• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.6
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• pp.131-140
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• 2021

It is becoming more important to utilize reclaimed lands in South Korea, due to the increasing competition for its usage among different sectors. However, the high groundwater level and poor permeability are exposing them to deterioration by salinization. Salinization is difficult to predict because the pattern changes according to various characteristics of soil and groundwater. In this study, the capillary rising time was studied by the water content profile in the soil. The prediction equation of soil salinity was developed based on simulation result of the CHEMFLO model. to enable prediction considering various soil water content and groundwater level. The two terms constituting the equation showed the coefficients of determination of 0.9816 and 0.9824, respectively. Using the prediction equation of the study, the surface salinity can be easily predicted from the initial surface salinity and the salinity of the groundwater. In the future, more precise predictions will be possible with the results of studies on the hydraulic characteristics of various reclaimed soils, changes in water content profile by seasonal and climate events.

• Smart Media Journal
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• v.11 no.2
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• pp.31-38
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• 2022

Promptly predicting changes in the salinity in rivers is an important task to predict the damage to agriculture and ecosystems caused by salinity infiltration and to establish disaster prevention measures. Because machine learning(ML) methods show much less computation cost than physics-based hydraulic models, they can predict the river salinity in a relatively short time. Due to shorter training time, ML methods have been studied as a complementary technique to physics-based hydraulic model. Many studies on salinity prediction based on machine learning have been studied actively around the world, but there are few studies in South Korea. With a massive number of datasets available publicly, we evaluated the performance of various kinds of machine learning techniques that predict the salinity of the Nakdong River Estuary Basin. As a result, LightGBM algorithm shows average 0.37 in RMSE as prediction performance and 2-20 times faster learning speed than other algorithms. This indicates that machine learning techniques can be applied to predict the salinity of rivers in Korea.

• Journal of Plant Biology
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• v.33 no.2
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• pp.141-146
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• 1990

Germination and vegetative growth of Enteromorpha multiramosa Bliding from Pyoson, Cheju Island were investigated in laboratory under various combinations of temperature (5-$25^{\circ}C$) and salinity (8-48$^{\circ}C$). Percent level of germination was relatively high at all combinations of the two factors. The highest value among the combinations was revealed at 15$^{\circ}C$ and 32$\textperthousand$. Dry weight also was fairly high at all levels of combination with maximum value at 2$0^{\circ}C$ and 32$\textperthousand$. Analysis of variance for germination and growth was completed respectively and polynomial prediction models were constructed. F ratio revealed that all factors had a significant effect (p<0.001) on percentage of germination and dry weight, and their interactions also were significant (p<0.001), although the F ratio of interactions was far less than that for either the separate effect of temperature or salinity. Response surface of polynomial equation represented that temperature influenced less than salinity on germination, while it effected remarkably on vegetative growth, so the Enteromorpha multiramosa was kept to visible macrothalli from winter to spring in Cheju Island.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1023-1035
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• 2021

The electricity cost of a desalination facility was also predicted and reviewed, which allowed the proposed model to be incorporated into the future design of such facilities. Input data from 2003 to 2014 of the Korea Hydrographic and Oceanographic Agency (KHOA) were used, and the structure of the model was determined using the trial and error method to analyze as well as hyperparameters such as salinity and seawater temperature. The future seawater quality was estimated by optimizing the prediction model based on machine learning. Results indicated that the seawater temperature would be similar to the existing pattern, and salinity showed a gradual decrease in the maximum value from the past measurement data. Therefore, it was reviewed that the electricity cost for seawater desalination decreased by approximately 0.80% and a process configuration was determined to be necessary. This study aimed at establishing a machine-learning-based prediction model to predict future water quality changes, reviewed the impact on the scale of seawater desalination facilities, and suggested alternatives.

• Journal of The Korean Society of Agricultural Engineers
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• v.51 no.2
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• pp.29-34
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• 2009

A series of laboratory model tests and numerical analysis is performed to analyze characteristics of desalination and to predict a period of desalination for subsurface saturated soil in Saemanguem reclaimed area. The results show that quantity of desalination is small as salinity of water is increased. On the contrary, quantity of desalination is increased as salinity of soil is high. In order to decrease the salinity to 10 % of initial salinity of soil at depth of 2 m, it takes 11 years to desalinate the soil 50 m away from drainage ditch. For soil at depth of 1.5 m only 1 year to desalinate the soil near drainage ditch. Also, water head of 80 cm is required to desalinate to 10 % of initial salinity for 60 cm thick soil. Because the following results is based upon the Saemangeum soil, an application of this result for another field will be cautious. More research will be required on this matter.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.46 no.6
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• pp.941-947
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• 2013

To understand the relationship between various oceanographic factors and seaweed production, we examined the annual accumulated aquaculture production of Undaria pinnatifida with respect to water temperature, salinity, dissolved oxygen, current patterns and nutrients over 21 years (1990-2010) (this date range does not add up to over 21 years) along the coast of Busan, Korea. According to the results of the cross-correlation function, annual production of U. pinnatifida was closely related to the following conditions: low water temperature, low salinity, strong Tsushima Warm Current, and high concentrations of dissolved oxygen and nutrients. In this study, we also considered the Index of Oceanographic factors for U. pinnatifida (IOU) by computation of a simple equation. This index will be used for the prediction of U. pinnatifida aquaculture production off the coast of Busan.

