International conference on innovation advances and implementation of flood forecasting technology IMPROVING DAILY STREAM FLOW FORECASTS BY COMBINING ARMA AND ANN MODELS

Combined forecasting has attracted lots of attention in the hydrological community recently. In this study, an autoregressive and moving average model, a periodic AR model, a normal multi-layer perceptron artificial neural network model and a periodic artificial neural network model are fitted to a univariate daily stream flow process for the upper Yellow River in China to forecast stream flows one to ten days in advance. Comparing the performance of these models, we find that while no model outperforms the others throughout all seasons over the year, each model shows its strength in some specific season(s). Four combination techniques, (i.e., simple average method (SAM), rollinglyupdated weighted average method, semi-fixed weighted average method, and modular semi-fixed weighted average), are applied to combine the daily stream flow forecasts. The results show that SAM can improve the accuracy of forecasts with a four to five day lead time, and it generally performs best among four competitive combination methods. Owing to its simplicity and robustness, SAM is recommended for improving stream flow forecast accuracy when no individual models to be combined performs consistently more accurate or more poorer than the others.