance with sinlgle-label SVM in classifying mines and industrial complexes, but was less accurate in classifying dominant land uses (50~60%). The poor performance of the multi-label SVM is likely due to the overfitting caused by small data sets compared to the complexity of the model. A larger data set might increase the performance of the machine learning models in identifying contamination sources.

• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.35-44
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• 2024

Research in dam inflow prediction has actively explored the utilization of data-driven machine learning and deep learning (ML&DL) tools across diverse domains. Enhancing not just the inherent model performance but also accounting for model characteristics and preprocessing data are crucial elements for precise dam inflow prediction. Particularly, existing rainfall data, derived from snowfall amounts through heating facilities, introduces distortions in the correlation between snow accumulation and rainfall, especially in dam basins influenced by snow accumulation, such as Soyang Dam. This study focuses on the preprocessing of rainfall data essential for the application of ML&DL models in predicting dam inflow in basins affected by snow accumulation. This is vital to address phenomena like reduced outflow during winter due to low snowfall and increased outflow during spring despite minimal or no rain, both of which are physical occurrences. Three machine learning models (SVM, RF, LGBM) and two deep learning models (LSTM, TCN) were built by combining rainfall and inflow series. With optimal hyperparameter tuning, the appropriate model was selected, resulting in a high level of predictive performance with NSE ranging from 0.842 to 0.894. Moreover, to generate rainfall correction data considering snow accumulation, a simulated snow accumulation algorithm was developed. Applying this correction to machine learning and deep learning models yielded NSE values ranging from 0.841 to 0.896, indicating a similarly high level of predictive performance compared to the pre-snow accumulation application. Notably, during the snow accumulation period, adjusting rainfall during the training phase was observed to lead to a more accurate simulation of observed inflow when predicted. This underscores the importance of thoughtful data preprocessing, taking into account physical factors such as snowfall and snowmelt, in constructing data models.

• Journal of Wetlands Research
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• v.13 no.3
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• pp.535-545
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• 2011

This study was carried out to suggest the baseline data necessary for vegetation restoration by naturalness assessment of riverine wetland within stream corridor. We selected stream reach both of near nature and urbanized by Nonsan stream and Hongchun river as experimental site. Composition of vegetation community and land use pattern between two sites indicated considerable difference, which imply for many different watershed property and process disturbed each other at river ecosystem. Naturalness of the sampled reaches showed that near nature is in better condition for riverine wetland than urbanized of all two sites. However, the prevalence index of Hongchun river within its natural state was lower than that of Nonsan stream, because the index included some vegetation communities occurred at upland fringe and bank slope. In conclusion assessment system using prevalence index would be considered an effective method for evaluating of natural states of riverine wetland.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.31 no.1
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• pp.74-83
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• 1995

To estimate the parameters of a mathematical model of fishes' swimming behavior, the behavior in a experimental water tank was observed and analyzed using the video monitoring system. The tank was equipped with vertical circulation system, and measured $3,500L\;{\times}\;1,500B\;{\times}\;1,000H\;mm$ at flow channel and $1,200L\;{\times}\;900B\;{\times}\;500H\;mm$ at observational part. Rainbow trout, salmo gairdnerii were used as experimental fishes. Their swimming behavior in the tank was observed by the monitoring system, and the positions of every individual were checked at 0.5 second intervals by the image processing of recorded pictures for 5 minutes. The mean swimming speed calculated from the time series data of positions of every individual ranged from 2.5BL cm/sec to 2.9BL cm/sec at the stagnated flow. The mean swimming speed of 10 individuals in a school increased according to the flow speed. The mean swimming depth ranged from 17 cm to 38 cm even though it changed irregularly at the stagnated flow and gradually became stable according to the increase of flow speed. In the present study, the mean distance of individuals from wall of the tank varied from 17.6cm to 21.4cm. The mean distance between the nearest individual varied from 0.4BL cm to 0.7BL cm when 10 individuals in a school were observed. The mean dimension of fish schools became enlarged in all directions according to increase in the number of individuals, and as flow speed increased the horizontal dimension of fish schools expanded while their vertical dimension decreased.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.3 no.3
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• pp.165-169
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• 1998