• Journal of Ocean Engineering and Technology
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• v.22 no.2
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• pp.34-41
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• 2008

As a basic study for developing a forecasting/estimating system that predicts water quality changes when Deep Sea Water (DSW) drains to the ocean after using it, this study was carried out as follows: 1) numerical simulation of the present state at DSW developing region in the East sea using SWEM, 2) numerical prediction of water quality changes by effluent DSW, 3) analysis of influence degree 'With defined DEI (DSW effect index) at F station. On the whole, when DSW drained to the ocean, Chl-a, COD and water-temperature were decreased and DIN, DIP and DO were increased by effluent DSW, and Salinity was steady. According to analysis of influence degree, the influence degree of DIN was the highest and it was high in order of Chl-a, COD, Water-temperature, DO, DIP and Salinity. The influence degree classified by DSW effluent position was predicted that suiface outflow was lower than bottom outflow. Ad When DSW discharge increased 10 times, the influence degree increased about $5{\sim}14$ times.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.4
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• pp.128-134
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• 2022

Nakdong river estuary is being operated with the goal of expanding the period of seawater inflow from this year to 2022 every month and creating a brackish water area within 15 km of the upstream of the river bank. In this study, the deep learning algorithm Long Short-Term Memory (LSTM) was applied to predict the salinity of the Nakdong Bridge (about 5 km upstream of the river bank) for the purpose of rapid decision making for the target brackish water zone and prevention of salt water damage. Input data were constructed to reflect the temporal and spatial characteristics of the Nakdong River estuary, such as the amount of discharge from Changnyeong and Hamanbo, and an optimal model was constructed in consideration of the hydraulic characteristics of the Nakdong River Estuary by changing the degree according to the sequence length. For prediction accuracy, statistical analysis was performed using the coefficient of determination (R-squred) and RMSE (root mean square error). When the sequence length was 12, the R-squred 0.997 and RMSE 0.122 were the highest, and the prior prediction time showed a high degree of R-squred 0.93 or more until the 12-hour interval.

• Journal of Korea Water Resources Association
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• v.47 no.3
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• pp.297-306
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• 2014

In this study, EFDC (Environmental Fluid Dynamics Code) model was used to simulate the salinity distribution for sea water inflow and rainfall runoff. The flowrate was given to the boundary conditions, which can be calculated by areal-specific flowrate method from the measured flowrate of the representative outfall. The boundary condition of the water elevation can be obtained from the hourly tidal elevation. The flowrate from the outfall can be calculated using the condition of the 245 mm raifall. The simulation results showed that at Sites 1~2 and the Mangsan island (Site 4) the salinity becomes 0 ppt after the rainfall. However, the salinity is 30 ppt when there is no rainfall. Time series of the salinity changes were compared with the measured data from January 1 to December 31, 2010 at the four sites (Site 2~5) of Yongwon channel. Lower salinities are shown at the inner sites of Yongwon channel (Site 1~4) and the sites of Songjeong river (Site 7~8). The intensive investigation near the Mangsan island showed that the changes of salinity were 21.9~28.8 ppt after the rainfall of 17 mm and those of the salinity were 2.33~8.05 ppt after the cumulative rainfall of 160.5 mm. This means that the sea water circulation is blocked in Yongwon channel, and the salinity becomes lower rapidly after the heavy rain.

• Journal of the korean society of oceanography
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• v.33 no.1-2
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• pp.1-7
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• 1998

In the western Weddell Sea, winter mixed layer is characterized by near-freezing temperature and higher salinity due to brine injection through sea-ice formation. This layer becomes Winter Water being capped by warmer and less saline Antarctic Surface Water during the sea-ice melt-ing season. In this study, Winter Water was preliminarily identified by the oxygen isotopic com-positions. The ${\delta}^{18}$O values of Winter Water show the progressively increasing trend from south to north in the study area. It presumably reflects the enhanced mixing with Antarctic Surface Water due to the extent of influence by low S'"0 value of sea-ice/glacier meltwater. Correlations between salinity and 6'"0 values of seawater can be used to more generally characterize Winter Water with a view to identification. However, the prediction on the degree of mixing from these relationships needs more detailed isotope data, although this study allows the oxygen isotopic composition of seawater as a tracer to identify the water mass.