• 한국농공학회논문집
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• 제63권6호
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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.

• 스마트미디어저널
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• 제11권2호
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• pp.31-38
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• 2022

하천의 염분 변화를 신속히 예측하는 것은 염분 침투로 인한 농업, 생태계의 피해를 예측하고 재해 방지 대책을 수립하기 위해서 중요한 작업이다. 머신러닝 기법은 물리 기반 수리 모델에 비해 계산량이 훨씬 적기 때문에, 비교적 짧은 시간에 염분농도를 예측 가능하여 물리 기반 수리 모델의 보완 기법으로 연구되고 있다. 해외에서는 머신러닝 기법 기반 염분 예측 연구들이 활발히 연구되고 있으나, 대한민국의 공공데이터에 머신러닝 기법을 적용한 연구는 충분치 않다. 낙동강 하구의 환경 정보에 관한 공공데이터와 함께, 본 연구는 여러 종류의 머신러닝 기법의 염분농도에 대한 예측 성능을 측정하였다. 실험 결과에서, 결정 트리 기반의 LightGBM 알고리즘은 평균 RMSE 0.37의 예측 정확도와 타 알고리즘 대비 2-20배 빠른 학습 속도를 보여주었다. 따라서 국내 하천의 염분농도 예측에도 머신러닝 기법을 적용할 수 있다고 판단된다.

• 한국지구과학회지
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• 제45권4호
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• pp.327-337
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• 2024

The ocean is linked to long-term climate variability, but there are very few methods to assess the short-term performance of forecast models. This study analyzes the short-term prediction performance regarding ocean temperature and salinity of the Global Seasonal prediction system version 5 (GloSea5). GloSea5 is a historical climate re-creation (2001-2010) performed on the 1st, 9th, 17th, and 25th of each month. It comprises three ensembles. High-resolution hindcasts from the three ensembles were compared with the Array for Real-Time Geostrophic Oceanography (ARGO) float data for the period 2001-2010. The horizontal position was preprocessed to match the ARGO float data and the vertical layer to the GloSea5 data. The root mean square error (RMSE), Brier Score (BS), and Brier Skill Score (BSS) were calculated for short-term forecast periods with a lead-time of 10 days. The results show that sea surface temperature (SST) has a large RMSE in the western boundary current region in Pacific and Atlantic Oceans and Antarctic Circumpolar Current region, and sea surface salinity (SSS) has significant errors in the tropics with high precipitation, with both variables having the largest errors in the Atlantic. SST and SSS had larger errors during the fall for the NINO3.4 region and during the summer for the East Sea. Computing the BS and BSS for ocean temperature and salinity in the NINO3.4 region revealed that forecast skill decreases with increasing lead-time for SST, but not for SSS. The preprocessing of GloSea5 forecasts to match the ARGO float data applied in this study, and the evaluation methods for forecast models using the BS and BSS, could be applied to evaluate other forecast models and/or variables.

• Journal of Plant Biology
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• 제33권2호
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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.

• 한국수자원학회논문집
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• 제54권spc1호
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• pp.1023-1035
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• 2021

본 연구는 머신러닝 기반의 분석으로 해수담수화(Desalination) 시설의 전력비 예측모델의 가능성을 검토하였다. 해수담수화 주요 공정인 역삼투(Seawater Reverse Osmosis) 시설의 전력비 예측 모델을 개발하고, 전력비 산정에 영향을 미치는 인자를 분석하였으며, 해수 수질 중에서 선정된 수온 및 염분도 측정자료를 활용하여 검토하였다. 국립해양조사원(Korea Hydrographic and Oceanographic Agency, KHOA)의 2003년부터 2014년까지의 자료를 이용하였으며, 모형의 구조는 시행오차법(Trial & Error)으로 하이퍼파라미터를 최적화하여 머신러닝 기반의 예측 모델을 구축하고, 장래 해수 수질을 예측하였다. 해수 수온은 기존 패턴과 유사할 것으로 예측되었고, 염분도는 과거 측정자료 범위 이내로 최대값이 점차 감소되는 경향을 보여 해수담수화의 전력비가 약 0.80% 감소하는 것으로 검토되었다. 본 연구는 머신러닝 기반의 예측 모델을 구축하여 장래 수질 변화 예측하였으며, 해수 수질 변동의 영향 및 대안을 제시했다는데 의의가 있다.

• 한국농공학회논문집
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• 제51권2호
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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.

• 한국수산과학회지
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• 제46권6호
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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.

• 한국해양공학회지
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• 제22권2호
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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.

• 한국해안·해양공학회논문집
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• 제34권4호
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• pp.128-134
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• 2022

낙동강 하굿둑은 올해 2022년 해수 유입기간을 매월 대조기마다로 확대, 하굿둑 상류 15 km 이내로 기수역 조성을 목표로 운영되고 있다. 목표 기수역 조성구간 및 염수피해 방지를 위한 신속한 의사결정을 위해 본 연구에서는 딥러닝 알고리즘 Long Short-Term Memory(LSTM)을 적용하여 낙동대교(하굿둑 상류 약 5 km)지점의 염분 예측을 수행하였다. 창녕·함안보 방류량 등 낙동강 하구역의 시·공간적 특성을 반영하기 위한 입력데이터를 구축하였으며, Sequence length에 따른 정도 변화를 통해 낙동강 하구역의 수리학적 특성을 고려한 최적모델을 구축하였다. 예측 정확도는 결정계수(R-squred)와 RMSE(root mean square error) 이용하여 통계분석을 실시하였으며. Sequence length가 12일 때 R-squred 0.997, RMSE 0.122로 가장 정도가 높았으며, 선행 예측시간은 12시간 간격까지 R -squred 0.93 이상으로 높은 정도를 보였다.

• 한국수자원학회논문집
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• 제47권3호
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• pp.297-306
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• 2014

본 연구에서는 해수유입과 강우유출에 따른 용원수로 내의 염분도 분포를 모의하기 위해 EFDC (Environmental Fluid Dynamics Code) 모형을 이용하였다. 유량경계조건은 대표 방류구에서 유출되는 양을 모니터링하여 면적비 유량법으로 산정하였으며, 수위경계조건으로는 시간별 조위 값을 입력하였다. 강우량에 따른 염분도 모의 결과는 일 강우량 245 mm의 유출조건을 반영하였으며, 그 결과 Site 1~2 지점과 망산도 부근 방류구가 위치한 곳에서는 염분도가 0 ppt에 가까운 수치가 나타났으며, 반면 비강우시에는 30 ppt가 넘는 것으로 나타났다. 용원수로 내측지점(Site 2~5)에서의 2010년 1월 1일~12월 31일까지의 염분도 시계열 변화 모의결과와 월별 실측값을 비교하여 나타내었다. 용원수로의 지점별 염분도를 분석한 결과, 내측지점(Site 1~4)과 송정천지점(Site 7~8)에서 염분도가 낮게 나타났다. 이러한 결과를 바탕으로 망산도 부근 염분도를 집중적으로 조사한 결과, 1차 조사결과 누적강우량은 17 mm로 염분도 농도는 21.9~28.8 ppt로 측정되었으며, 2차 조사결과 누적강우 량은 160.5 mm로 염분도 농도는 2.33~8.05 ppt로 나타났다. 결과적으로 용원수로에서는 해수의 순환이 원활하게 이루어지지 않으므로, 이로 인하여 염분도의 차이가 크게 나타났으며 특히 강우시에는 염분도가 급격히 낮아지는 것으로 나타났다.