A reliable streamflow forecasting is essential for flood disaster prevention, reservoir operation, water supply and water resources management. This study proposes a hybrid model for river stage forecasting and investigates its accuracy. The proposed model is the wavelet packet-based artificial neural network(WPANN). Wavelet packet transform(WPT) module in WPANN model is employed to decompose an input time series into approximation and detail components. The decomposed time series are then used as inputs of artificial neural network(ANN) module in WPANN model. Based on model performance indexes, WPANN models are found to produce better efficiency than ANN model. WPANN-sym10 model yields the best performance among all other models. It is found that WPT improves the accuracy of ANN model. The results obtained from this study indicate that the conjunction of WPT and ANN can improve the efficiency of ANN model and can be a potential tool for forecasting river stage more accurately.

Abstract
 1. 서 론
 2. 재료 및 방법
  2.1. 분석자료
  2.2. 분석방법
 3. 결과 및 고찰
  3.1. 분석
  3.2. 모델성능평가
 4. 결 론
 REFERENCE

• 서영민(경북대학교 건설방재공학부 건설환경공학전공) | Youngmin Seo (Department of Constructional Environmental Engineering, Kyungpook National University) Corresponding author