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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
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This study applied the Neural Network and Fuzzy theory to show water-purity control andpreventive measure in water quality forecasting of the future river. This study picked out NAJUand HAMPYUNG as the subject of investigation and used monthly the water quality and theoutflow data of KWANGJU2, NAJU, YOUNGSANNPO and HAMPYUNG from 1995 to 1999to forecast BOD, COD, T-N, T-P water density. The datum from 1995 to 1999 are used for studyand that of 2000 are used for verification. To develop model of water quality forecasting, firstly,this research formed Neural Network model and divided Neural Network model into two case- the case of considering lag and not considering. And this study selected optimal NeuralNetwork model through changing the number of hidden layer based on input layer(n) from nto 3n. Through forecasting result, the case without considering lag showed more precisesimulated result. Accordingly, this study intended to compare, analyse that Fuzzy model usingthe method without considering lag with Neural Network model. As a result, this study found that the model without considering lag in Neural Network Network shows the most excellent outcome. Thus this study examined a forecasting accuracy, analyzed result and verified propriety through appling the method of water quality forecasting using Neural Network and Fuzzy Algorithms to the actual case.
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