• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.2217-2221
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• 2008

정도 높은 유량자료는 수자원 분야의 연구와 실무에 있어 대단히 중요하다. 이런 유량자료를 연속적으로 측정하는 것은 현실적으로 어려운 문제이므로 하천에서 직접 측정된 수위-유량자료를 통해 수위-유량관계곡선을 작성하고, 이를 이용하여 연속적으로 관측된 수위에 대한 유량을 산출한다. 산정된 유량자료의 신뢰성을 평가하기 위해서 유출평가의 과정을 거치게 되며, 이를 위해 유출률 검토, 상 하류 유량검토, 누가유출량 평가, 첨두홍수량 및 저 평수기 동시유량 검토 등의 다양한 평가를 하게 된다. 정확한 유출평가를 위해서는 대상유역의 수계망도 및 배수계통도를 조사해야 하며, 하천 취수량, 댐 및 하수 방류량 등의 자료를 수집하여 물수지 분석을 실시해야 한다. 하지만 농업 지역의 경우 농업용수 공급을 위해 관개수로가 많이 설치되어 있어 배수계통이 매우 복잡하고, 관개수로를 통해 공급되는 정확한 용수량을 파악하는데 한계가 있어 정확한 유출평가가 어렵다. 국내 유역 중 농업 지역으로 복잡한 배수계통를 가지는 대표적인 유역은 동진강 유역이다. 동진강 유역은 섬진강 유역에 위치한 섬진강 댐에서 발전 및 농업용수 공급을 위해 유역변경식으로 동진강 유역으로 용수가 공급되고 있으며, 방류량은 동진강 본류 및 동진강 도수로, 김제간선, 정읍간선, 기타 간선 공급되는 복잡한 배수계통을 가지고 있다. 그리고 방류량 및 각 간선으로 공급되는 용수량은 인위적인 수문조작에 의해 운영되고 있어 유량자료의 평가를 위한 유출검토가 매우 어렵다. 본 연구에서는 복잡한 배수계통을 갖는 동진강 유역에서 2007년 유량측정을 통해 개발된 수위-유량관계 곡선 및 유량자료의 평가를 위해 유출평가를 실시하였다. 대상지점은 동진강 본류의 옹동과 태인 지점이며, 정확한 유출평가를 위해 수계망도 및 배수계통도를 조사하였고, 댐 방류량 및 각 간선으로 공급되는 용수량을 파악하여 물수지 분석을 하였다. 그 결과 2007년 전 기간 유출률은 옹동 53.4%, 태인 47.2%로 분석되었고, $6{\sim}$9월 주요 홍수기의 유출률이 옹동 60.1%, 태인 64.8%로 두 지점의 유출률 차가 심하지 않았고, 자연하천의 일반적인 유출률과 비슷한 결과를 보였다. 상 하류 유량검토를 통해서는 하류의 태인 지점이 상류의 옹동 지점보다 큰 정상적인 상 하류 관계를 나타내었다. 이러한 결과로서 동진강 유역의 옹동, 태인 지점의 유량자료는 적절하며, 측정된 유량자료를 토대로 개발된 수위-유량관계곡선식도 신뢰성이 높다는 것을 확인할 수 있었다.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.158-168
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• 2019

Korea Hydrographic and Oceanographic Agency(KHOA) carried out a research project named 'Marine Fisheries Infrastructure Construction and Technology Training for the Philippines' as part of the 1st Official Development Assistance(ODA) from 2015 to 2018. It is preparing for the 2nd ODA project which will begin in 2020. Besides, recently, the Philippines is paying attention to marine territory management and response capability due to problems such as the sea-level rise and coastal erosion caused by climate change. Therefore, before 2nd ODA to the Philippines, this study analyzed the vertical ocean model on the vertical datum in Korea and suggests the direction of development of the vertical ocean modeling system for the vertical datum in the Philippines using the observed data from the permanent tide station which was built by the Philippines ODA research project over the last four years. Moreover, as a pilot study, the Sulu Sea in the Philippines was selected and analyzed by harmonic analysis method. At each tide station, constants for correction had been computed. And the grid-based tidal model was constructed based on this study. As a result of the study, similar tidal characteristic were observed when the prediction and the measured tide were compared by applying the constants for correction between two station in the sea area with similar tidal level. These results could be used as basic data for the 2nd ODA to the Philippines, and contributed to construct and maintain a close cooperation and friendship between Korea and the Philippines.

