• Korean Journal of Hydrosciences
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• v.5
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• pp.1-16
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• 1994

In this paper distributed models for simulating spatially and temporally varied moving storm in a watershed were developed. The complete simulation in a watershed is achieved through two sequential flow simulations which are overland flow simulation and channel network flow simulation. Two dimensional continuity equation and momentum equation of kinematic approximation were used in the overland flow simulation. On the other hand, in the channel network simulation two types of governing equations which are one dimensional continuity and momentum equations between two adjacent sections in a channel, and continuity and energy equations at a channel junction were applied. The finite difference formulations were used in the channel network model. Macks Creek Experimental Watershed in Idaho, USA was selected as a target watershed and the moving storm on August 23, 1965, which continued from 3:30 P.M. to 5:30 P.M., was utilized. The rainfall intensity fo the moving storm in the watershed was temporally varied and the storm was continuously moved from one place to the other place in a watershed. Furthermore, runoff parameters, which are soil types, vegetation coverages, overland plane slopes, channel bed slopes and so on, are spatially varied. The good agreement between the hydrograph simulated using distributed models and the hydrograph observed by ARS are Shown. Also, the conservations of mass between upstreams and downstreams at channel junctions are well indicated and the wpatial and temporal vaiability in a watershed is well simulated using suggested distributed models.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.6
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• pp.195-200
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• 2009

In this paper, we verified the reliability through local examination by introducing a wired network that is operated at the local waterworks to wireless LAN based Wi-Fi network. The adopted wireless network compared to the existing wired network through saving cost and reducing breakdown points have been proven to be effective qualitatively and quantitatively. This study proved that wireless networks could be introduced for the advancement of operations management of existing metropolitan water supply and the local waterworks that are operating currently.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.156-156
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• 2019

In this study, a gated recurrent unit (GRU) network is constructed based on a deep neural network (DNN) with the aim of restoring the missing daily flow data in river basins. Lai Chau hydrological station is located upstream of the Da river basin (Vietnam) is selected as the target station for this study. Input data of the model are data on observed daily flow for 24 years from 1961 to 1984 (before Hoa Binh dam was built) at 5 hydrological stations, in which 4 gauge stations in the basin downstream and restoring - target station (Lai Chau). The total available data is divided into sections for different purposes. The data set of 23 years (1961-1983) was employed for training and validation purposes, with corresponding rates of 80% for training and 20% for validation respectively. Another data set of one year (1984) was used for the testing purpose to objectively verify the performance and accuracy of the model. Though only a modest amount of input data is required and furthermore the Lai Chau hydrological station is located upstream of the Da River, the calculated results based on the suggested model are in satisfactory agreement with observed data, the Nash - Sutcliffe efficiency (NSE) is higher than 95%. The finding of this study illustrated the outstanding performance of the GRU network model in recovering the missing flow data at Lai Chau station. As a result, DNN models, as well as GRU network models, have great potential for application within the field of hydrology and hydraulics.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.392-395
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• 2017

본 연구에서는 일 증발접시 증발량 산정을 위한 딥러닝 (deep learning) 모형의 적용성을 평가하였다. 본 연구에서 적용된 딥러닝 모형은 deep belief network (DBN) 기반 deep neural network (DNN) (DBN-DNN) 모형이다. 모형 적용성 평가를 위하여 부산 관측소에서 측정된 기상자료를 활용하였으며, 증발량과의 상관성이 높은 기상변수들 (일사량, 일조시간, 평균지상온도, 최대기온)의 조합을 고려하여 입력변수집합 (Set 1, Set 2, Set 3)별 모형을 구축하였다. DBN-DNN 모형의 성능은 통계학적 모형성능 평가지표 (coefficient of efficiency, CE; coefficient of determination, $r^2$; root mean square error, RMSE; mean absolute error, MAE)를 이용하여 평가되었으며, 기존의 두가지 형태의 ANN (artificial neural network), 즉 모형학습 시 SGD (stochastic gradient descent) 및 GD (gradient descent)를 각각 적용한 ANN-SGD 및 ANN-GD 모형과 비교하였다. 효과적인 모형학습을 위하여 각 모형의 초매개변수들은 GA (genetic algorithm)를 이용하여 최적화하였다. 그 결과, Set 1에 대하여 ANN-GD1 모형, Set 2에 대하여 DBN-DNN2 모형, Set 3에 대하여 DBN-DNN3 모형이 가장 우수한 모형 성능을 나타내는 것으로 분석되었다. 비록 비교 모형들 사이의 모형성능이 큰 차이를 보이지는 않았으나, 모든 입력집합에 대하여 DBN-DNN3, DBN-DNN2, ANN-SGD3 순으로 모형 효율성이 우수한 것으로 나타났다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1989.10a
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• pp.51-51
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• 1989

• Journal of Korean Society of Water and Wastewater
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• v.26 no.2
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• pp.249-256
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• 2012

The paper describes a design methodology that can select a proper reliability factor and apply the selected reliability factor into the real water distribution system. Reliability factors which are used for the assesment of water supply networks, can be categorized by a connectivity, a reachability, an expected shortage and an availability. Among these factors, an expected shortage is the most proper reliability factor in the aspect of economic evaluation. Therefore, the expected shortage is applied to draw a water supply reliability into Changwon water supply systems. And the economic pipe diameter can be determined as 600mm for a connection pipe in the pipe network from the estimation of the expected shortage. Also, a quantitative effect of the connection pipe can be expressed in terms of the reduction, which is estimated by the expected shortage of 30,269$m^{3}$ from 68,705$m^{3}$ at initial condition to 38,436$m^{3}$ under the connected condition with the diameter 600mm pipe.

