유량 보간 신경망 모형의 개발 및 낙동강 유역에 적용
Development of Flow Interpolation Model Using Neural Network and its Application in Nakdong River Basin
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Abstract
The objective of this study is to develop a reliable flow forecasting model based on neural network algorithm in order to provide flow rate at stream sections without flow measurement in Nakdong river. Stream flow rate measured at 8-days interval by Nakdong river environment research center, daily upper dam discharge and precipitation data connecting upstream stage gauge were used in this development. Back propagation neural network and multi-layer with hidden layer that exists between input and output layer are used in model learning and constructing, respectively. Model calibration and verification is conducted based on observed data from 3 station in Nakdong river.
참고문헌 (15)
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