• Journal of Korean Society of Water and Wastewater
• /
• v.25 no.4
• /
• pp.453-462
• /
• 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.

• Korean Journal of Agricultural Science
• /
• v.46 no.4
• /
• pp.843-856
• /
• 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.

• Journal of Wetlands Research
• /
• v.13 no.2
• /
• pp.363-375
• /
• 2011

Recently, Water quiality of urban river largely have gotten better by virtue of sewer pipe laying and sewage treatment plants construction. or the various contaminants which is flowed in into river have generated underwater ecosystem disturbance and red tide by lack of sewage and waste water disposal facilities. With tidal river, taehwa river of ulsan metropolitan city has large river width and gradual stream bed gradient at the dry and storage period. Moreover, the flow is paralyzed due to the bridge pier protection work, consist of the mat foundation which is about 1.2km from two bridge and the contaminant is accumulated. it is caused by of the red tide generated from the several years or it activates. In this study, When flow area is largest by changing independent footing of bridge pier of two bridges and using RMA2 model, we hydraulically analyzed a variable breadth of velocity and discharge. Consequently, flow rate increased the maximum 103%, discharge was exposed to increase the maximum 61%. Directly this cannot extinguish the red tide but suppresses the red tide occurrence or can reduce. And it is determined to prevent the depositioning of the contaminant and can control fundamentally the red tide occurrence cause.

• Journal of Ecology and Environment
• /
• v.33 no.3
• /
• pp.237-244
• /
• 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
• /
• v.34 no.2
• /
• pp.139-154
• /
• 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.

• Journal of Environmental Science International
• /
• v.18 no.10
• /
• pp.1155-1162
• /
• 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
• /
• v.9 no.5
• /
• pp.389-396
• /
• 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.

• Economic and Environmental Geology
• /
• v.55 no.2
• /
• pp.127-135
• /
• 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.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.131-131
• /
• 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.

• Korean Journal of Remote Sensing
• /
• v.37 no.3
• /
• pp.419-430
• /
• 2021

Remote sensing techniques using drone-based multispectral image were studied for fast and two-dimensional monitoring of algal blooms in the river. Drone is anticipated to be useful for algal bloom monitoring because of easy access to the field, high spatial resolution, and lowering atmospheric light scattering. In addition, application of multispectral sensors could make image processing and analysis procedures simple, fast, and standardized. Spectral indices derived from the active spectrum of photosynthetic pigments in terrestrial plants and phytoplankton were tested for estimating chlorophyll-a concentrations (Chl-a conc.) from drone-based multispectral image. Spectral indices containing the red-edge band showed high relationships with Chl-a conc. and especially, 3-band model (3BM) and normalized difference chlorophyll index (NDCI) were performed well (R²=0.86, RMSE=7.5). NDCI uses just two spectral bands, red and red-edge, and provides normalized values, so that data processing becomes simple and rapid. The 3BM which was tuned for accurate prediction of Chl-a conc. in productive water bodies adopts originally two spectral bands in the red-edge range, 720 and 760 nm, but here, the near-infrared band replaced the longer red-edge band because the multispectral sensor in this study had only one shorter red-edge band. This index is expected to predict more accurately Chl-a conc. using the sensor specialized with the red-edge range.