• Journal of Korean Society on Water Environment
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• v.24 no.1
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• pp.91-98
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• 2008

This work was performed to develop a model possible to predict the influent flow and influent components, which are one of main disturbances causing process problems at the operation of municipal wastewater treatment plant. In this study, artificial neural network (ANN) was used in order to develop a model that was able to predict the influent flow, $COD_{Mn}$, SS, TN 1 day-ahead, 2day-ahead and 3 day ahead. Multi-layer feed-forward back-propagation network was chosen as neural network type, and tanh-sigmoid function was used as activation function to transport signal at the neural network. And Levenberg-Marquart (LM) algorithm was used as learning algorithm to train neural network. Among 420 data sets except missing data, which were collected between 2005 and 2006 at field plant, 210 data sets were used for training, and other 210 data sets were used for validation. As result of it, ANN model for predicting the influent flow and components 1-3day ahead could be developed successfully. It is expected that this developed model can be practically used as follows: Detecting the fault related to effluent concentration that can be happened in the future by combining with other models to predict process performance in advance, and minimization of the process fault through the establishment of various control strategies based on the detection result.

• Journal of Korea Water Resources Association
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• v.41 no.11
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• pp.1123-1133
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• 2008

It needs to conceptualize watershed with triangular or rectangular elements and to analyze the changes in hydrological components of each element for distributed modeling of rainfall-runoff process. This study is the network analysis of watershed grid for flow routing occurred in each element when analyzing rainfall-runoff process by one-dimensional kinematic wave equation. Single flow direction from D8-method(deterministic eight-neighbors method) is used, and the information of flow direction and flow accumulation are used to determine the computation order of each element. The application theory of finite volume method is suggested for each flow direction pattern between elements, and it is applied it to calculate the flow of each grid. Network analysis method from this study is applied to GRM(Grid based Rainfall-runoff Model) which is physically based distributed rainfall-runoff model, and the results from simplified hypothetical watersheds are compared with $Vflo^{TM}$ to examine the reasonability of the method. It is applied to Jungrangcheon watershed in Han river for verification, and examination of the applicability to real site. The results from Jungrangcheon watershed show good agreement with measured hydrographs, and the application of the network analysis method to real site is proper.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.6
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• pp.43-54
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• 2018

The accurate estimation of reference crop evapotranspiration ($ET_o$) is essential in irrigation water management to assess the time-dependent status of crop water use and irrigation scheduling. The importance of $ET_o$ has resulted in many direct and indirect methods to approximate its value and include pan evaporation, meteorological-based estimations, lysimetry, soil moisture depletion, and soil water balance equations. Artificial neural networks (ANNs) have been intensively implemented for process-based hydrologic modeling due to their superior performance using nonlinear modeling, pattern recognition, and classification. This study adapted two well-known ANN algorithms, Backpropagation neural network (BPNN) and Generalized regression neural network (GRNN), to evaluate their capability to accurately predict $ET_o$ using daily meteorological data. All data were obtained from two automated weather stations (Chupungryeong and Jangsu) located in the Yeongdong-gun (2002-2017) and Jangsu-gun (1988-2017), respectively. Daily $ET_o$ was calculated using the Penman-Monteith equation as the benchmark method. These calculated values of $ET_o$ and corresponding meteorological data were separated into training, validation and test datasets. The performance of each ANN algorithm was evaluated against $ET_o$ calculated from the benchmark method and multiple linear regression (MLR) model. The overall results showed that the BPNN algorithm performed best followed by the MLR and GRNN in a statistical sense and this could contribute to provide valuable information to farmers, water managers and policy makers for effective agricultural water governance.

• Journal of Korea Water Resources Association
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• v.53 no.11
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• pp.961-971
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• 2020

