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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.
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 comparing using the average monthly flow data from the Kalabeili gaging station on the Kizil River. Jordan-Elman ANN models, using previous flow conditions as inputs, resulting 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 s imilar to the Jordan-Elman ANN models.