• Journal of the Korean earth science society
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• v.28 no.2
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• pp.214-226
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• 2007

Because precipitation is influenced by various atmospheric variables, it is highly nonlinear. Although precipitation predicted by a dynamic model can be corrected by using a nonlinear Artificial Neural Network, this approach has limits such as choices of the initial weight, local minima and the number of neurons, etc. In the present paper, we correct simulated precipitation by using a multiple linear regression (MLR) method, which is simple and widely used. First of all, Ensemble hindcast is conducted by the PNU/CME Coupled General Circulation Model (CGCM) (Park and Ahn, 2004) for the period from April to August in 1979-2005. MLR is applied to precipitation simulated by PNU/CME CGCM for the months of June (lead 2), July (lead 3), August (lead 4) and seasonal mean JJA (from June to August) of the Northeast Asian region including the Korean Peninsula $(110^{\circ}-145^{\circ}E,\;25-55^{\circ}N)$. We build the MLR model using a linear relationship between observed precipitation and the hindcasted results from the PNU/CME CGCM. The predictor variables selected from CGCM are precipitation, 500 hPa vertical velocity, 200 hPa divergence, surface air temperature and others. After performing a leave-oneout cross validation, the results are compared with the PNU/CME CGCM's. The results including Heidke skill scores demonstrate that the MLR corrected results have better forecasts than the direct CGCM result for rainfall.

• Journal of Korea Water Resources Association
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• v.51 no.6
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• pp.503-514
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• 2018

In recent, the hydrological regime of the Mekong river is changing drastically due to climate change and haphazard watershed development including dam construction. Information of hydrologic feature like streamflow of the Mekong river are required for water disaster prevention and sustainable water resources development in the river sharing countries. In this study, runoff simulations at the Kratie station of the lower Mekong river are performed using SWAT (Soil and Water Assessment Tool), a physics-based hydrologic model, and LSTM (Long Short-Term Memory), a data-driven deep learning algorithm. The SWAT model was set up based on globally-available database (topography: HydroSHED, landuse: GLCF-MODIS, soil: FAO-Soil map, rainfall: APHRODITE, etc) and then simulated daily discharge from 2003 to 2007. The LSTM was built using deep learning open-source library TensorFlow and the deep-layer neural networks of the LSTM were trained based merely on daily water level data of 10 upper stations of the Kratie during two periods: 2000~2002 and 2008~2014. Then, LSTM simulated daily discharge for 2003~2007 as in SWAT model. The simulation results show that Nash-Sutcliffe Efficiency (NSE) of each model were calculated at 0.9(SWAT) and 0.99(LSTM), respectively. In order to simply simulate hydrological time series of ungauged large watersheds, data-driven model like the LSTM method is more applicable than the physics-based hydrological model having complexity due to various database pressure because it is able to memorize the preceding time series sequences and reflect them to prediction.

• Journal of Wetlands Research
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• v.7 no.4
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• pp.97-107
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• 2005

As the distributed model is developed and widely used, the accuracy of a rainfall measurement and more dense rainfall observation network are required for the reflection of various spatial properties. However, in reality, it is not easy to get the accurate data from dense network. Generally, we could not have the proper rainfall gages in space and even we have proper network for rainfall gages it is not easy to reflect the variations of rainfall in space and time. Often, we do also have missing rainfall data at the rainfall gage stations due to various reasons. We estimate the distribution of mean areal rainfall data from the point rainfalls. So, in the aspect of continuous rainfall property in time, we should fill the missing rainfall data then we can represent the spatial distribution of rainfall data. This study uses the Fuzzy-Genetic algorithm as a interpolation method for filling the missing rainfall data. We compare the Fuzzy-Genetic algorithm with arithmetic average method, inverse distance method, normal ratio method, and ratio of distance and elevation method which are widely used previously. As the results, the previous methods showed the accuracy of 70 to 80 % but the Fuzzy-Genetic algorithm showed that of 90 %. Especially, from the sensitivity analysis, we suggest the values of power in the equation for filling the missing data according to the distance and elevation.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.195-201
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• 2020

COVID-19 is hitting the world. In order to bring about new ways of innovation in the space sector, we need to analyze changes in the space sector and design new challenge strategies. COVID-19 exposes inherent vulnerabilities in the space sector. In particular, COVID-19 is causing supply chain shocks in the space industry, resulting in delays in the supply of systems, subsystems and parts due to a complete or partial interruption of a manufacturing unit. As the overall impact of New Normal on the industry is overall, we continue to look at it in the space sector. COVID is causing supply chain shock in the space industry. It causes a delay in the supply of systems, subsystems and parts due to a complete or partial interruption of a manufacturing unit. In the supply of launch services, the launch schedule is being delayed, but the main launch is still taking place. Demand for major applications such as environmental monitoring is soaring in the earth observation utilization sector. Analyzing the impact on manufacturing, the vendor-based contraction is bringing delays in the supply of systems, subsystems and components, and launch service providers are trying to minimize delays in the launch schedule.