The Intergovernmental Panel on Climate Change (IPCC) reported that the amount of precipitation in South Korea would increase regardless of the reduction of Greenhouse Gas (GHG) emissions. Moreover, the temporal and spatial rainfall variation would also increase in the future. Due to the geographic allocation of Korea, more than 80% of the annual precipitation occurs in the wet season from early July to late September. It is expected that the average precipitation in this period will increase from the Representative Concentration Pathways (RCP) scenario projections. These predictions imply an increased variability of available water resources. Rainwater harvesting system is widely used as an alternative water resources today. This study introduces a RBSN (rain barrel sharing network) as an efficient way to utilize alternative water resources under the RCP scenarios. The concept of RBSN combines individual rainwater harvesting system to a sharing network, which make the whole system more reliable. This study evaluated a RBSN in South Korea composed of four users based on a storage-reliability-yield (SRY) relationship. The study area comprises all 17 provincal areas in South Korea. The result showed a huge benefit from a RBSN in Korea under the historical rainfall condition. Even in the climate change condition, the results showed that a RBSN is still beneficial but the changes in reliability are different depending on provinces in Korea. The results of this study shows that a RBSN is a very effective and alternative measure that can deal with the impacts of climate change in the near future.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1999.10c
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• pp.99-104
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• 1999

In 2000 FFIA , FIA, RDC are united into new corporation. This corporation will manage rural water with TM/TC(Tele-Monitoring/Tele-Control) system. Most systems which were adopted in TM/TC system were Closed Control System which use exclusive network and protocol . Closed Control System can not support new corporation's requirement in water management system. Therefore, new corporation should adopt Open Control System as standard rural water management system. Open Control System support Fieldbus technology, TCP/IP various protocols, programming model, OPC which is essential to the water management program, and so on.

• Journal of Internet of Things and Convergence
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• v.2 no.3
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• pp.31-35
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• 2016

IoT technologies have been rapidly developed in recent years, and applied to many industries. In the field of fisheries, the water quality management system have been developed, helping in improving productivity and working environment. In this paper, we have designed the smart feeding system, interoperable with the water quality system, using LPWA network. LPWA network is an IoT network, which is appropriate to fish farms because of its wide area coverages and low power consumption. We expect this work to contribute to developing the aquaculture technology through the big data analysis with the accumulated data.

• Water for future
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• v.27 no.4
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• pp.59-67
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• 1994

The objective of this study is to develop a method which can design an optimal pipe network system using nonlinear programming(NLP) technique. The method finds the minimum-cost pipe network while satisfying all the design constraints including hydraulic constraints. The method developed in this study was applied to the Goyang distribution area in Goyang, Kyoungi-do. It has been found in the application and the comparison between the original design and the optimal design of this study that the optimal design method developed in this study does not require the trial-and-error procedure while satisfying the discharge and pressure requirements at the demanding nodes. Therefore, the optimal design method using NLP could be effectively utilized in the practical design considering economic aspect of the pipe network system at the same time.

• Korean Journal of Agricultural Science
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• v.46 no.4
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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.

• Journal of Korea Water Resources Association
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• v.36 no.2
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• pp.143-152
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• 2003

This paper suggests the improvement of the Sumjinkang for the estimation of areal averages of heavy rainfall events based on the optimal network and three existing networks. The problem consists of minimizing an objective function which includes both the accuracy of the areal mean estimation as expressed by the Kriging variance and the economic cost of the data collection. The wellknown geostatistical variance-reduction method is used in combination with SATS which is an algorithm of minimization. At the first stage, two kinds of optimal solutions are obtained by two trade-off coefficients. One of them is a optimal solution, the other is an alternative. At the second stage, a quasi optimal network and a quasi alternative are suggested so that the existing raingages near to the selected optimal raingages are included in the two solutions instead of gages of new gages.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.397-397
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• 2015

The frequency of urban floods is recently increased as a consequence of climate change and haphazard development in urban area. To mitigate and prevent the flood damage, we generally utilized a numerical model to investigate the causes and risk of urban flood. Contrary to general flood inundation model simulating only the surface flow, the model needs to consider flow of the sewer network system like SWMM and ILLUDAS. However, this kind of model can not consider the interaction between the surface flow and drainage network. Therefore, we tried to evaluate the impact of bidirectional interaction between sewer and surface flow in urban flooding analysis based on simulations using the quasi-interacted model and the interacted model. As a general quasi-interacted model, SWMM5 and FLUMEN are utilized to analyze the flow of drainage network and simulate the inundation area, respectively. Then, FLO-2D is introduced to consider the interaction between the surface flow and sewer system. The two method applied to the biggest flood event occurred in July 2011 in Sadang area, South Korea. Based on the comparison with observation data, we confirmed that the model considering the interaction the sewer network and surface flow, showed a good agreement than the quasi-interacted model.