Comparison of performance of statistical models in forecasting monthly streamflow of Kizil River,Chi

• 作 者: Shalamu ABUDU Chun-liang CUI J
• 机 构: Xinjiang Water Resources Research Institute,Urumqi 830049,P.R.China;Civil Engineering Department,New
• 出 处: 水科学与水工程
• 发表日期: 2010年3期
• 关键词: time series model Jordan-Elman artificial neural networks model monthly streamfl

This paper presents the application of autoregressive integrated moving average(ARIMA),seasonal ARIMA(SARIMA),and Jordan-Elman artificial neural networks(ANN)models in forecasting the monthly streamflow of the Kizil River in Xinjiang,China.Two different types of monthly streamflow data(original and deseasonalized data)were used to develop time series and Jordan-Elman ANN models using previous flow conditions as predictors.The one-month-ahead forecasting performances of all models for the testing period(1998-2005)were compared using the average monthly flow data from the Kalabeili gaging station on the Kizil River.The Jordan-Elman ANN models,using previous flow conditions as inputs,resulted in no significant improvement over time series models in one-month-ahead forecasting.The results suggest that the simple time series models(ARIMA and SARIMA)can be used in one-month-ahead streamflow forecasting at the study site with a simple and explicit model structure and a model performance similar to the Jordan-Elman ANN models.