• Journal of Korea Water Resources Association
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• v.40 no.1 s.174
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• pp.73-88
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• 2007

The goal of this research is to develop and apply the generalized regression neural networks model(GRNNM) embedding genetic algorithm(GA) for the estimation and calculation of the pan evaporation(PE), which is missed or ungaged and of the alfalfa reference evapotranspiration ($ET_r$), which is not measured in South Korea. Since the observed data of the alfalfa 37. using Iysimeter have not been measured for a long time in South Korea, the Penman-Monteith(PM) method is used to estimate the observed alfalfa $ET_r$. In this research, we develop the COMBINE-GRNNM-GA(Type-1) model for the calculation of the optimal PE and the alfalfa $ET_r$. The suggested COMBINE-GRNNM-GA(Type-1) model is evaluated through training, testing, and reproduction processes. The COMBINE-GRNNM-GA(Type-1) model can evaluate the suggested climatic variables and also construct the reliable data for the PE and the alfalfa $ET_r$. We think that the constructive data could be used as the reference data for irrigation and drainage networks system in South Korea.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.63-70
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• 2021

Generating a super-resolution meteological data by using a high-resolution deep neural network can provide precise research and useful real-life services. We propose a new technique of generating improved training data for super-resolution deep neural networks. To generate high-resolution meteorological data with domain specific knowledge, Lambert conformal conic projection and objective analysis were applied based on observation data and ERA5 reanalysis field data of specialized institutions. As a result, temperature and humidity analysis data based on domain specific knowledge showed improved RMSE by up to 42% and 46%, respectively. Next, a super-resolution generative adversarial network (SRGAN) which is one of the aritifial intelligence techniques was used to automate the manual data generation technique using damain specific techniques as described above. Experiments were conducted to generate high-resolution data with 1 km resolution from global model data with 10 km resolution. Finally, the results generated with SRGAN have a higher resoltuion than the global model input data, and showed a similar analysis pattern to the manually generated high-resolution analysis data, but also showed a smooth boundary.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.903-911
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• 2022

Groundwater, one of the resources for supplying water, fluctuates in water level due to various natural factors. Recently, research has been conducted to predict fluctuations in groundwater levels using Artificial Neural Network (ANN). Previously, among operators in ANN, Gradient Descent (GD)-based Optimizers were used as Optimizer that affect learning. GD-based Optimizers have disadvantages of initial correlation dependence and absence of solution comparison and storage structure. This study developed Gradient Descent combined with Harmony Search (GDHS), a new Optimizer that combined GD and Harmony Search (HS) to improve the shortcomings of GD-based Optimizers. To evaluate the performance of GDHS, groundwater level at Icheon Yullhyeon observation station were learned and predicted using Multi Layer Perceptron (MLP). Mean Squared Error (MSE) and Mean Absolute Error (MAE) were used to compare the performance of MLP using GD and GDHS. Comparing the learning results, GDHS had lower maximum, minimum, average and Standard Deviation (SD) of MSE than GD. Comparing the prediction results, GDHS was evaluated to have a lower error in all of the evaluation index than GD.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.407-418
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• 2023

The Multi-Object Goal Visual Navigation(MultiOn) is a visual navigation task in which an agent must visit to multiple object goals in an unknown indoor environment in a given order. Existing models for the MultiOn task suffer from the limitation that they cannot utilize an integrated view of multimodal context because use only a unimodal context map. To overcome this limitation, in this paper, we propose a novel deep neural network-based agent model for MultiOn task. The proposed model, MCFMO, uses a multimodal context map, containing visual appearance features, semantic features of environmental objects, and goal object features. Moreover, the proposed model effectively fuses these three heterogeneous features into a global multimodal context map by using a point-wise convolutional neural network module. Lastly, the proposed model adopts an auxiliary task learning module to predict the observation status, goal direction and the goal distance, which can guide to learn the navigational policy efficiently. Conducting various quantitative and qualitative experiments using the Habitat-Matterport3D simulation environment and scene dataset, we demonstrate the superiority of the proposed model.