• 상하수도학회지
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• 제25권4호
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• pp.453-462
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• 2011

This study was analyzed to determine the cause of red tide at 10 and 30 days antecedental rainfall, stage and discharge in the Taehwa River, tidal data of Ulsan port, also, it was analyzed variation of red tide population, salinity, BOD, COD, T-N, T-P at S1, S2 each point. Most of the red tide in the Taehwa River occurred by provision of proper nutrients with antecedent, the proximity between discharge and low-flow capacity, and stage and discharge of stabilized condition after the sea water was inflowed by maximum tide difference. Red tide population is not nearly related to the change of salinity, the Taehwa River seems specific features of Non-coastal rivers downstream, because red tide was occurred when salinity quite low-end condition.

• 농업과학연구
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• 제46권4호
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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.

• 한국습지학회지
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• 제13권2호
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• pp.363-375
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• 2011

최근 하수관거 매설과 하수종말처리장 등의 건설로 인해 도시하천의 수질이 크게 개선되고 있으나, 하 폐수 처리시설의 부족으로 하천으로 유입되는 다양한 오염물질은 수중생태계 교란과 적조를 발생시키고 있다. 울산 태화강의 하류부는 하폭이 크고 하상경사가 매우 완만한 감조하천으로써 갈 저수기에는 유속이 매우 느리게 된다. 게다가 약 1.2km 떨어져 있는 2개 교량의 전면기초로 된 교각보호공으로 인해 흐름이 정체되고 오염물질이 퇴적되어, 수년전부터 발생하고 있는 태화강 적조의 원인이 되거나 적조를 활성화 시키고 있다. 본 연구에서는 RMA2모형을 이용하여 두 교량의 교각을 독립기초로 개선하고 흐름을 소통시킬 수 있는 통수단면을 확보했을 때, 수리학적으로 개선될 수 있는 유속과 유량의 변화폭을 모의하였다. 그 결과 유속은 최대 약 103%, 유량은 최대 61% 증가하는 것으로 나타났다. 이는 적조를 직접 소멸시킬 수는 없으나 적조발생을 억제하거나 감소시킬 수 있으며, 오염물질의 퇴적을 막아 적조발생 원인을 근원적으로 제어할 수 있는 것으로 판단된다.

• Journal of Ecology and Environment
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• 제33권3호
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• pp.237-244
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• 2010

In this study, we evaluated the distribution, habitat characteristics, and diet of two Korean freshwater turtle species (Chinemys reevesii, Pelodiscus sinensis) and an invasive turtle species (Trachemys scripta elegans) in the area surrounding the Seomjin River and the Nam River. We surveyed basking turtles in multiple locations along a 48-km stretch of the Seomjin River and in 99 reservoirs distributed along the Seomjin and Nam rivers from June to September, 2009. We observed 8 and 6 red-eared turtles in 3 reservoirs and at 3 sites in the Seomjin River, respectively, and 33 Reeve's turtles in 9 reservoirs. There were also 28 and 16 mud turtles detected at 15 sites along the Seomjin River and in 8 reservoirs, respectively. Among the 14 biotic and abiotic habitat characteristics that might influence the abundance of freshwater turtles in reservoirs, only the distance between a reservoir and the nearest residential areas was correlated negatively with abundance. With regard to the diet, all Reeve's and red-eared turtles investigated were determined to forage on water snails. Some turtles also foraged on vegetation and aquatic invertebrates. Additionally, we found fish in the stomach of one of the Reeve's turtles, and dragonflies in the stomachs of two red-eared turtles.

• Geomechanics and Engineering
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• 제34권2호
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• pp.139-154
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• 2023

Red-bed soft rock is a common stratum and it is necessary to evaluate the mechanical properties and bearing capacity of red-bed soft rock mass affected by different environmental effects. This paper presents a complete procedure for evaluating the bearing capacity of red-bed soft rock by means of geophysical exploration and in-situ rock mechanics tests. Firstly, the thickness of surface loosened rock mass of red-bed soft rock was determined using geophysical prospecting method. Then, three environmental effects, including natural weathering effect, dry-wet cycling effect and concrete sealing effect, were considered. After each effect lasted for three months, in-situ rock mass mechanical tests were conducted. The test results show that the mechanical properties of rock mass considering the sealing effect of concrete were maintained. After considering the natural weathering effect, the mechanical parameters decrease to a certain extent. After considering the effect of dry-wet cycling, the decreases of mechanical parameters are the most significant. The test results confirm that the red-bed soft rock dam foundation rock mass will be significantly affected by various environmental effects. Therefore, combined with the mechanical test results, some useful implementations are proposed for the construction of a red-bed soft rock dam foundation.

• 한국환경과학회지
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• 제18권10호
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• pp.1155-1162
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• 2009

To investigate the influence of water area calmness on the red tide development, runoff phenomena due to antecedent precipitation of red tide development day were analyzed. There were examined the water quality variation properties at about the same time of the red tide develop. The red tide was developed when the stage and discharge nearly had not changed. It was estimated that the stability of particle behavior in the mouth of river effected on the red tide develop. Also, the concentrations of $COD_{Mn}$ were increased about 241~629% when the red tide developed.