• Water Engineering Research
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• v.5 no.3
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• pp.147-156
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• 2004

Recently, the population growth, industrial and agricultural development are rapidly undergoing in the Lower Rio Grande Valley (LRGV) in Texas. The Lower Rio Grande Valley (LRGV) composed of the 4 counties and three of them are interesting for Non-point and point source pollutant modeling: Starr, Cameron, and Hidalgo. Especially, the LRGV is an intensively irrigation region, and Texas A&M University Agriculture Program and the New Mexico State University College of Agriculture applied irrigation district program (Guy Fipps and Craig Pope, 1998), projects in GIS and Hydrology based agricultural water management systems and assessment of prioritized protecting stream network, water quality and rehabilitation based on water saving potential in Rio Grande River. In the LRGV region, where point and non-point sources of pollution may be a big concern, because increasing fertilizers and pesticides use and population cause. This project objective seeks to determine the accumulation of non-point and point source and discuss the main impacts of agriculture and environmental concern with water quality related to pesticides, fertilizer, and nutrients within LRGV region. The GIS technique is widely used and developed for the assessment of non-point source pollution in LRGV region. This project shows the losses in kg/$km^2$/year of BOD (Biological Oxygen Demand), TN (total Nitrogen) and TP (total phosphorus) in the runoff from the surface of LRGV.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.282-285
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• 1996

In the water purification plant, the raw water is promptly purified by injecting chemicals. The amount of chemicals is directly related to water quality such as turbidity, temperature, pH and alkalinity. At present, however, the process of chemical reaction to the turbidity has not been clarified as yet. Since the process of coagulant dosage has no feedback signal, the amount of chemical can not be calculated from water quality data which were sensed from the plant. Accordingly, it has to be judged and determined by Jar-Test data which were made by skilled operators. In this paper, it is concerned to model and control the coagulant dosing process using jar-test results in order to predict optimum dosage of coagulant, PAC(Polymerized Aluminium Chloride). The considering relations to the reaction of coagulation and flocculation, the five independent variables(turbidity, temperature, pH, Alkalinity of the raw water, PAC feed rate) are selected out and they are put into calculation to develope a neural network model and a fuzzy model for coagulant dosing process in water purification system. These model are utilized to predict optimum coagulant dosage which can minimize the water turbidity in flocculator. The efficacy of the proposed control schemes was examined by the field test.

• Membrane and Water Treatment
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• v.13 no.5
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• pp.219-225
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• 2022

5th Assessment Report of the Intergovernmental Panel on Climate Change Weather (AR5) predicts that recent severe hydrological events will affect the quality of water and increase water pollution. To analyze changes in water quality due to future climate change, input data (precipitation, average temperature, relative humidity, average wind speed, and solar radiation) were compiled into a representative concentration curve (RC), defined using 8.5. AR5 and future use are calculated based on land use. Semi-distributed emission model Calculate emissions for each target period. Meteorological factors affecting water quality (precipitation, temperature, and flow) were input into a multiple linear regression (MLR) model and an artificial neural network (ANN) to analyze the data. Extensive experimental studies of flow properties have been carried out. In addition, an Acoustic Doppler Velocity (ADV) device was used to monitor the flow of a large open channel connection in a wastewater treatment plant in Ho Chi Minh City. Observations were made along different streams at different locations and at different depths. Analysis of measurement data shows average speed profile, aspect ratio, vertical position Measure, and ratio the vertical to bottom distance for maximum speed and water depth. This result indicates that the transport effect of the compound was considered when preparing the hazard analysis.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.1348-1351
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• 2006

Recently human beings face serious water crisis, namely water management at the critical moment because of rapid increasing in population, subordinate part of a national budget, water pollution by domestic use and industrial use or waste, agricultural water use by intensive market farming, excessive development and extraction of water sources, and etc. We become to recognize that water and sanitation is the one of most important part which is threatening us with a drain on the water resources and death around the world and then are making opportunities of discussion for water policies and solutions of water problems through international network such as Global Water Partnership(GWP), World Water council(WWC). World Water Forum(WWF), as one of main water-related activities, aims at sharing knowledge and experience among various stakeholders for accomplishing a holistic water resources management by making common thoughts regarding the necessity of integration among different organizations related with water management, problems and issues. Also the unity of each local area for the holistic approach focuses on showing present local actions and actively supporting them, and suggesting new ideas. With Korea's participation in the 4th WWF this year, Korean government should have consistency in its establishment and implementation of sustainable water resources policies as a more active and future-oriented member of international water network. Finally it will be necessary that we will set up an organization and system which does publicity activities about results of various local actions of Korea around the world through next WWF in the future and contribute to solving global water problems.