In August 1996, seawater salinity and nutrient distribution were investigated at surface waters in the South Sea of Korea. The low-salinity (< 20.00 psu) waters were observed in the western and southwestern offshore areas of Cheju Island. Relatively low saline (< 30.0 psu) waters occupied most of the survey areas only except in the eastern part. The observed minimum salinity was lower by 11.78 psu than that of the average between 1963 and 1995. The low saline waters appeared in the upper layer of generally 10-20 m deep, and were obriously distinguished from high-salinity (> 32.00 psu) waters, 30 m deep. The low saline waters may originate from the freshwater discharge of vast amount of from Yangtze River during the heavy rainfall season in China. Phosphate concentrations in the surface waters were relatively low and were less variable than those of nitrate and silicate. The maximum concentrations of nitrate and silicate occured in the western and southwestern offshore areas of Cheju Island, where the salinities were the lowest. The concentrations of nitrate and silicate were inversely correlated with salinity, whereas that of phosphate showed a considerable scatter and non-conservative behaviours. This indicates extensive desorption reactions of suspended materials releasing phosphate.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.287-287
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• 2011

모니터링 자료의 부족으로 인하여 다양한 토지이용에서 발생하는 비점오염물질의 관리에 어려움을 겪고 있는 실정이다. 현재 환경부에서는 현행 토지계의 원단위를 세부적으로 분류하여 재산정하기 위하여 지목별로 장기 모니터링이 수행되고 있다. 특히, 산림 지역의 경우 도시 및 축산지역에 비하여 강우유출수의 농도는 낮더라도 유량적인 측면에 보았을 때 전체 수계에 대한 부하량 기여도는 매우 높다고 볼 수 있다. 따라서 본 연구는 장기모니터링의 일환으로 산림지역에 대한 비점오염물질 유출 특성을 파악하기 위하여 모니터링 및 분석을 실시하였으며, 이러한 결과는 향후 비점오염원 평가기반을 마련하고자 한다. 본 연구는 활엽수지역을 대상으로 2010년 4월부터 10월까지 총 16회에 걸쳐 모니터링이 수행되었으며, 시료의 성분 변화를 막기 위해 냉장기능이 있는 자동채수기를 이용하여 시료를 채취하였다. 수질분석항목은 BOD, COD, DOC, SS, T-N, $NO_3$-N, $NH_3$-N, T-P, $PO_4$-P로 총 9가지 항목을 분석하였다. 강우사상에 대한 모니터링 결과, 총강우량은 7.0~76.5mm, 강우지속시간은 1~30hr, 평균 강우강도는 0.88~18.50mm/hr의 범위를 보이고 있으며, EMC(Event Mean Concentration, 유량가중평균농도)결과 BOD는 0.4~2.4mg/L, T-N은 1.156~14.777mg/L, T-P는 0.009~0.562mg/L인 것으로 나타났으며, SS는 1.8~71.9mg/L 로 비교적 높은 값을 나타내는 것으로 분석되었다. 농도 변화 및 유출경향의 패턴을 볼 때, 유량이 증가함에 따라 농도도 점점 증가하여 첨두유량이 발생된 후 감소하는 경향을 나타내는 것으로 분석되었다. 또한 우리나라의 경우, 시험유역을 대개 산지 소유역에 설치하는 경우가 많아서 일반적으로 지연시간이 짧은 경우가 많기 때문에 이 지역 역시 강우가 내린 후 계류유출량의 증가에 영향을 주는 강우의 유출속도는 비교적 빠른 것으로 나타났다. 그리고 단기 수문곡선상에서 강우량이 많을 시 유출이 빠르게 일어나 첨두 유량에 도달하는 시간이 짧고, 강우량이 적을 시에는 첨두 유량의 출현시간이 늦어지는 것을 볼 수 있었다.

• Journal of Wetlands Research
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• v.13 no.1
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• pp.149-160
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• 2011

This study developed a HSI for the creatures in Hongcheon River in order to analyze the conditions proper for habitats. For the index, the investigator identified a total of seven items encompassing hydraulic characteristics such as flow velocity and water depth, and water quality characteristics such as water temperature, BOD, DO, TN, and TP. The subject river was simulated, inspected, and revised with a two-dimensional river model (RMA-2) and water quality model (QUAL2E). Using GIS, the developed index was divided by section by reflecting river characteristics and compared and analyzed with the statistics. The river was divided into a total of 29 reaches by reflecting the basic characteristics and the features of the hydraulic coefficient on the cross-sections of the river. According to the analysis results, the fish scored the highest mean of the overall habitat suitability index of 0.769 at reach 27. Each of the variables had the following mean values: 0.122 m/s for flow velocity, 0.782m for water depth, $14.3^{\circ}C$ for water temperature, 0.68 mg/l for BOD, 10.3 mg/l for DO, 2.4 mg/l for TN, and 0.0121mg/l for TP.