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수자원의 이용계획을 위한 장기유출모형의 개발에 관한 연구
조현경(Hyeon-Kyeong Cho) 한국산업융합학회 2013 한국산업융합학회 논문집 Vol.16 No.3
Long-term runoff model can be used to establish the effective plan of water reources allocation and the determination of the storage capacity of reservoir. So this study aims at the development of monthly runoff model using artificial neural network technique. For this, it was selected multi-layer neural network(MLN) and radial basis function neural network(RFN) model. In this study, it was applied model to analysis monthly runoff process at the Wi stream basin in Nakdong river which is representative experimental river basin of IHP. For this, multi-layer neural network model tried to construct input 3, hidden 7, and output 1 for each number of layer. As the result of analysis of monthly runoff process using models connected with artificial neural network technique, it showed that these models were effective in the simulation of monthly runoff.
낙동강유역에서 신경망 모델을 이용한 강우예측에 관한 연구 - 다변량 모델과의 비교-
조현경(Hyeon-Kyeong Cho),이증석(Jeung-Seok Lee) 한국산업융합학회 1999 한국산업융합학회 논문집 Vol.2 No.2
This study aims at the development of the techniques for the rainfall forecasting in river basins by applying neural network theory and compared with results of Multivariate Model (MVM). This study forecasts rainfall and compares with a observed values in the San Chung gauging stations of Nakdong river basin for the rainfall forecasting of river basin by proposed Neural Network Model(NNM). For it, a multi-layer Neural Network is constructed to forecast rainfall. The neural network learns continuous-valued input and output data. The result of rainfall forecasting by the Neural Network Model is superior to the results of Multivariate Model for rainfall forecasting in the river basin. So I think that the Neural Network Model is able to be much more reliable in the rainfall forecasting.<br/>
FLDWAV 모형을 이용한 하천합류부에서의 수리학적 특성 연구
조현경(Hyeon-Kyeong Cho) 한국산업융합학회 2007 한국산업융합학회 논문집 Vol.10 No.4
This study aims at the calculation of a variation of flow characteristics of main channel for tributary inflow in river junction. So this study was analyzed the variation of flow depth and velocity in main channel for a change of inflow degree. For this purpose, FLDWAV model are carried out for variations of 30°, 60° and 90° tributary inflow at junction. Results show that velocity ratio(V1/V3) increases and flow depth ratio(H1/H3) decreases for discharge ratio(Q1/Q3) of upstream and downstream when degree increases in junction. And FLDWAV model was applied at a real river junctions. Selected area is a junction of Gumho river and Sin stream. Results show that pattern is similar to a virtual channel.
조현경 ( Hyeon Kyeong Cho ) 한국환경과학회 2011 한국환경과학회지 Vol.20 No.3
This study aims at the examination of the relative characteristics of discharge and water quality in river basins using statistical methods. For it, water quality and discharge data was collected in observed stations of Nakdong river and carried out correlation analysis, regression analysis, factor analysis and cluster analysis. And it was investigated the applicability of water quality prediction using Nearest-neighbor method. As a result, it grasped a trenditional characteristics and mutual relations between discharge an water quality data. Therefore, this results were suggested the comprehensive data and methods for a management of water quality, effective operation and policy development in Nakdong river basin.
Muskingum 홍수 추적방법의 매개변수 최적화에 관한 연구
조현경(Hyeon-Kyeong Cho) 한국산업융합학회 2008 한국산업융합학회 논문집 Vol.11 No.1
This study presents techniques for the estimation of parameters in flood routing method of natural channel.. The Muskingum routing method is the most widely used method of hydrologic stream channel routing. In this paper, Genetic Algorithm and Fletcher-Powell method is applied to determine parameters(K and x) of the Muskingum routing method. The results of the approach shows that Genetic Algorithm method can be one of methods to determine parameters of the Muskingum routing method. Based on the analysis for estimated parameters and the comparison with the results from observed data, the applicability of Genetic Algorithm is verified.