• The Journal of Asian Finance, Economics and Business
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• 제9권5호
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• pp.389-396
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• 2022

Tourism has always played an important role in socio-economic development in Vietnam and in many countries around the world. Tourism is also an industry that has attracted a large number of workers in the past two decades in Vietnam in general and territories in particular. Over the past two decades, tourism in the southern Red River sub-region has created thousands of jobs for local workers and neighboring provinces. The study aims to examine the factors affecting the employment decision of workers in the tourism industry in the South Red River sub-region. Using a pilot study surveying 10 workers in three provinces to adjust the questionnaire and a sample data of 193 observations were performed. The experimental results prove that the independent variables explain 64% of the variation of the dependent variable, and other reasons can explain the rest (36%). Research results show that four factors, namely, welfare (WE), working conditions (IN), the potential for tourism development (POT), and development policy (POL) have a positive impact on the employment decision of workers. Meanwhile, the two factors that are tourism cooperation (CO)and Education (EDU), have an insignificant impact on the employment decision of workers in the southern Red River sub-region.

• 자원환경지질
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• 제55권2호
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• pp.127-135
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• 2022

Excessive presence of As level in groundwater is a major health problem worldwide. In the Red River Delta in Vietnam, several million residents possess a high risk of chronic As poisoning. The As releases into groundwater caused by natural process through microbially-driven reductive dissolution of Fe (III) oxides. It has been extracted by Red River residents using private tube wells for drinking and daily purposes because of their unawareness of the contamination. This long-term consumption of As-contaminated groundwater could lead to various health problems. Therefore, a predictive model would be useful to expose contamination risks of the wells in the Red River Delta Vietnam area. This study used four machine learning algorithms to predict the As probability of study sites in Red River Delta, Vietnam. The GBM was the best performing model with the accuracy, precision, sensitivity, and specificity of 98.7%, 100%, 95.2%, and 100%, respectively. In addition, it resulted the highest AUC of 92% and 96% for the PRC and ROC curves, with Eh and Fe as the most important variables. The partial dependence plot of As concentration on the model parameters showed that the probability of high level of As is related to the low number of wells' depth, Eh, and SO₄, along with high PO₄^3- and NH₄⁺. This condition triggers the reductive dissolution of iron phases, thus releasing As into groundwater.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.131-131
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• 2021

Deep learning models, especially those based on long short-term memory (LSTM), have presented their superiority in addressing time series data issues recently. This study aims to comprehensively evaluate the performance of deep learning models that belong to the supervised learning category in streamflow prediction. Therefore, six deep learning models-standard LSTM, standard gated recurrent unit (GRU), stacked LSTM, bidirectional LSTM (BiLSTM), feed-forward neural network (FFNN), and convolutional neural network (CNN) models-were of interest in this study. The Red River system, one of the largest river basins in Vietnam, was adopted as a case study. In addition, deep learning models were designed to forecast flowrate for one- and two-day ahead at Son Tay hydrological station on the Red River using a series of observed flowrate data at seven hydrological stations on three major river branches of the Red River system-Thao River, Da River, and Lo River-as the input data for training, validation, and testing. The comparison results have indicated that the four LSTM-based models exhibit significantly better performance and maintain stability than the FFNN and CNN models. Moreover, LSTM-based models may reach impressive predictions even in the presence of upstream reservoirs and dams. In the case of the stacked LSTM and BiLSTM models, the complexity of these models is not accompanied by performance improvement because their respective performance is not higher than the two standard models (LSTM and GRU). As a result, we realized that in the context of hydrological forecasting problems, simple architectural models such as LSTM and GRU (with one hidden layer) are sufficient to produce highly reliable forecasts while minimizing computation time because of the sequential data nature.

• 대한원격탐사학회지
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• 제37권3호
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• pp.419-430
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• 2021

신속하게 면단위(2-Dimension)로 하천의 녹조 우심지역을 관측하기 위해 드론 다중분광영상을 이용한 분석기법을 연구하였다. 드론은 항공기나 위성에 비해 관측 면적이 작지만 높은 공간해상도와 현장접근 및 데이터 획득 용이성, 대기에 의한 간섭 저감, 다중분광센서를 이용한 신속한 자료처리로 녹조 모니터링 효율성을 향상시킬 수 있을 것으로 보고 있다. 이러한 드론의 다중분광센서 영상으로 하천의 녹조발생 현황을 모니터링하기 위해 광합성 색소에 의한 분광특성을 반영하는 분광지수들을 비교 분석하고 Chlorophyll-a(Chl-a) 농도 추정식에 적용 가능성을 평가하였다. 주로 Red-edge 밴드를 포함하는 분광지수가 Chl-a 농도와 높은 상관성을 보였는데, 그 중에서도 3-Band Model(3BM), Normalized Difference Chlorophyll Index(NDCI)가 각각 비슷한 수준으로 가장 높은 통계적 유의성(R²=0.86, RMSE=7.5)을 나타내었다. NDCI의 경우에는 두 개의 분광밴드만 적용되는 간결한 수식과 정규화된(Normalizing) 결과값으로 신속하고 표준화된 프로세싱이 가능할 것으로 보이며 드론 녹조 모니터링에서 적용성이 높을 것으로 기대된다. 3BM의 경우에는 Red-edge 영역에서 두 개의 파장대를 적용해야 하나 본 연구에서 사용한 드론 센서에는 한 개의 Red-edge 밴드만 포함되어서 근적외선으로 대체하여 수식을 적용하였는데 Red-edge 파장영역이 세밀한 분광센서를 활용할 경우에 NDCI 보다 높은 정확도를 나타낼 수 있을 것으로 보인다.